The authors would like to acknowledge the support of the John Robinson &#38; Churchill family foundations.

The following study and protocols were reviewed and approved by the Institutional Review Board of Emory University.
1. L3 CT Segmentation
<ol>
	<li>Obtain the axial CT Digital Imaging and Communications in Medicine (DICOM) image.
	<ol>
		<li>In the image viewer, identify the L3 vertebra.
		<ol>
			<li>If possible, select two horizontal window views, and select coronal or sagittal view on the left for reference, and axial view on the right.</li>
			<li>Click on Cross Link to link the left and right windows.</li>
			<li>Scroll down the images from cranial to caudal direction. Identify L1 vertebra, which is the first vertebra without a rib attachment.</li>
			<li>Count from L1 to L3 and use the coronal or sagittal view to identify the slice of the middle of L3. This is identified as the point at which both transverse processes are able to be maximally and equally visualized.</li>
			<li>Select the L3 slice. From the Exam tab, select Send Exam and save the image as a DICOM file. 
			NOTE: Step 1 is a pre-processing step and is listed here to demonstrate how to obtain an L3 image. If the researcher already has an L3 image, they can go to step 2. If the image viewer does not enable cross-referencing, the researcher can skip 1.1.1&#160;to 1.1.2. If the imaging does not include thoracic region, identify L5, which is anterior to the sacrum, and count from L5 to L3, keeping in mind that the presence of a sixth lumbar vertebra is a normal variant.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Open the DICOM image with Slice-O-Matic Software.</li>
	<li>Drag the DICOM file to anywhere on the Slice-O-Matic window.</li>
	<li>Select Modes | Region Growing&#160;to begin segmentation.
	<ol>
		<li>If the version of Slice-O-Matic has Alberta Protocol options at the top of the Modes&#160;list of options, then one can also select Step 3: Segmentation to begin segmentation. If using Step 3: Segmentation, complete step 5, and then proceed to step 11.</li>
	</ol>
	</li>
	<li>Select Tools | Tag Lock. This will enable the user to &#34;lock&#34; tagged colors to ensure they are not accidently colored over or erased later on.</li>
	<li>Skeletal Muscle Identification: Click on 1 (Red) under the Region Growing area on the left side of the screen.
	<ol>
		<li>Click on the Off button by Lower Limit to turn it to On. Click on the arrows by Mouse Wheel to set Disabled to Lower Limit. Drag the slider on Lower Limit to set Hounsfield Unit (HU) threshold as close to -29 as possible, then use the mouse wheel to set HU threshold exactly to -2913.</li>
		<li>Click on the Off button by Upper Limit to turn it to On. Click on the arrows by Mouse Wheel to set Lower Limit to Upper Limit. Drag the slider on Upper Limit to set HU threshold as close to 150 as possible, then use the mouse wheel to set HU threshold exactly to 15013.</li>
	</ol>
	</li>
	<li>Intramuscular Adipose Tissue (IMAT) Identification: Click on 2 (Green) under the Region Growing area on the left side of the screen.
	<ol>
		<li>Click on the Off button by Lower Limit to turn it to On. Click on the arrows by Mouse Wheel to set Disabled to Lower Limit. Drag the slider on Lower Limit to set HU threshold as close to -190 as possible, then use the mouse wheel to set HU threshold exactly to -19013.</li>
		<li>Click on the Off button by Upper Limit to turn it to On. Click on the arrows by Mouse Wheel to set Lower Limit to Upper Limit. Drag the slider on Upper Limit to set HU threshold as close to -30 as possible, then use the mouse wheel to set HU threshold exactly to -3013.</li>
	</ol>
	</li>
	<li>Visceral Adipose Tissue (VAT) Identification: Click on 5 (Yellow) under the Region Growing area on the left side of the screen.
	<ol>
		<li>Click on the Off button by Lower Limit to turn it to On. Click on the arrows by Mouse Wheel to set Disabled to Lower Limit. Drag the slider on Lower Limit to set HU threshold as close to -150 as possible, then use the mouse wheel to set HU threshold exactly to -15013.</li>
		<li>Click on the Off button by Upper Limit to turn it to On. Click on the arrows by Mouse Wheel to set Lower Limit to Upper Limit. Drag the slider on Upper Limit to set HU threshold as close to -50 as possible, then use the mouse wheel to set HU threshold exactly to -5013.</li>
	</ol>
	</li>
	<li>Subcutaneous Adipose Tissue (SAT) Identification: Click on 7 (Cyan) under the Region Growing area on the left side of the screen.
	<ol>
		<li>Click on the Off button by Lower Limit to turn it to On. Click on the arrows by Mouse Wheel to set Disabled to Lower Limit. Drag the slider on Lower Limit to set HU threshold as close to -190 as possible, then use the mouse wheel to set HU threshold exactly to -19013.</li>
		<li>Click on the Off button by Upper Limit to turn it to On. Click on the arrows by Mouse Wheel to set Lower Limit to Upper Limit. Drag the slider on Upper Limit to set HU threshold as close to -30 as possible, then use the mouse wheel to set HU threshold exactly to -3013.</li>
	</ol>
	</li>
	<li>Use the + and - keys on the keyboard to zoom in and out of the CT image. Adjust the zoom as necessary throughout segmentation to clearly and accurately tag tissues.</li>
	<li>Begin segmenting by selecting 1 for Skeletal Muscle tissue (SM).
	<ol>
		<li>Set the brush option to Paint.</li>
		<li>Use the brush tools found directly under Region Growing to adjust to the desired size of the brush and begin painting over the Psoas, Paraspinal Muscle groups, oblique, and rectus muscle groups. 
		NOTE: If fluids or organs outside the muscle fascia is tagged in red as muscle, be sure to clear the tagging using the None color selection.</li>
	</ol>
	</li>
	<li>Once all the muscles are tagged, select 1 in the TAG Lock menu at the bottom left of the screen. This will ensure no muscle is accidently re-tagged or erased as segmentation proceeds.</li>
	<li>Select 2 under Region Growing and paint over all fat tissues (IMAT) within the muscle fascia. Be sure to use the None color selection if any fat or structures outside the muscle fascia are mistakenly tagged as IMAT. 
	NOTE: The edges of the muscle fascia usually appear lighter than the visceral or subcutaneous fat surrounding it. Be sure to tag all the fat within the lighter edges of the muscle fascia as IMAT and not VAT or SAT. If the linea alba is not tagged as muscle, the entirety of the linea alba should be analyzed as IMAT.</li>
	<li>Once all IMAT is tagged, select 2 from the TAG Lock menu at the bottom left of the screen.</li>
	<li>Select 5 from the Region Growing menu to tag the VAT tissue.
	<ol>
		<li>When tagging VAT, depending on the image, it may be easier to use Grow 2D instead of Paint.</li>
		<li>If using Grow 2D, use the smallest Paint Brush option. If using Grow 2D be sure to look back over all tagged VAT and make sure no intralumenal tissue inside intestines or organs is mistakenly tagged, since that fat is usually from either digesting food or other structures which are not VAT.</li>
		<li>If using Paint be sure to not paint inside the lumen of organs or the intestines.</li>
	</ol>
	</li>
	<li>Once all VAT is tagged, select 5 from the TAG Lock menu at the bottom left of the screen.</li>
	<li>Select 7 from the Region Growing menu to tag SAT tissue.
	<ol>
		<li>When tagging SAT, depending on the image, it is usually easier to use Grow 2D instead of Paint.</li>
		<li>If using Grow 2D, use the smallest Paint Brush option.</li>
		<li>If using Grow 2D be sure to go back over the edges of the image with the None tool selected to be sure no tissue within the muscle fascia is tagged as SAT and to be sure no skin is tagged as SAT. 
		&#8203;NOTE: Skin is usually lighter in appearance than SAT and is usually around 2-3 pixels thick, but be aware that skin&#39;s appearance and thickness may vary from image to image.</li>
		<li>If using Paint, be sure to take care around the edges, particularly around the skin to ensure no tissue is incorrectly tagged.</li>
	</ol>
	</li>
