We would like to acknowledge and thank Rui Wang and Wah Chiu from Baylor College of Medicine for providing the cryoET dataset and Andrew Bodey from Diamond Light Source for assisting with the I13 beamtime. Parts of this research were supported by the National Institutes of Health (NIH) grant No. (P41GM103832) We acknowledge Diamond Light Source for jointly funding Imanol Luengo under PhD STU0079.

NOTE: Generally useful ranges of parameters for each processing step and the specific parameters for each data type shown here are provided in Table 1.
1. Preparing a Workspace and Loading Data
<ol>
	<li>Launch SuRVoS Workbench, click the open dataset button, and in the resulting popup, select the data file to be segmented. Choose an appropriate orientation of the dataset. Next, choose or create a folder where the workspace and associated files will be stored. It is recommended that this folder is empty when starting a new segmentation. After the data has been loaded, the workbench will open with the Plugins pane on the left, the Visualization pane on the right and a set of Tool shortcuts between the two panes (Figure 1).</li>
</ol>
2. Preprocessing and Data Representation
<ol>
	<li>In the Select ROI tab, input the z, y, and x start and end coordinates for the region of interest and click add. To choose appropriate y, and x coordinates hover the mouse over a point on the image. Choose z coordinates using the slider at the top of the Visualization pane. Once a region has been added, make sure it is selected by checking the box to the right. All downstream calculations will be performed on the selected region. Generally, starting with a small, representative region of interest (ROI), optimizing parameters and then re-applying these parameters to the whole area to be segmented is advised.</li>
	<li>In the Feature Channels tab, use the drop-down menu at the top to choose a feature/filter and add it to the queue (see Discussion for more info about feature channels). Once a feature/filter has been added and selected by clicking on its name, modify options specific to the feature/filter and choose the input dataset on which to run the feature/filter. Once all options have been chosen, click the checkbox to the right of the feature/filter name to compute.
	<ol>
		<li>In order to optimize parameters for a new dataset, add multiple filters/features and choose the parameters for them, before being computed in order, one after the other. To do this, add each new filter/feature and select appropriate parameters, check the box to the left of each filter/feature to be run, and click the compute features box at the top of the pane. See Discussion for additional information.</li>
	</ol>
	</li>
</ol>
3. Generating Appropriate Super-Regions
<ol>
	<li>In the Super Regions tab, in the Supervoxels section, use the source drop-down menu to select the filtered dataset from which supervoxels will be created. Then specify the shape, spacing and compactness of the supervoxels (see Discussion and Figure 2 for further details). Finally, click on the apply button to generate the supervoxels. Once the supervoxels have been created they can be viewed in the Visualization pane, turned on or off and their transparency controlled in the Visualization tab and Viewer Window shortcut.</li>
	<li>In the Super Regions tab, in the Megavoxels section, use the source drop-down menu to select the filtered dataset from which megavoxels will be created. Next, specify the lambda, numBins and Gamma parameters of the megavoxels (see Discussion for further details). Once the megavoxels have been created they can be viewed in the Visualization pane, turned on or off and their transparency controlled in the Visualization tab and Viewer Window shortcut.</li>
</ol>
4. Introduction to Annotation
<ol>
	<li>In the Annotations tab use the Add Level button to add an annotation level. After a Level has been added, use the Add Label button in that level to add a label for the annotation. Once added, the name and color of the label can be modified for ease of annotation.</li>
	<li>Next, in order to begin annotating, select the pen icon from the tool shortcut section. When this is selected a set of options appear at the top of the Visualization pane. These options control the pen width and whether voxels, supervoxels or megavoxels will be used to annotate.
	<ol>
		<li>For the purpose of model training, generally, select supervoxels in the Annotation Level drop-down box and a middling to large pen width should be used. In the annotations tab, select the label to be annotated by checking the box to the far right of the label information. Next, click in the Visualization pane to annotate a single supervoxel, or click and drag to annotate many. 
		NOTE: Voxels and megavoxels can be selected in the Annotation Level drop-down box and used to annotate in the same way, which, in the case of megavoxels, can enable many thousands of similar voxels to be segmented with a single click of the mouse.</li>
	</ol>
	</li>
</ol>
5. Segmentation Using Model Training demonstrated with a microCT dataset.
NOTE: The first segmentation for many datasets is to differentiate multiple large regions from each other. For example, separating the nucleus from the cytoplasm, or the cell from the external ice and support structure. For this type of segmentation, with clear delineated boundaries and large regions, model training is useful. To demonstrate this, X-ray phase contrast tomographic data of Goosegrass will be used.
<ol>
	<li>Load the data, preprocess using the filter and feature suite, and determine appropriate supervoxels and/or megavoxels as described in the above sections using the parameters in Table 1 as a guide. Continuing to use the parameters from Table 1 and the instructions in Section 4, roughly annotate some large areas of the dataset, as shown in Figure 3. 
	NOTE: The dataset does not need to be completely segmented at this point.</li>
	<li>In the Model Training tab, set the predict level to the level that contains the manual training annotations, and in the descriptor section set the region to Supervoxels. Next, select the descriptors that are to be used to differentiate regions of the data by clicking on the Select Sources drop-down and checking the boxes of the features and filters of choice (see Table 1 and discussion).</li>
	<li>Next, click on the predict button. After computation is complete, the Visualization pane will be updated with the predictions for all of the non-labeled voxels showing which of the annotation classes they are predicted to belong to. Generally, the default parameters for each classifier methodology provide a good starting point and the user should only need to switch between classifiers to find a good fit. However, for expert or experienced users the options for each classifier are available and can be modified.</li>