	<li>When finished tagging tissues, go to Tools | Tag Surface/Volume. This will display the Surface area and volume of each of the tissues tagged, typically the interest is in the surface area.
	<ol>
		<li>Click on Display in Window to fully open the Tag Surface/Volume window. This will also display HU values.</li>
		<li>Record the surface area and HU threshold values. 
		NOTE: If the Tag Surface/Volume window does not appear in the bottom left of the screen, it may be because there is not enough room to display it. In this case, be sure the Slice-O-Matic window is maximized and then select Tools | Tag Lock to remove the Tag Lock window. This should make enough space to display the Tag Surface/Volume window.</li>
	</ol>
	</li>
	<li>When complete, go to File | Save TAG Files. This will save a TAG file where the DICOM file is located.</li>
</ol>
2. L3 MRI Segmentation
<ol>
	<li>Obtain the axial MRI (T2-weighted sequences) DICOM image
	<ol>
		<li>In the image viewer, identify the L3 vertebra.
		<ol>
			<li>If possible, select two horizontal window views, and select coronal or sagittal view on the left for reference, and axial view on the right.</li>
			<li>Click on Cross Link to link the left and right windows.</li>
			<li>Scroll down the images from cranial to caudal direction. Identify L1 vertebra, which is the first vertebra without a rib attachment.</li>
			<li>Count from L1 to L3 and use the coronal or sagittal view to identify the slice of the middle of L3. This is identified as the point at which both transverse processes are able to be maximally and equally visualized.</li>
			<li>Select the L3 slice. From the Exam tab, select Send Exam and save the image as a DICOM file. 
			NOTE: Step 1 is a pre-processing step and is listed here to demonstrate how to obtain an L3 image. If the researcher already has an L3 image, they can go to step 2. If the image viewer does not enable cross-referencing, the researcher can skip 1.1.1 to 1.1.2. If the imaging does not include the thoracic region, identify L5, which is anterior to the sacrum, and count from L5 to L3, keeping in mind that the presence of a sixth lumbar vertebra is a normal variant.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Open the DICOM image with Slice-O-Matic software.</li>
	<li>Drag DICOM file to anywhere on the Slice-O-Matic window.</li>
	<li>Select Modes | Region Growing&#160;to begin segmentation. 
	NOTE: Due to poor differentiation of adipose tissues in MRI images, only SM is segmented.
	<ol>
		<li>Paraspinal muscles Segmentation: Click on 1 (Red) under the Region Growing area on the left side of the screen.
		<ol>
			<li>In the Preview Mode, histograms of the image would show multiple peaks, with the first peak representing air, and the subsequent second, third, and fourth peaks representing muscle, bone, and fat, respectively.</li>
			<li>Click on the Off button by Lower Limit to turn it to On.</li>
			<li>Click on the arrows by Mouse Wheel to set Disabled to Lower Limit.</li>
			<li>Drag the slider on Lower Limit to set Hounsfield Unit (HU) threshold to 0.</li>
			<li>Click on the Off button by Upper Limit to turn it to On.</li>
			<li>Click on the arrows by Mouse Wheel to set Lower Limit to Upper Limit.</li>
			<li>Drag the slider on Upper Limit to set HU to include the paraspinal muscle.</li>
			<li>Begin Segmenting Paraspinal Muscle by selecting 1 for Skeletal Muscle tissue (SM). Set the brush option to Paint. Use the brush tools found directly under Region Growing to adjust to the desired size of the brush and begin painting over the Paraspinal Muscle groups. 
			NOTE: If anything is tagged in red as muscle on fluids or organs outside the muscle fascia, be sure to clear the tagging using the None color selection.</li>
		</ol>
		</li>
		<li>Segmentation of remaining muscle groups: Move the mouse anteriorly to linea alba. In the preview mode, adjust the Upper Limit to include linea alba. This upper limit of the intensity is then adopted for all remaining muscle groups.
		<ol>
			<li>Begin segmenting by selecting 1 for Skeletal Muscle tissue (SM). Set the brush option to Paint. Use the brush tools found directly under Region Growing to adjust to the desired size of the brush and begin painting over the Paraspinal Muscle groups. 
			NOTE: If anything is tagged in red as muscle on fluids or organs outside the muscle fascia, be sure to clear the tagging using the None color selection.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>When finished tagging tissues, go to Tools | Tag Surface/Volume. This will display the surface area and volume of each of the tissues tagged, typically the interest is in the surface area.</li>
	<li>Click on Display in Window to fully open the Tag Surface/Volume window. This will also display HU values.</li>
	<li>Record the surface area and HU threshold values. 
	NOTE: If the Tag Surface/Volume window does not appear in the bottom left of the screen, it may be because there is not enough room to display it. In this case, be sure the Slice-O-Matic window is maximized and then select Tools | Tag Lock to remove the Tag Lock window. This should make enough space to display the Tag Surface/Volume window.</li>
	<li>When complete, go to File | Save TAG Files. This will save a TAG file where the DICOM file is located.</li>
</ol>
3. Linear Measurement for CT and MRI
<ol>
	<li>Obtain the axial CT or MRI DICOM image.
	<ol>
		<li>In the image viewer, identify the L3 vertebra.
		<ol>
			<li>If possible, select two horizontal window views, and select coronal or sagittal view on the left for reference, and axial view on the right.</li>
			<li>Click on Cross Link to link the left and right windows.</li>
			<li>Scroll down the images from cranial to caudal direction. Identify L1 vertebra, which is the first vertebra without a rib attachment.</li>
			<li>Count from L1 to L3 and use the coronal or sagittal view to identify the slice of the middle of L3, as identified by the point at which both transverse processes are equally identified. 
			&#8203;NOTE: Step 1 is a pre-processing step and is listed here to demonstrate how to obtain an L3 image. If the researcher already has an L3 image, they can go to step 2. If the image viewer does not enable cross-referencing, the researcher can skip 1.1.1 to 1.1.2. If the imaging does not include the thoracic region, identify L5, which is anterior to sacrum, and count from L5 to L3.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Import the image into a medical imaging viewer and open it.
	<ol>
		<li>For Horos: open the app and click on Import.</li>
		<li>Navigate to where the DICOM image is located, select it and click on Open. The file and image should appear under the Patient Name list.</li>
		<li>Double click on Patient Name, then double click on the image to begin linear segmentation.</li>
	</ol>
	</li>
	<li>Identify the psoas muscles and the paraspinal muscles.</li>
	<li>Select the ruler tool and measure the horizontal (180&#176;) and vertical (90&#176;) diameters of the four muscles mentioned above. 
	NOTE: The lines must be horizontal and vertical to the image, not diagonal. The horizontal and vertical lines drawn should create a rectangular box that encompasses the entirety of each muscle. Do not simply measure the longest distance of the muscle. If using an image viewer that allows for a box drawing tool, that tool can be used instead of the simple ruler tool. This is provided that the box drawing tool displays at least the height and length of the box.</li>
	<li>Record all eight measurements (Right Psoas Width, Right Psoas Length, Left Psoas Width, Left Psoas Length, Right Paraspinal Width, Right Paraspinal Length, Left Paraspinal Width, Left Paraspinal Length) for further analysis.
	<ol>
		<li>Calculate the individual muscle surface area by multiplying the horizontal and vertical value of that muscle.</li>
		<li>Obtain the total muscle surface area psoas muscles and paraspinal muscles by adding the left muscle to the right muscle, respectively.</li>
		<li>Calculate the linear muscle index by dividing the combined surface area (mm2) by patient height squared (m2).</li>
	</ol>
	</li>
</ol>

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A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Body-composition+assessment+via+air-displacement+plethysmography+in+adults+and+children:+a+review.">Body-composition assessment via air-displacement plethysmography in adults and children: a review.</a> The American Journal of Clinical Nutrition. 75, 453-467 (2002).</li><li>Zopfs, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-slice+CT+measurements+allow+for+accurate+assessment+of+sarcopenia+and+body+composition.">Single-slice CT measurements allow for accurate assessment of sarcopenia and body composition.</a> European Radiology. 30 (3), 1701-1708 (2020).</li><li>Schwenzer, N. F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+analysis+of+adipose+tissue+in+single+transverse+slices+for+estimation+of+volumes+of+relevant+fat+tissue+compartment.">Quantitative analysis of adipose tissue in single transverse slices for estimation of volumes of relevant fat tissue compartment.</a> Investigative Radiology. 45 (12), 788-794 (2010).</li><li>Baracos, V. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Psoas+as+a+sentinel+muscle+for+sarcopenia:+a+flawed+premise.">Psoas as a sentinel muscle for sarcopenia: a flawed premise.</a> Journal of Cachexia, Sarcopenia and Muscle. 8 (4), 527-528 (2017).</li><li>Potretzke, A. M., Schmitz, K. H., Jensen, M. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Preventing+overestimation+of+pixels+in+computed+tomography+assessment+of+visceral+fat.">Preventing overestimation of pixels in computed tomography assessment of visceral fat.</a> Obesity Research. 12 (10), 1698-1701 (2004).</li><li>Shen, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Total+body+skeletal+muscle+and+adipose+tissue+volumes:+estimation+from+a+single+abdominal+cross-sectional+image.">Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image.</a> Journal of Applied Physiology. 97 (6), 2333-2338 (2004).</li><li>Weston, A. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+abdominal+segmentation+of+CT+scans+for+body+composition+analysis+using+deep+learning.">Automated abdominal segmentation of CT scans for body composition analysis using deep learning.</a> Radiology. 290 (3), 669-679 (2019).</li></ol>

The authors have nothing to disclose.

The psoas muscle, paraspinal muscle groups, and oblique muscles closely correlate with the overall muscle mass5. In particular, the surface area within a CT or MRI cross section of these muscle groups at the midpoint of the third lumbar vertebra (L3) is highly correlated with overall muscle mass, making this image an ideal one for researchers or clinicians to use when assessing sarcopenia1,2,13. Segmentation and linear measurements have demonstrated great value in assessing body composition and identifying poor prognostic conditions such as sarcopenia and sarcopenic obesity in patients16,17. Research has shown that muscle mass measurements are associated with survival and risks of major complications following major surgeries or treatment plans such as chemotherapy and chemotherapeutic toxicity16,17,18. Therefore, we would posit it may be beneficial for clinicians to have body composition data before counseling patients regarding treatment options.
Currently, there are several methods of assessing body composition. Several methods, such as densitometry12 and air displacement plethysmography (ADP)19, utilize air weight and displacement, respectively to estimate percentage body fat and body density. While these methods can be useful, they are unable to determine adipose tissue distribution5,19. Other body composition analytic techniques, such as BIA, base their analysis upon the differing electric characteristics of fat mass and fat-free mass12. However, once again this technique fails to adequately assess fat distributions, and it also requires more information such as ethnicity, age, and sex for more accurate measurements19. Conversely, assessments such as DEXA have been shown to be useful in body composition assessment, but have a tendency to overestimate muscle mass with increasing adiposity12. Several protocols have also used the Region-of-Interest (ROI) method to obtain muscle mass and adipose tissue data within the DICOM-viewing software, which has been shown to have good correlation with BIA body composition analysis for sarcopenia assessment and nutritional assessment20,21.
The segmentation procedure developed by Mourtzakis et al. has an advantage over alternative body composition assessments since it can be done on most CT or MRI images and accurately determines adipose tissue distributions and muscle area13. Additionally, axial L3 segmentation has the advantage of accuracy regardless of patient obesity status13. Similar to the aforementioned alternatives, the linear measures technique developed by Avrutin et al.14 does not have the ability to assess fat distribution. Recently, researchers have demonstrated disparate in body segmentation, especially in methods measuring psoas muscles alone22. Psoas muscle mass alone is not highly representative of the lumbar muscle quantity or systematic muscle wasting, and may not be highly correlated with clinical outcomes22. This problem may be more concerning in linear measurement, as psoas muscle is the major muscle group in assessment. However, our outlined technique includes bilateral psoas and paraspinal muscle estimations to gauge a more accurate, while still rapid and convenient assessment of cross-sectional muscle mass. Future studies that validate the accordance between CT/MRI linear measurement and segmentation methods and their correlation to clinical outcomes are warranted.
Both the L3 segmentation and linear measurement procedures were initially designed to rapidly and accurately assess body-wide muscle content. By segmenting at the L3 vertebrae only, the protocol saves time while still providing the researchers or clinicians enough information to determine the patient&#39;s lean muscle mass and adiposity status. However, even though L3 segmentation takes far less time than full body segmentation, it can still be time-consuming and expensive to use the Slice-O-Matic software. Conversely, linear measurements have the potential to be as accurate as the L3 segmentation in assessing muscle status and sarcopenia in critically ill patients14,15. We have demonstrated such relationship in the T3 renal cell carcinoma cohort, where the skeletal muscle measured by linear measurements is closely correlated with the value measured by segmentation (Figure 6). Importantly, the method is extremely fast, and the imaging software is free. However, the most notable limitation to the linear measurement procedure is its lack of ability to assess adipose tissue content, which limits the clinicians to contexts where general assessment of muscle content is sufficient.
There are three critical steps in both segmentation and linear measurement procedures. First, clinicians and researchers should identify the middle of the L3 vertebrae to achieve consistency. The middle of the L3 vertebrae will be the slice where the marrow of the transverse processes is most prominent. The axial L3 vertebrae slice is more easily identified with the aid of a cross-linked sagittal or coronal view. Researchers or clinicians can first find L1 vertebrae or sacrum as the reference point, keeping in mind that the presence of six lumbar vertebrae instead of five is a normal variant. The next crucial step is identifying muscles. In linear measurements, the quadratus lumborum should not be included while taking the vertical and horizontal measurements. Third, researchers should also pay close attention when labeling VAT in the segmentation protocol, as the colon content may sometimes be tagged as visceral adipose tissue23. When such an error occurs, researchers should erase these areas before moving on to the next step.
A common issue in segmentation is poor CT or MRI image quality (see Representative Results for examples). In some cases, the poor quality does not render the image useless, but in other cases the image may need to be excluded from analysis. Another, possibly unavoidable, limitation of the segmentation of a single image includes the random variation of solid organ position from image to image.
Other common issues for both L3 segmentation analysis and linear measurement analysis are often related to inter and intra-rater variation. As would be the case with most protocols, a certain amount of variation between observers and between a single individual&#39;s separate trials can be expected. To account for and minimize inter-rater variation with multiple people performing analysis, the team of researchers or clinicians can test for any statistically significant variations in surface area measurements and average HU from the same image. Take special note of HU variation as this will indicate whether researchers or clinicians who have very similar surface areas for the same image are indeed tagging the tissues approximately the same. To test for significant intra-rater variation for an individual, researchers or clinicians may take a small subset of images and segment each image until all replicas for each image are within a narrow, statistically insignificant margin.
We acknowledge that both the protocols presented here have limitations in body composition analysis as only a single slice is used. As suggested by Shen et al., the 3D analysis may provide more accurate information for the abdominal visceral fat, and single-slice analysis for VAT is at different levels for men and women24. However, the protocols discussed here are still valuable as they provide quick assessments of muscle as well as adipose tissue, which can be used for sarcopenia screening in clinics.
Moreover, there have been many automated body composition analysis protocols using 3D machine learning algorithms, especially neural-net-based classification algorithms25. We acknowledge that these may be the potential future alternatives to traditional 2D segmentation. However, these methods require large datasets of CT and MRI images to be developed, tested, and implemented in clinical and research settings. Plus, these methods often require 2D segmentation analysis to establish a baseline reference against which to validate the machine learning algorithms against. The protocols demonstrated here can therefore be useful when large data sets or 3D images are not available, and these protocols can be applied to help develop and validate machine learning algorithms when they are applicable. Thus, we believe that clinicians and researchers can benefit from this training video and adopt these rapid and reliable methods as preliminary screening before automated analysis is available and in order to facilitate the implementation of this advanced technology.
The ability to rapidly analyze adipose tissue distribution and skeletal muscle mass has a wide breadth of clinical interests ranging from cancer treatment and research to cardiac disease5.Compared to other commonly used methods, the Mourtzakis et al. L3 segmentation procedure in Slice-O-Matic can accurately and rapidly assess adipose tissue distribution and determine sarcopenia status5,12,13,19.Additionally, in contexts where information on skeletal muscle mass is sufficient, L3 linear measurement procedure is a reliable and very fast tool to help predict success in cancer treatments such as surgery, radiotherapy, and chemotherapy1,2,4,6,7,8. The purpose of this training video and manuscript is to clearly delineate the protocol for segmentation and linear measurements for future use so that clinicians can more easily assess body composition in the clinic setting.