	<li>After assessing the effect of the training methodologies and choosing one, apply additional refinement by clicking on the Refine drop-down in the Refinement section. At the bottom of the Model Training tab, in the &#34;Update Annotations&#34; section, make sure that the visualization drop-down menu is set to Predictions. Use the confidence slider to assign more or less of the unannotated supervoxels to the selected annotation labels.</li>
	<li>After an appropriate level of confidence has been selected based on visual inspection, use the Save buttons next to the labels at the bottom of the Confidence tool to save the predictions into specific labels. The Visualization pane will update to show the changes. Each label can be saved separately, and indeed, labels can be saved from smaller sub-regions by inputting values in the From and To z, y, and x boxes and clicking on the Save button for each label.</li>
	<li>Address minor mislabeling by providing further training data as described in Section 4. After appropriate predictions are added to labels, repeat the process of model training with refinement and adding high confidence predictions until there are no more unlabeled supervoxels. This is effective because each time the model training process is run there are more assigned supervoxels to train with, and hence the process becomes more robust as the iterations increase.</li>
</ol>
6. Segmentation using Super-Regions, Demonstrated with a CryoET Dataset.
NOTE: Since Super-Region segmentation is useful for smaller, discretely bound areas, the focus here will be on segmentation of the organelles and microtubules within this dataset. Model training was used to quickly segment the platelet from the background ice and carbon; these parameters are not discussed further, but are presented in Table 1.
<ol>
	<li>Load the data, preprocess using the filter and feature suite, and determine appropriate supervoxels and/or megavoxels as described in the above sections using the parameters in Table 1 as a guide.</li>
	<li>Add appropriate levels and labels to the annotation tab, select a label and begin annotating using a middling pen width with supervoxels selected. Be mindful of the need to choose different labels for objects in close proximity to each other in order to avoid labeling them as a single object.</li>
	<li>In order to clean up the annotations further, use morphological refinement methods (dilation, erosion, opening, closing and fill holes). These options can be found at the bottom of the Annotations tab. To use them, select the segmentation label and refinement method. Enter a radius value and choose how to apply the refinement. Then click refine.</li>
</ol>
7. Classification and Analysis of Data Objects Based on Inherent Characteristics, demonstrated with a CryoSXT Dataset
NOTE: Generally, the next step after segmentation is analysis of the data. The label splitter tool in SuRVoS allows for the classification of segmented objects using rules based on intrinsic characteristics of the objects such as average object intensity, variance, volume, or position. The label statistics tool allows for the visualization of relationships between these measures for each new object class. These are powerful new tools for the analysis of complex 3D datasets after segmentation.
<ol>
	<li>Load the data, preprocess using the filter and feature suite, determine appropriate supervoxels and/or megavoxels and segment as described in the above sections using the parameters in Table 1 as a guide.</li>
	<li>After segmentation, click on the second tab of the Visualization pane, called Label Splitter. This will add a new area to the right-hand side of the window - the Rule Creation pane.</li>
	<li>At the top of this area, select an appropriate level and labels for label splitting. Then select the dataset to query and click Label. All of the objects in the selected labels will now be outlined in blue as separate objects in the Visualization pane and a plot showing the average intensity of the objects will be displayed in the Rule Creation pane. To change the measure being shown in the plot, click on the drop-down box at the top of the right-hand side.</li>
	<li>To begin to split the objects into relevant classes, click add new label at the bottom of the Rule Creation pane. The name and color associated with this new label can be changed as described previously.
	<ol>
		<li>Click Add new rule and using the drop-down and freeform entry boxes define the rule to be applied. Click Apply to see the effects of the new rule in the Visualization pane and the plot in the Rule Creation pane. Multiple rules can be applied to a single label and multiple labels can be created within the same dataset. 
		NOTE: In order to gather any unlabeled objects, create a new label and instead of adding a rule to it, click Select Others.</li>
	</ol>
	</li>
	<li>When the objects of interest have been split into new labels, create a new, empty level in the Annotations tab. Then choose this level in the Rule Creation Tab and click Save labels. This will save the new labels to this empty level.</li>
	<li>On the edge of the Visualization pane, click on the Label Statistics tab. This will open a new Visualization pane that can be used to begin to understand relationships between object classes in the data. At the top, select an appropriate level and labels, and the dataset to query.
	<ol>
		<li>Select a few of the measures of interest by checking the boxes next to them. Then click Label. This will produce pairwise comparison plots for each of the selected measures. To update the plots, select or deselect appropriate measures then click Update plot.</li>
	</ol>
	</li>
</ol>
8. Exporting Data and Segmentations
<ol>
	<li>Export the plots and the raw measurement data by clicking on Export plot and Export data, respectively, in the Label Statistics tab of the Visualization pane.</li>
	<li>Export the image data and segmentations by clicking on the Export tab in the Plugins pane. First, click to select a folder where the data will be saved. Next, select the output (Raw Data, Raw Annotations, Segmentation Masks, or Masked Data) and the format (HDF5, MRC, or TIFF). Lastly, select the Annotation level(s) to be exported using the check boxes and click Export. The data can be scaled and inverted prior to exporting as necessary. When exporting the masked data, the dataset that the mask(s) will be applied to can be selected using a drop-down menu.</li>
</ol>

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The authors declare they have no competing financial interests.

SuRVoS Workbench differs from other segmentation programs in that optimization of parameters is a necessary and important step prior to beginning the actual segmentation. In some manual or semi-manual segmentation programs, the user begins segmenting within moments of opening a new project. With SuRVoS, because large amounts of the volume will be segmented with very little user input and boundaries are delineated by the program, optimizing the parameters is critical to a successful segmentation. Specifically, feature channels and super-region building are two areas where attention should be paid.
Feature channels and model training
In addition to the raw data, SuRVoS allows the user to create additional datasets or channels derived from an existing dataset. These channels can be created using a selection of computational methodologies or feature extractors. Each of the data representations are available in parallel, and can be individually displayed to assess outcomes of feature or filter application. Because of these characteristics, they are referred to as feature channels in SuRVoS. There are many feature channel options provided within SuRVoS. For information on the options and parameters used here, see Table 1, for a full list and description of available feature channels visit https://diamondlightsource.github.io/SuRVoS/ 2. First, noisy datasets will benefit from denoising with either the Gaussian or total variation filter. It is recommended that further feature channel and supervoxel/megavoxel computations be performed using one of these denoised datasets as the data source. Generally, the total variation denoised dataset is used as the source data for feature channel and supervoxel/megavoxel computations. It is suggested to run with the default values first, assess the result in 3D and finally, iteratively optimize the parameters for the dataset. Additionally, feature channels can be built up into &#34;filter sets&#34; to specifically isolate aspects of the dataset, and these can then be used as data sources to create supervoxels and megavoxels. While this strategy is highly dataset dependent, it can be beneficial.
Feature channels are also used as sources to train the classifier in model training. When deciding on which feature channels to use, it is recommended that a few robust feature channels (e.g., from blob detection, texture and structure, or robust features categories) are used when working with a small amount of annotations to train the classifier. When working with a large amount of training data, it is recommended to use more feature channels overall, from any of the categories as long as they provide varied information to the classifier (e.g., add to the above list feature channels from local features and Gaussian features categories).
There are three main parts to model training: providing input data sources that describe the data, using these inputs to train a classifier, and finally refining the output predictions. Generally, smaller regions of the data will require more user annotations to accurately train the classifier, while larger regions of the data will require fewer user annotations. Model training first without selecting a refinement can be used to find the best predictions. Then include the refinement and optimize the lambda parameter as needed to fix issues with the predictions such as holes or jagged edges.
Supervoxels and megavoxels
Supervoxels are clusters of multiple nearby, similar voxels38,39. Supervoxels begin as a standard 3D grid overlaid on the data that is then iteratively deformed in order to adhere to the underlying boundaries, and thus better represent the data. Supervoxel creation and deformation is controlled by four user inputs: data source, superpixel shape, spacing, and compactness. The data source provides the data inputs that are queried during supervoxel creation. Any source can be used including filtered data sources. The superpixel shape parameters determine the starting 3D grid and the approximate desired shape of the resulting supervoxels. Changing these parameters can increase or decrease the size of the supervoxels before deformation. The spacing parameters define the importance of boundaries in each direction. Changing these parameters can emphasize boundaries in one or two directions at the expense of the other(s), meaning the resulting supervoxels will deform to better follow data boundaries in the given direction(s). The last parameter, compactness, controls how much the supervoxels can deform. A low compactness number allows the supervoxels to deform more. These parameters should be optimized to provide supervoxels that represent the boundaries of the data of interest. Note: Currently, supervoxel shape parameters must equal 1024 or less when multiplied together.
In some ways, supervoxel parameters can compensate for each other, meaning there is no one &#34;right answer&#34; when deciding on parameters. For example, a large starting grid (e.g. superpixel shape: 10 x 10 x 10) and a low compactness number (ex. 20) may give supervoxels with similar boundary adherence as compared to a small starting grid (e.g. superpixel shape 5 x 5 x 5) and a higher compactness number (e.g. 50). Because there are more, smaller supervoxels in the second scenario, they don&#39;t have to deform as much to represent boundaries. Both sets of parameters could be appropriate for segmentation of the dataset.