Assessment of sarcopenia and body composition is currently of great clinical interest. Though specific definitions of sarcopenia vary depending on the setting and context, all definitions include significant loss of skeletal muscle mass or muscle strength, which are closely correlated1,2,3. Body composition analysis incorporates measurements of skeletal muscle mass and adipose tissue distribution, providing more comprehensive information about the general fitness of patients1,3,4. Similarly, disproportionally distributed adipose tissue, especially visceral adipose tissue, has been found to be related to various diseases, including cardiac disease, type II diabetes, and cancer5.
Clinically, sarcopenia and its assessment by linear measurements have been repeatedly shown to be a strong prognostic factor for cancer-specific survival across malignancies and oncologic outcomes following surgery, radiotherapy, and chemotherapy1,2,4,6,7,8. In particular, previous research demonstrates that patients with sarcopenia have decreased cancer-specific survival and overall survival1,2,9,10. Therefore, accurate and rapid clinical assessment of sarcopenia progression is important in determining treatment election. Conventional whole-body composition profiling requires analysis at a three-dimensional (3D) level using imaging techniques, including Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Bone Densitometry (DEXA), and Bioelectrical Impedance Analysis (BIA), which are time-consuming, costly, and require extensive training5,11. Another drawback is a lack of information on adipose distribution, especially for the air displacement plethysmography (ADP) and DEXA12. Therefore, assessment and determination of sarcopenia and body composition with the use of conventional cross-sectional imaging modalities such as CT or MRI, which are used as part of standard-of-care clinical practice, has great clinical value5.
One commonly used segmentation software in the clinical research setting is the Slice-O-Matic program developed by TomoVision. Using the Mourtzakis et al.13 segmentation procedure, the program allows for researchers or clinicians to semi-automatically tag various tissue types such as skeletal muscle (SM), intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) using density-based thresholds, permitting measurement of the overall cross-sectional areas of each tissue. These measurements are then used to estimate total body skeletal muscle mass and adiposity, often after normalization by a patient&#39;s height squared, to identify sarcopenia and sarcopenic obesity by population-based thresholds.
A recent developed method by Avrutin et al.14 using linear measurements of skeletal muscle developed has shown the potential to be equally reliable in estimating total muscle mass using MRI and CT images of the L3 cross section14,15. The psoas and paraspinal muscle groups comprise much of the muscle surface area of the L3 region and have high functionality, suggesting they may be high-fidelity predictors of overall muscle strength, and thus the chief candidates of linear measurement14,15. To calculate the muscular surface area, horizontal and vertical measurements of the psoas and paraspinal muscle groups are obtained using a ruler tool to draw 90&#176; intersecting straight lines. The horizontal and vertical measurements of each muscle group are multiplied to estimate the surface area of each muscle group, which is then used to calculate a linear muscle index when divided by the patient&#39;s height. With minimal training, this entire process can take less than 1 min.
Given the potential implications of body composition measurements on patient care, there is an urgent need for creating accessible training materials. In this article, we provide a detailed description of two methods developed by Avrutin et al.14 and Mourtzakis et al.13 to quantify skeletal muscle mass and body composition, respectively, for providers and clinical researchers.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Centricity PACS Radiology RA 1000 Workstation</td>
			<td>GE&#160; Healthcare</td>
			<td></td>
			<td>Image viewer to obtain subject&#39;s MRI and CT images</td>
		</tr>
		<tr>
			<td>Slice-O-Matic 5.0</td>
			<td>TomoVision</td>
			<td></td>
			<td>Segmentation software used in this protocol. Other versions of this software may be used, but tools may be slightly different.</td>
		</tr>
		<tr>
			<td>Horos</td>
			<td>Nimble Co LLC d/b/a Purview</td>
			<td></td>
			<td>Linear segmentation software used in this protol, but researchers can use any image viewer with a ruler tool.</td>
		</tr>
	</tbody>
</table>

segmentation and linear measurement for body composition analysis using slice-o-matic and horos

Body composition is associated with risk of disease progression and treatment complications in a variety of conditions. Therefore, quantification of skeletal muscle mass and adipose tissues on Computed Tomography (CT) and/or Magnetic Resonance Imaging (MRI) may inform surgery risk evaluation and disease prognosis. This article describes two quantification methods originally described by Mourtzakis et al. and Avrutin et al.: tissue segmentation and linear measurement of skeletal muscle. Patients' cross-sectional image at the midpoint of the third lumbar vertebra was obtained for both measurements. For segmentation, the images were imported into Slice-O-Matic and colored for skeletal muscle, intramuscular adipose tissue, visceral adipose tissue, and subcutaneous adipose tissue. Then, surface areas of each tissue type were calculated using the tag surface area function. For linear measurements, the height and width of bilateral psoas and paraspinal muscles at the level of the third lumbar vertebra are measured and the calculation using these four values yield the estimated skeletal muscle mass. Segmentation analysis provides quantitative, comprehensive information about the patients' body composition, which can then be correlated with disease progression. However, the process is more time-consuming and requires specialized training. Linear measurements are an efficient and clinic-friendly tool for quick preoperative evaluation. However, linear measurements do not provide information on adipose tissue composition. Nonetheless, these methods have wide applications in a variety of diseases to predict surgical outcomes, risk of disease progression and inform treatment options for patients.

The L3 segmentation procedure results in a tagged CT or MRI image with skeletal muscle (SM) tissue tagged in red, IMAT in green, VAT in yellow, and SAT in cyan (Figure 1). The remaining untagged tissues will remain in their original white, grey, and back hues that correspond to each pixel&#39;s respective Hounsfield unit (HU) values. The majority of the untagged tissues that remain in white will be bone, the majority of tissues that remain in greys will be non-skeletal muscle, organ tissue, and adipose tissues within the lumens of intestines, and the majority of the image that remains in black will be air. A properly segmented image will have no red or green tagging outside the skeletal muscle fascia, and no yellow or cyan tagging within the skeletal muscle fascia. Additionally, yellow tagging should not invade lumens of intestines or organs such as the kidney or liver, and cyan tagging should not be present along the lighter outer edges that correspond to skin. Once image segmentation is completed, the surface areas and average tissue HU values should be recorded, alongside the patient&#39;s height (Table 1). From this data, one can calculate the skeletal muscle index and proceed with any other analysis relevant to the specific research or clinical questions. Note that for most MRI images, only skeletal muscle can be properly tagged and subsequently analyzed (Table 2). In linear measurements, an index is calculated by dividing the surface area over the square of the height (Table 3).
Common issues researchers may encounter during the segmentation procedure include images that have omission of key information. For example, images may have sizable portions cut or cropped off (Figure 2). Specifically, images that have SAT and/or skeletal muscle tissue cut out of frame will drastically lower the accuracy of surface area calculations of affected tissues. Whether this renders an image unsuitable for analysis will depend on the clinical or research context and must be decided by the research team on a case-by-case basis. Another pitfall is that researchers may inadvertently include spinal cord and bone marrow in skeletal muscle. To avoid this issue, researchers should be well-trained and remain cautious during segmentation. Other common artifacts in CT or MRI images include technical issues caused by patient placement or motion in the scanner, fat stranding and scar tissues around the skeletal muscle fascia, and other oddly shaped artifacts (Figure 3). Technical issues caused by patient motion or improper placement will usually appear lighter, with higher HU values than surrounding tissue. These kind of technical issues usually appear in SAT and can also lower the accuracy of surface area calculation. The clinical or research context will determine the level of tolerance for such issues. Fat stranding and scar tissue artifacts usually do not result in high amounts of error in tissue surface area calculations. However, they can lead to misidentification of the fascial line. Skeletal muscle and IMAT surface areas can be vastly inaccurate in cases where fat strands or scar tissue are mistaken as the muscle fascia line. Other small blemishes and artifacts in CT and MRI images usually do not affect overall image quality except in rare cases. Depending on the clinical or research context, these artifacts may need to be assessed by a radiology expert to verify image quality. The last common issue in CT and MRI images are deformities in the muscle fascia line (Figure 4). These breaks usually will not affect image quality, but images containing large breaks or other deformities in muscle fascia should be assessed by a radiologist to determine if the deformity origin will affect the clinical or research context&#39;s analysis.
The L3 linear measurement procedure developed by Avruvin et al. has fewer common errors than the L3 segmentation procedure14,15. The main issues encountered in linear measures revolve around identifying the muscle groups of interest, the two psoas and paraspinal muscle groups (Figure 5). In most cases the psoas edges will be distinct from nearby organs, but in the event that the edge is difficult to discern, changing the HU filters or brightness usually will solve the majority of the issues. Additionally, the edges of the paraspinal muscle groups will often be distinct from other nearby tissues, but one should note that if no clear muscle reaches the bottom-most fascia line, the line should not be included in determining the lower edge of the paraspinal muscle group. Finally, the quadratus lumborum should be excluded when determining the edge of the psoas or paraspinal muscle groups (Figure 5E).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61674/61674fig01.jpg" /> 
Figure 1: Proper L3 segmentation in Slice-O-Matic. (A) The unaltered axial CT image at L3 vertebrae. (B) The fully tagged axial CT with red corresponding to skeletal muscle (SM), green to Intramuscular Adipose Tissue (IMAT), yellow to Vesical Adipose Tissue (VAT), and cyan to Subcutaneous Adipose Tissue (SAT). <a href="https://www.jove.com/files/ftp_upload/61674/61674fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61674/61674fig02.jpg" /> 
Figure 2: Cut off L3 CT image. An untagged CT image in Slice-O-Matic with substantial amounts of SAT as well as significant amounts of skeletal muscle tissue cut off. <a href="https://www.jove.com/files/ftp_upload/61674/61674fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/61674/61674fig03.jpg" /> 
Figure 3: Common artifacts. (A) The untagged CT image has various artifacts highlighted in the red box, blue oval, and green box, respectively. The red box shows technical issues with a CT scan, potentially from malalignment or motion during the scan. The blue oval highlights a common artifact likely stemming from scar tissues. The green square highlights blemishes that may have multiple potential causes. (B) The tagged CT scan with appearances of the same respective artifacts highlighted in the red box, blue oval, and green box. <a href="https://www.jove.com/files/ftp_upload/61674/61674fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/61674/61674fig04.jpg" /> 
Figure 4: Large break in muscle fascia. (A) The untagged L3 CT image highlights a large break in the skeletal muscle fascia in the purple box. (B) The tagged L3 CT image highlights the tagged appearance of the large break in the skeletal muscle fascia in the purple box. <a href="https://www.jove.com/files/ftp_upload/61674/61674fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/61674/61674fig05.jpg" /> 
Figure 5: L3 linear measurements. (A) The original L3 CT image prior to analysis in the Horos image viewer. (B) The traditional linear measurement method includes one vertical line and one horizontal line drawn for each muscle. These lines are measured with a ruler tool and multiplied to find each muscle group&#39;s surface area. Note the traditional linear measures method should always have lines intersecting at 90&#730;. This image of the traditional linear measures method is visual demonstration only since it was created in Horos and is not guaranteed to have 90&#730; intersections. (C) (D) (E) The Box method for L3 linear measurements. (C) (D) The blue and purple box encompass the right and left psoas, respectively, and the yellow and green box encompass the right and left paraspinal muscle, respectively. (E) The light purple and orange boxes highlight the quadratus lumborum, which should not be considered when determining edges of the Psoas and paraspinal muscle groups. <a href="https://www.jove.com/files/ftp_upload/61674/61674fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/61674/61674fig06.jpg" /> 
Figure 6: Comparison of linear measures and L3 cross-sectional skeletal muscle area, n = 65. The combined psoas and paraspinal areas are in accordance with the total skeletal muscle in the L3 cross-section. <a href="https://www.jove.com/files/ftp_upload/61674/61674fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>CT SEGMENTATION</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>Muscle</td>
			<td>Intramuscular Adipose Tissue</td>
			<td>Visceral Adipose Tissue</td>
			<td>Subcutaneous Adipose Tissue</td>
		</tr>
		<tr>
			<td>Suface Area (cm2)</td>
			<td>134.4</td>
			<td>8.402</td>
			<td>72.43</td>
			<td>271</td>
		</tr>
		<tr>
			<td>Hounsfield Unit (mean)</td>
			<td>33.61</td>
			<td>2.1</td>
			<td>18.11</td>
			<td>67.76</td>
		</tr>
		<tr>
			<td>Patient Height Squared (m2)</td>
			<td>2.69</td>
			<td>Skeletal Muscle index (Muscle area/Height2,&#160; cm2/ m2)</td>
			<td></td>
			<td>49.97</td>
		</tr>
	</tbody>
</table>
Table 1: CT Segmentation
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>MRI SEGMENATION</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>Muscle</td>
		</tr>
		<tr>
			<td>Suface Area (cm2)</td>
			<td>241.8</td>
		</tr>
		<tr>
			<td>Hounsfield Unit (mean)</td>
			<td>35.85</td>
		</tr>
		<tr>
			<td>Patient Height (m2)</td>
			<td>3.39</td>
		</tr>
		<tr>
			<td>Skeletal Muscle index</td>
			<td rowspan="2">71.42</td>
		</tr>
		<tr>
			<td>(Muscle area/Height2, cm2/m2)</td>
		</tr>
	</tbody>
</table>
Table 2: MRI Segmentation
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="8">LINEAR MEASURES</td>
		</tr>
		<tr>
			<td>Right Psoas Height (cm)</td>
			<td>Right Psoas Width (cm)</td>
			<td>Left Psoas Height (cm)</td>
			<td>Left Psoas Width (cm)</td>
			<td>Right Paraspinal Height (cm)</td>
			<td>Right Paraspinal Width (cm)</td>
			<td>Left Paraspinal Height (cm)</td>
			<td>Left Paraspinal Width (cm)</td>
		</tr>
		<tr>
			<td>3.934</td>
			<td>2.927</td>
			<td>3.743</td>
			<td>2.788</td>
			<td>4.916</td>
			<td>6.264</td>
			<td>4.403</td>
			<td>7.045</td>
		</tr>
		<tr>
			<td colspan="2">Total Psoas Area (cm2)</td>
			<td colspan="2">21.950</td>
			<td colspan="2">Total Paraspinal Area (cm2)</td>
			<td colspan="2">61.813</td>
		</tr>
		<tr>
			<td colspan="4">Total Muscle Area (cm2)</td>
			<td colspan="4">83.76</td>
		</tr>
		<tr>
			<td colspan="2">Patient Height Squared (m2)</td>
			<td colspan="2">2.496</td>
			<td colspan="2">Linear Measure Index (cm2/m2)</td>
			<td colspan="2">33.55</td>
		</tr>
	</tbody>
</table>
Table 3: Linear Measures