The biggest consideration when choosing supervoxel parameters is how well the supervoxels represent the data. Displaying the supervoxels alone, without data underneath them, as in Figure 2D, is a good way to assess supervoxel parameters. When displayed this way, the edges and outlines of shapes found in the data should still be visible in the supervoxels.
Megavoxels are conglomerates of multiple neighboring, similar supervoxels38,39. They are again controlled by four user inputs: data source, lambda, numbins, and gamma. As with supervoxels, the data source provides the data inputs that are queried during megavoxel creation. Both lambda and numbins impact the size and boundary adherence of the megavoxels. As the megavoxels grow larger (high lambda, low numbins), their boundary adherence decreases. The converse is also true, boundary adherence will increase with smaller megavoxels (low lambda, high numbins), however as the megavoxel size decreases, so does their usefulness in segmenting large amounts of voxels quickly. The optional gamma parameter controls the smoothness factor versus the cost of merging two supervoxels together. Small values of gamma can enhance the similarity between two supervoxels, at the cost of having fewer megavoxels overall.
As with supervoxels, the biggest consideration when choosing and optimizing megavoxel parameters is how well the megavoxels represent the data. Displaying the megavoxels alone as described for supervoxels can again be used to assess parameters. However, because megavoxels will generally be much larger and are three-dimensional, using the annotation tools to choose single megavoxels to ensure the boundary between regions of interest is tight is also recommended.
Annotation strategy
Two general annotation strategies have been described: a model training approach is useful for separating large regions of a dataset, while a super-region segmentation approach is useful for smaller, more diverse features such as individual organelles. Annotations can be organized in a hierarchical fashion so that it is possible to annotate large regions first, then subdivide them into more specific regions using a parent-child relationship. The parent label for a label can be assigned by clicking on the area to the right of the label color selection and choosing an appropriate parent label from a previous level. In practice, most datasets use both the model training and super-region segmentation strategies to segment specific regions/features of interest.
In the model training example here, a few training inputs (in the form of manual user supervoxel-based annotations) were used on three equally spaced slices of the data. In this way, the model training aspect of SuRVoS vastly increases the speed with which segmentation is possible especially when working with large, differentiated regions such as the divide between the regions in the goosegrass fruiting body as highlighted in Figure 3.
When model training, if the predictions cannot be seen, it may be necessary to go to the Visualization tab and make sure that the Predictions layer is turned on and set to an appropriate amount of transparency. Also, a confidence of 0 will assign every unlabeled supervoxel to a label, based on whatever the closest match is. Confidence of 100 will only assign a label if only one category of label has any proportional match. Everything in between is a tradeoff of these two extremes. When selecting a confidence level it is suggested to check a few slices to visually inspect that there are no incorrectly predicted voxels prior to saving the prediction to a label.
A good strategy for annotating using super-regions is to use the magnification tool to zoom in on the data, annotate a few organelles at once on one slice, using a &#34;quick, messy&#34; approach first (Figure 4C). Next, move up or down a few slices in Z and repeat this process. Because supervoxels are three-dimensional, many of the faults of the &#34;messy&#34; approach are fixed by annotations made in the above or below slices. In this way, segmentation is sped up and the boundaries are provided by the supervoxels rather than manually.
To clean up a label, standard segmentation refinement options have been provided. Dilation causes the selected segmentation label to grow by the given radius, erosion causes it to shrink. Opening and closing are the application of first erosion and then dilation, or vice-versa, respectively. And fill holes does exactly that. The order of these operations does matter. Generally, performing fill holes, then opening, then dilation works well. Each refinement method can be applied on a single slice (&#34;this slice&#34;), on all slices in 2D (&#34;All slices (2D)&#34;) or in 3D (&#34;Whole volume (3D)&#34;). All slices (2D) is recommended.
Significance and future directions
Efficient and accurate segmentation is the next bottleneck in processing of 3D datasets, especially with the routine automated collection of terabytes of image data during long-run sessions. SuRVoS Workbench can speed the segmentation process by a factor of 5 as compared to manual segmentation. Also, because the boundaries are delineated by supervoxels, the variability of the resultant segmentations should improve. In future, we hope to explore ways of using the segmentation of a representative 3D region of interest as training data to apply to the rest of the volume, or even a separate volume, with high confidence. This advance would further decrease the amount of user time and input necessary to segment even complex biological volumes, helping to alleviate the image processing and segmentation bottleneck. This, in turn, will allow the quantitative comparison of biological data in various states (e.g. non-disease, disease, treated) with robust experimental numbers.

The SuRVoS Workbench is a piece of software designed to allow researchers to extract scientifically relevant information from volumetric data from various samples, regardless of the structure of interest, resolution or the imaging modality1,2. Volumetric data such as these are often collected using X-ray or electron tomography systems, routinely based at large laboratories or centralized facilities due to their complexity. Both of these methods, and other techniques, produce large, information rich datasets which prove challenging to segment with either semi-automatic methods or manually. Particularly, near-native state cryo-immobilized datasets require low-dose imaging conditions, resulting in a low signal-to-noise ratio and poor contrast, especially in cryo electron tomography (cryoET)3,4,5. An additional factor in some 3D datasets is the presence of artifacts introduced by the challenging experimental conditions involved, for example missing wedge artifacts due to data collection over a limited tilt range, resulting in missing information and elongation in the direction of the beam3,4,5. Even when low signal-to-noise or missing wedge artifacts are not problematic (e.g. focused ion beam SEM6 or serial block face SEM7), the complexity and three-dimensional nature of the sample, and the large amount of data mean analysis would still benefit from an automated process for data segmentation.
Currently, when considering biological volumes of cells, there are many options for automatically or semi-automatically identifying very specific cellular features, such as actin, microtubules, or specific protein complexes, using a template-based search, or identifying features in specific types of datasets (e.g. high contrast, stained, resin-embedded samples)8,9,10,11,12. However, in these cases a priori information or specific sample preparation protocols are necessary, limiting the broad applicability of these segmentation strategies. There are also tools available that perform model training at the voxel level to learn the appearance of various structures of interest when given user input13. However, at this level the complexity of training and testing the models can be error-prone and computationally expensive. Given the challenging image conditions, and the lack of broadly applicable, semi-automatic segmentation strategies, manual segmentation is common, even when working with complex biological materials14,15,16,17. However, it is generally accepted that the process of manual segmentation is not only time-consuming, but also error-prone, subjective and variable4,5,18,19,20. Some segmentation programs offer tools to ease the manual segmentation process (i.e. interpolation, lasso, or blow tools)21,22, however, in cases of noisy datasets, they are difficult to apply successfully, and even when they are used successfully, the process is still subjective and variable.
Traditionally, segmentations have been used in two distinct ways: qualitatively or quantitatively. As imaging technologies and segmentation strategies improve, it has become more common to use segmentation as a quantitative tool to answer biological questions and as a &#34;ground truth&#34; for algorithm development8,12,15,23,24,25. In order to do this, detailed checks and balances are required to decrease variability and subjectivity throughout the process26. However, these precautions further increase the time-consuming nature of segmentation. Because of this, it is critical to provide a faster and less variable segmentation strategy.
The SuRVoS Workbench begins to address these issues by providing the user with a selection of machine learning and image processing tools that assist the user in the segmentation process, while also guiding the user through the required steps. To achieve this, two key innovations are implemented together in SuRVoS. First, it uses a super-region hierarchy to group similar, nearby regions of the data based on their inherent properties. Each of the regions in the hierarchy represents the same volume using fewer elements, while still providing strong boundary adherence. Thus, super-regions reduce the complexity of segmenting a volume by several orders of magnitude yet still represent the data without significant loss of information27. Second, SuRVoS provides a semi-automated segmentation strategy that uses minimal manual segmentation inputs to train classifiers, which are then used to segment the remaining volume28,29. This strategy reduces manual segmentation, greatly decreasing the amount of user time spent on segmenting and, when using super-regions, removes manual delineation of boundaries, potentially reducing variability and subjectivity.
A further key feature of SuRVoS is the Label Splitter Tool, whereby a user can classify a series of already segmented objects based on their inherent properties. After segmentation of various objects of interest, this tool can be used to divide the set into subclasses based on measures such as average object intensity, variance, size, location, etc. This is useful when classifying large groups of objects with high complexity. For example, a group of cellular organelles can be split into mitochondria, empty vesicles, lipid droplets, etc.; or a set of material inclusions can be separated based on size or shape. Once segmented the individual labels can be split into groups using any number of classifiers, reducing identification bias.
The SuRVoS Workbench has been successfully used to segment data from several imaging techniques. Here, synchrotron X-ray phase contrast tomography (microCT) of the fruiting body of a plant is used to demonstrate segmentation using model training, cryo electron tomography (cryoET) of human platelets is used to demonstrate segmentation using super- and megavoxels, and cryo soft X-ray tomography (cryoSXT) of a mammalian cell line is used to demonstrate the label splitting tools