Watch this Scientific Journal Video about Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos at JoVE.com

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Segmentation and linear measurements quantify skeletal muscle mass and adipose tissues using Computed Tomography and/or Magnetic Resonance Imaging images. Here, we outline the use of Slice-O-Matic software and Horos image viewer for rapid and accurate analysis of body composition. These methods can provide important information for prognosis and risk stratification.

Body composition is associated with risk of disease progression and treatment complications in a variety of conditions. Therefore, quantification of ...

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10.1K Views.  Emory University School of Medicine. Segmentation and linear measurements quantify skeletal muscle mass and adipose tissues using Computed Tomography and/or Magnetic Resonance Imaging images. Here, we outline the use of Slice-O-Matic software and Horos image viewer for rapid and accurate analysis of body composition. These methods can provide important information for prognosis and risk stratification.

Video: Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Dissection of Human Vitreous Body Elements for Proteomic Analysis

The vitreous is an optically clear, collagenous extracellular matrix that fills the inside of the eye and overlies the retina. 1,2 Abnormal interactions between vitreous substructures and the retina underlie several vitreoretinal diseases, including retinal tear and detachment, macular pucker, macular hole, age-related macular degeneration, vitreomacular traction, proliferative vitreoretinopathy, proliferative diabetic retinopathy, and inherited vitreoretinopathies. 1,2 The molecular composition of the vitreous substructures is not known. Since the vitreous body is transparent with limited surgical access, it has been difficult to study its substructures at the molecular level. We developed a method to separate and preserve these tissues for proteomic and biochemical analysis. The dissection technique in this experimental video shows how to isolate vitreous base, anterior hyaloid, vitreous core, and vitreous cortex from postmortem human eyes. One-dimensional SDS-PAGE analyses of each vitreous component showed that our dissection technique resulted in four unique protein profiles corresponding to each substructure of the human vitreous body. Identification of differentially compartmentalized proteins will reveal candidate molecules underlying various vitreoretinal diseases.

This video shows an effective technique for differentiating and dissecting the various semi-transparent structures of the human vitreous body in post mortem eyes.

The vitreous is an optically clear, collagenous extracellular matrix that fills the inside of the eye and overlies the retina. 1,2 Abnormal interactions ...

Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography

Obesity is associated with increased morbidity and mortality as well as reduced metrics in quality of life.1 Both environmental and genetic factors are associated with obesity, though the precise underlying mechanisms that contribute to the disease are currently being delineated.2,3 Several small animal models of obesity have been developed and are employed in a variety of studies.4 A critical component to these experiments involves the collection of regional and/or total animal fat content data under varied conditions.
Traditional experimental methods available for measuring fat content in small animal models of obesity include invasive (e.g. ex vivo measurement of fat deposits) and non-invasive (e.g. Dual Energy X-ray Absorptiometry (DEXA), or Magnetic Resonance (MR)) protocols, each of which presents relative trade-offs. Current invasive methods for measuring fat content may provide details for organ and region specific fat distribution, but sacrificing the subjects will preclude longitudinal assessments. Conversely, current non-invasive strategies provide limited details for organ and region specific fat distribution, but enable valuable longitudinal assessment. With the advent of dedicated small animal X-ray computed tomography (CT) systems and customized analytical procedures, both organ and region specific analysis of fat distribution and longitudinal profiling may be possible. Recent reports have validated the use of CT for in vivo longitudinal imaging of adiposity in living mice.5,6 Here we provide a modified method that allows for fat/total volume measurement, analysis and visualization utilizing the Carestream Molecular Imaging Albira CT system in conjunction with PMOD and Volview software packages.

Fat content analysis is routinely conducted in studies utilizing murine obesity models. Emerging methods in small animal CT imaging and analysis are providing for longitudinal detail rich fat content analysis. Here we detail step by step procedures for performing small animal CT imaging, analysis, and visualization.

Obesity is associated with increased morbidity and mortality as well as reduced metrics in quality of life.1 Both environmental and genetic factors are ...

Semi-automated Analysis of Mouse Skeletal Muscle Morphology and Fiber-type Composition

For years, distinctions between skeletal muscle fiber types were best visualized by myosin-ATPase staining. More recently, immunohistochemical staining of myosin heavy chain (MyHC) isoforms has emerged as a finer discriminator of fiber-type. Type I, type IIA, type IIX and type IIB fibers can now be identified with precision based on their MyHC profile; however, manual analysis of these data can be slow and down-right tedious. In this regard, rapid, accurate assessment of fiber-type composition and morphology is a very desirable tool. Here, we present a protocol for state-of-the-art immunohistochemical staining of MyHCs in frozen sections obtained from mouse hindlimb muscle in concert with a novel semi-automated algorithm that accelerates analysis of fiber-type and fiber morphology. As expected, the soleus muscle displayed staining for type I and type IIA fibers, but not for type IIX or type IIB fibers. On the other hand, the tibialis anterior muscle was composed predominantly of type IIX and type IIB fibers, a small fraction of type IIA fibers and little or no type I fibers. Several image transformations were used to generate probability maps for the purpose of measuring different aspects of fiber morphology (i.e., cross-sectional area (CSA), maximal and minimal Feret diameter). The values obtained for these parameters were then compared with manually-obtained values. No significant differences were observed between either mode of analysis with regards to CSA, maximal or minimal Feret diameter (all p &#62; 0.05), indicating the accuracy of our method. Thus, our immunostaining analysis protocol may be applied to the investigation of effects on muscle composition in many models of aging and myopathy.

Immunohistochemical staining of myosin heavy chain isoforms has emerged as the state-of-the-art discriminator of skeletal muscle fiber-type (i.e., type I, type IIA, type IIX, type IIB). Here, we present a staining protocol along with a novel semi-automated algorithm that facilitates rapid assessment of fiber-type and fiber morphology.

For years, distinctions between skeletal muscle fiber types were best visualized by myosin-ATPase staining. More recently, immunohistochemical staining of ...

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;Application in Premanifest Huntington's Disease

Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington&#39;s disease (HD), regional brain atrophy begins many years prior to the motor onset (during the &#34;premanifest&#34; period), but the spatiotemporal pattern of regional atrophy across the brain has not been fully characterized. Here we demonstrate an online cloud-computing platform, &#34;MRICloud&#34;, which provides atlas-based whole-brain segmentation of T1-weighted images at multiple granularity levels, and thereby, enables us to access the regional features of brain anatomy. We then describe a regression model that detects statistically significant inflection points, at which regional brain atrophy starts to be noticeable, i.e. the &#34;change-point&#34;, with respect to a disease progression index. We used the CAG-age product (CAP) score to index the disease progression in HD patients. Change-point analysis of the volumetric measurements from the segmentation pipeline, therefore, provides important information of the order and pattern of structural atrophy across the brain. The paper illustrates the use of these techniques on T1-weighted MRI data of premanifest HD subjects from a large multicenter PREDICT-HD study. This design potentially has wide applications in a range of neurodegenerative diseases to investigate the dynamic changes of brain anatomy.

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&amp;#8212;Application in Premanifest Huntington&#039;s Disease

This paper describes a statistical model for volumetric MRI data analysis, which identifies the &#34;change-point&#34; when brain atrophy begins in premanifest Huntington&#39;s disease. Whole-brain mapping of the change-points is achieved based on brain volumes obtained using an atlas-based segmentation pipeline of T1-weighted images.

Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington&#39;s disease (HD), regional brain atrophy ...

Body Composition and Metabolic Caging Analysis in High Fat Fed Mice

Alterations to body composition (fat or lean mass), metabolic parameters such as whole-body oxygen consumption, energy expenditure, and substrate utilization, and behaviors such as food intake and physical activity can provide important information regarding the underlying mechanisms of disease. Given the importance of body composition and metabolism to the development of obesity and its subsequent sequelae, it is necessary to make accurate measures of these parameters in the pre-clinical research setting. Advances in technology over the past few decades have made it possible to derive these measures in rodent models in a non-invasive and longitudinal fashion. Consequently, these metabolic measures have proven useful when assessing the response of genetic manipulations (for example knockout or transgenic mice, viral knock-down or overexpression of genes), experimental drug/compound screening and dietary, behavioral or physical activity interventions. Herein, we describe the protocols used to measure body composition and metabolic parameters using an animal monitoring system in chow-fed and high fat diet-fed mice.

This protocol describes the use of a body composition analyzer and metabolic animal monitoring system to characterize body composition and metabolic parameters in mice. An obesity model induced by high-fat feeding is used as an example for the application of these techniques.

Alterations to body composition (fat or lean mass), metabolic parameters such as whole-body oxygen consumption, energy expenditure, and substrate ...

Handheld Metal Detector Screening for Metallic Foreign Body Ingestion in Children

Coins are the most common ingested metallic foreign bodies among children. The goal of this protocol is to assess the accuracy and feasibility of using a handheld metal detector to detect ingested metallic foreign bodies in children. We propose that by introducing handheld metal detector screening early in the triage process of children with high suspicion of metallic foreign body ingestion, the number of radiographs being ordered to localize the metallic foreign body can be reduced in this radio-sensitive population. The study protocol requires the screening of the participants for history of foreign body ingestion and exclusion of patients with respiratory distress or metallic implants. The patient changes to hospital gown and items that could contain metal like eyeglasses, earrings, pendants, and ornaments are removed. The patient is positioned in the center of the room away from other metallic interferences. The working status of the handheld metal detector is first confirmed by eliciting a positive audio-visual signal. Then the screening is done in an erect position with head in extension to expose the neck, from the level of the chin to the level of the hip joint, to cover the anatomical areas from neck to pelvis in a zig-zag manner both anteriorly and posteriorly. A positive audio-visual signal is carefully noted during the scanning for the presence of metallic foreign body. Relevant radiographs are ordered as per the area detected on the metal detector screening. The handheld metal detector was able to precisely identify all the coins among the ingested metallic foreign bodies in our study. The handheld metal detector could not consistently detect non-coin metallic foreign bodies. This protocol demonstrates the accuracy of handheld metal detector in the identification and localization of coins and coin like metallic foreign bodies.

Here, we present a protocol to use the handheld metal detector, to screen for the presence of ingested metallic foreign bodies in children who present to the pediatric emergency medicine department with a history of foreign body ingestion.

Coins are the most common ingested metallic foreign bodies among children. The goal of this protocol is to assess the accuracy and feasibility of using a ...