<table><tbody><tr><td>computer</td><td>n/a</td><td>n/a</td><td>Must be running Linux operating system and have an NVidia GPU with at least 4 GB of memory</td></tr></tbody></table>

volume segmentation and analysis of biological materials using survos (super-region volume segmentation) workbench

Segmentation is the process of isolating specific regions or objects within an imaged volume, so that further study can be undertaken on these areas of interest. When considering the analysis of complex biological systems, the segmentation of three-dimensional image data is a time consuming and labor intensive step. With the increased availability of many imaging modalities and with automated data collection schemes, this poses an increased challenge for the modern experimental biologist to move from data to knowledge. This publication describes the use of SuRVoS Workbench, a program designed to address these issues by providing methods to semi-automatically segment complex biological volumetric data. Three datasets of differing magnification and imaging modalities are presented here, each highlighting different strategies of segmenting with SuRVoS. Phase contrast X-ray tomography (microCT) of the fruiting body of a plant is used to demonstrate segmentation using model training, cryo electron tomography (cryoET) of human platelets is used to demonstrate segmentation using super- and megavoxels, and cryo soft X-ray tomography (cryoSXT) of a mammalian cell line is used to demonstrate the label splitting tools. Strategies and parameters for each datatype are also presented. By blending a selection of semi-automatic processes into a single interactive tool, SuRVoS provides several benefits. Overall time to segment volumetric data is reduced by a factor of five when compared to manual segmentation, a mainstay in many image processing fields. This is a significant savings when full manual segmentation can take weeks of effort. Additionally, subjectivity is addressed through the use of computationally identified boundaries, and splitting complex collections of objects by their calculated properties rather than on a case-by-case basis.

Watch this Scientific Journal Video about Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench at JoVE.com

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Segmentation of three-dimensional data from many imaging techniques is a major bottleneck in analysis of complex biological systems. Here, we describe the use of SuRVoS Workbench to semi-automatically segment volumetric data at various length-scales using example datasets from cryo-electron tomography, cryo soft X-ray tomography, and phase contrast X-ray tomography techniques.

Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench

Segmentation is the process of isolating specific regions or objects within an imaged volume, so that further study can be undertaken on these areas of ...

volume-segmentation-analysis-biological-materials-using-survos-super

Research

JoVE Journal

Biology

9.8K Views.  Diamond Light Source. Segmentatie van driedimensionale gegevens uit vele beeldvormingstechnieken is een grote knelpunt in de analyse van complexe biologische systemen. Hier beschrijven we het gebruik van SuRVoS Workbench tot semi-automatisch segment volumetrische gegevens op verschillende lengte-schalen voorbeeld datasets van cryo-Elektronentomografie, cryo zachte X-ray tomografie en fase contrast X-ray tomografie technieken gebruiken.

Video: Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

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 ...

Medicine

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Modern 3D electron microscopy approaches have recently allowed unprecedented insight into the 3D ultrastructural organization of cells and tissues, enabling the visualization of large macromolecular machines, such as adhesion complexes, as well as higher-order structures, such as the cytoskeleton and cellular organelles in their respective cell and tissue context. Given the inherent complexity of cellular volumes, it is essential to first extract the features of interest in order to allow visualization, quantification, and therefore comprehension of their 3D organization. Each data set is defined by distinct characteristics, e.g., signal-to-noise ratio, crispness (sharpness) of the data, heterogeneity of its features, crowdedness of features, presence or absence of characteristic shapes that allow for easy identification, and the percentage of the entire volume that a specific region of interest occupies. All these characteristics need to be considered when deciding on which approach to take for segmentation.
The six different 3D ultrastructural data sets presented were obtained by three different imaging approaches: resin embedded stained electron tomography, focused ion beam- and serial block face- scanning electron microscopy (FIB-SEM, SBF-SEM) of mildly stained and heavily stained samples, respectively. For these data sets, four different segmentation approaches have been applied: (1) fully manual model building followed solely by visualization of the model, (2) manual tracing segmentation of the data followed by surface rendering, (3) semi-automated approaches followed by surface rendering, or (4) automated custom-designed segmentation algorithms followed by surface rendering and quantitative analysis. Depending on the combination of data set characteristics, it was found that typically one of these four categorical approaches outperforms the others, but depending on the exact sequence of criteria, more than one approach may be successful. Based on these data, we propose a triage scheme that categorizes both objective data set characteristics and subjective personal criteria for the analysis of the different data sets.

The bottleneck for cellular 3D electron microscopy is feature extraction (segmentation) in highly complex 3D density maps. We have developed a set of criteria, which provides guidance regarding which segmentation approach (manual, semi-automated, or automated) is best suited for different data types, thus providing a starting point for effective segmentation.

Modern 3D electron microscopy approaches have recently allowed unprecedented insight into the 3D ultrastructural organization of cells and tissues, ...

Bioengineering

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Obtaining in vivo human brain tissue volumetrics from MRI is often complicated by various technical and biological issues. These challenges are exacerbated when significant brain atrophy and age-related white matter changes (e.g. Leukoaraiosis) are present. Lesion Explorer (LE) is an accurate and reliable neuroimaging pipeline specifically developed to address such issues commonly observed on MRI of Alzheimer's disease and normal elderly. The pipeline is a complex set of semi-automatic procedures which has been previously validated in a series of internal and external reliability tests1,2. However, LE's accuracy and reliability is highly dependent on properly trained manual operators to execute commands, identify distinct anatomical landmarks, and manually edit/verify various computer-generated segmentation outputs.
LE can be divided into 3 main components, each requiring a set of commands and manual operations: 1) Brain-Sizer, 2) SABRE, and 3) Lesion-Seg. Brain-Sizer's manual operations involve editing of the automatic skull-stripped total intracranial vault (TIV) extraction mask, designation of ventricular cerebrospinal fluid (vCSF), and removal of subtentorial structures. The SABRE component requires checking of image alignment along the anterior and posterior commissure (ACPC) plane, and identification of several anatomical landmarks required for regional parcellation. Finally, the Lesion-Seg component involves manual checking of the automatic lesion segmentation of subcortical hyperintensities (SH) for false positive errors.
While on-site training of the LE pipeline is preferable, readily available visual teaching tools with interactive training images are a viable alternative. Developed to ensure a high degree of accuracy and reliability, the following is a step-by-step, video-guided, standardized protocol for LE's manual procedures.

Lesion Explorer (LE) is a semi-automatic, image-processing pipeline developed to obtain regional brain tissue and subcortical hyperintensity lesion volumetrics from structural MRI of Alzheimer's disease and normal elderly. To ensure a high level of accuracy and reliability, the following is a video-guided, standardized protocol for LE's manual procedures.

Obtaining in vivo human brain tissue volumetrics from MRI is often complicated by various technical and biological issues. These challenges are ...

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

The ability to visualize complex and dynamic physiological interactions between numerous cell types and the extracellular matrix (ECM) within a live tumor microenvironment is an important step toward understanding mechanisms that regulate tumor progression. While this can be accomplished through current intravital imaging techniques, it remains challenging due to the heterogeneous nature of tissues and the need for spatial context within the experimental observation. To this end, we have developed an intravital imaging workflow that pairs collagen second harmonic generation imaging, endogenous fluorescence from the metabolic co-factor NAD(P)H, and fluorescence lifetime imaging microscopy (FLIM) as a means to non-invasively compartmentalize the tumor microenvironment into basic domains of the tumor nest, the surrounding stroma or ECM, and the vasculature. This non-invasive protocol details the step-by-step process ranging from the acquisition of time-lapse images of mammary tumor models to post-processing analysis and image segmentation. The primary advantage of this workflow is that it exploits metabolic signatures to contextualize the dynamically changing live tumor microenvironment without the use of exogenous fluorescent labels, making it advantageous for human patient-derived xenograft (PDX) models and future clinical use where extrinsic fluorophores are not readily applicable.

The intravital imaging method described here utilizes collagen second harmonic generation and endogenous fluorescence from the metabolic co-factor NAD(P)H to non-invasively segment an unlabeled tumor microenvironment into tumor, stromal, and vascular compartments for in-depth analysis of 4D intravital images.

The ability to visualize complex and dynamic physiological interactions between numerous cell types and the extracellular matrix (ECM) within a live tumor ...

Cancer Research

Three and Four-Dimensional Visualization and Analysis Approaches to Study Vertebrate Axial Elongation and Segmentation

Somitogenesis is a hallmark of vertebrate embryonic development. For years, researchers have been studying this process in a variety of organisms using a wide range of techniques encompassing ex vivo and in vitro approaches. However, most studies still rely on the analysis of two-dimensional (2D) imaging data, which limits proper evaluation of a developmental process like axial extension and somitogenesis involving highly dynamic interactions in a complex 3D space. Here we describe techniques that allow mouse live imaging acquisition, dataset processing, visualization and analysis in 3D and 4D to study the cells (e.g., neuromesodermal progenitors) involved in these developmental processes. We also provide a step-by-step protocol for optical projection tomography and whole-mount immunofluorescence microscopy in mouse embryos (from sample preparation to image acquisition) and show a pipeline that we developed to process and visualize 3D image data. We extend the use of some of these techniques and highlight specific features of different available software (e.g., Fiji/ImageJ, Drishti, Amira and Imaris) that can be used to improve our current understanding of axial extension and somite formation (e.g., 3D reconstructions). Altogether, the techniques here described emphasize the importance of 3D data visualization and analysis in developmental biology, and might help other researchers to better address 3D and 4D image data in the context of vertebrate axial extension and segmentation. Finally, the work also employs novel tools to facilitate teaching vertebrate embryonic development.

Here, we describe computational tools and methods that allow visualization and analysis of three and four-dimensional image data of mouse embryos in the context of axial elongation and segmentation, obtained by in toto optical projection tomography, and by live imaging and whole-mount immunofluorescence staining using multiphoton microscopy.

Somitogenesis is a hallmark of vertebrate embryonic development. For years, researchers have been studying this process in a variety of organisms using a ...

Developmental Biology

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.

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 ...

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Serial sectioning and subsequent high-resolution imaging of biological tissue using electron microscopy (EM) allow for the segmentation and reconstruction of high-resolution imaged stacks to reveal ultrastructural patterns that could not be resolved using 2D images. Indeed, the latter might lead to a misinterpretation of morphologies, like in the case of mitochondria; the use of 3D models is, therefore, more and more common and applied to the formulation of morphology-based functional hypotheses. To date, the use of 3D models generated from light or electron image stacks makes qualitative, visual assessments, as well as quantification, more convenient to be performed directly in 3D. As these models are often extremely complex, a virtual reality environment is also important to be set up to overcome occlusion and to take full advantage of the 3D structure. Here, a step-by-step guide from image segmentation to reconstruction and analysis is described in detail.

The described pipeline is designed for the segmentation of electron microscopy datasets larger than gigabytes, to extract whole-cell morphologies. Once the cells are reconstructed in 3D, customized software designed around individual needs can be used