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Literature databases (i.e., PubMed, Scopus, and Web of Science) differ in terms of their coverage, focus, and the tool they provide. PubMed focuses mainly on life sciences and biomedical disciplines, whereas Scopus and Web of Science are multidisciplinary. The protocol described in the current study was used to search for publications from Jordanian authors in the years 2013-2017. In this protocol, how to use each database to conduct this type of search is explained in detail. A Scopus search resulted in the highest number of documents (11,444 documents), followed by a Web of Science search (10,943 documents). PubMed resulted in a smaller number of documents due to its narrower scope and coverage (4,363 documents). The results also show a yearly trend in: (1) the number of publications, (2) the disciplines that have the most publications, (3) the countries of collaboration, and (4) the number of open access publications. In contrast, PubMed has a sophisticated keyword optimization service (i.e., Medical Subject Heading, or MeSH), while both Scopus and Web of Science provide search analysis tools that can produce representative figures. Finally, the features of each database are explained in detail and several indices that can be extracted using the search results are provided. This study provides a base for using literature databases for bibliometric analysis.

Literature databases are commonly used to assess publications in a certain subject, discipline, country, or region of the world, a practice known as bibliometric analysis. The current protocol details how to use PubMed, Scopus, and Web of Science databases to do bibliometric analysis.

Literature databases (i.e., PubMed, Scopus, and Web of Science) differ in terms of their coverage, focus, and the tool they provide. PubMed focuses mainly ...

Quantitative Analysis of Cellular Composition in Advanced Atherosclerotic Lesions of Smooth Muscle Cell Lineage-Tracing Mice

Atherosclerosis remains the leading cause of death worldwide and, despite countless preclinical studies describing promising therapeutic targets, novel interventions have remained elusive. This is likely due, in part, to a reliance on preclinical prevention models investigating the effects of genetic manipulations or pharmacological treatments on atherosclerosis development rather than the established disease. Also, results of these studies are often confounding because of the use of superficial lesion analyses and a lack of characterization of lesion cell populations. To help overcome these translational hurdles, we propose an increased reliance on intervention models that employ investigation of changes in cellular composition at a single cell level by immunofluorescent staining and confocal microscopy. To this end, we describe a protocol for testing a putative therapeutic agent in a murine intervention model including a systematic approach for animal dissection, embedding, sectioning, staining, and quantification of brachiocephalic artery lesions. In addition, due to the phenotypic diversity of cells within late-stage atherosclerotic lesions, we describe the importance of using cell-specific, inducible lineage tracing mouse systems and how this can be leveraged for unbiased characterization of atherosclerotic lesion cell populations. Together, these strategies may assist vascular biologists to more accurately model therapeutic interventions and analyze atherosclerotic disease and will hopefully translate into a higher rate of success in clinical trials.

We propose a standardized protocol to characterize the cellular composition of late-stage murine atherosclerotic lesions including systematic methods of animal dissection, tissue embedding, sectioning, staining, and analysis of brachiocephalic arteries from atheroprone smooth muscle cell lineage tracing mice.

Atherosclerosis remains the leading cause of death worldwide and, despite countless preclinical studies describing promising therapeutic targets, novel ...

A Protocol for Roux-en-Y Gastric Bypass in Rats using Linear Staplers

Roux-en-Y gastric bypass (RYGB) is commonly performed for the treatment of severe obesity and type 2 diabetes. However, the mechanism of weight loss and metabolic changes are not well understood. Multiple factors are thought to play a role, including reduced caloric intake, decreased nutrient absorption, increased satiety, the release of satiety-promoting hormones, shifts in bile acid metabolism, and alterations in the gut microbiota.
The rat RYGB model presents an ideal framework to study these mechanisms. Prior work on mouse models have had high mortality rates, ranging from 17 to 52%, limiting their adoption. Rat models demonstrate more physiologic reserve to surgical stimulus and are technically easier to adopt as they allow for the use of surgical staplers. One challenge with surgical staplers, however, is that they often leave a large gastric pouch which is not representative of RYGB in humans. 
In this protocol, we present a RYGB protocol in rats that result in a small gastric pouch using surgical staplers. Utilizing two stapler fires which remove the forestomach of the rat, we obtain a smaller gastric pouch similar to that following a typical human RYGB. Surgical stapling also results in better hemostasis than sharp division. Additionally, the forestomach of the rat does not contain any glands and its removal should not alter the physiology of RYGB.
Weight loss and metabolic changes in the RYGB cohort were significant compared to the sham cohort, with significantly lower glucose tolerance at 14 weeks. Furthermore, this protocol has an excellent survival of 88.9% after RYGB. The skills described in this protocol can be acquired without previous microsurgical experience. Once mastered, this procedure will provide a reproducible tool for studying the mechanisms and effects of RYGB.

Roux-en-Y gastric bypass (RYGB) is performed to treat obesity and diabetes. However, the mechanisms underlying RYGB's efficacy are not fully understood, and studies are limited by technical difficulty leading to high mortality in animal models. This article provides instructions on how to perform RYGB in rats with high success rates.

Roux-en-Y gastric bypass (RYGB) is commonly performed for the treatment of severe obesity and type 2 diabetes. However, the mechanism of weight loss and ...

Developing an Exercise Program for Rheumatoid Arthritis Patients

Evaluation of Changes in Hydration and Body Cell Mass with Bioelectrical Impedance Analysis after Exercise Program for Rheumatoid Arthritis Patients

Rheumatoid arthritis (RA) is a debilitating disease that can result in complications such as rheumatoid cachexia. While physical exercise has shown benefits for RA patients, its impact on hydration and body cell mass remains uncertain. The presence of pain, inflammation, and joint changes often restrict activity and make traditional body composition assessments unreliable due to altered hydration levels. Bioelectrical impedance is a commonly used method for estimating body composition, but it has limitations since it was primarily developed for the general population and does not consider changes in body composition. On the other hand, bioelectrical impedance vectorial analysis (BIVA) offers a more comprehensive approach. BIVA involves graphically interpreting resistance (R) and reactance (Xc), adjusted for height, to provide valuable information about hydration status and the integrity of the cell mass.
Twelve women with RA were included in this study. At the beginning of the study, hydration and body cell mass measurements were obtained using the BIVA method. Subsequently, the patients participated in a six-month dynamic exercise program encompassing cardiovascular capacity, strength, and coordination training. To evaluate changes in hydration and body cell mass, the differences in the R and Xc parameters, adjusted for height, were compared using BIVA confidence software. The results showed notable changes: resistance decreased after the exercise program, while reactance increased. BIVA, as a classification method, can effectively categorize patients into dehydration, overhydration, normal, athlete, thin, cachectic, and obese categories. This makes it a valuable tool for assessing RA patients, as it provides information independent of body weight or prediction equations. Overall, the implementation of BIVA in this study shed light on the effects of the exercise program on hydration and body cell mass in RA patients. Its advantages lie in its ability to provide comprehensive information and overcome the limitations of traditional body composition assessment methods.

Author Spotlight: Implementation of BIVA for Analyzing Disease Risk Factors in Patients with Low Body Cell Mass 

This protocol assesses alterations in hydration and body cell mass status using bioelectrical impedance vectorial analysis following a dynamic exercise program designed for patients with rheumatoid arthritis. The dynamic exercise program itself is detailed, highlighting its components focused on cardiovascular capacity, strength, and coordination. The protocol details steps, instruments, and limitations.

Rheumatoid arthritis (RA) is a debilitating disease that can result in complications such as rheumatoid cachexia. While physical exercise has shown ...

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Subtitles

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos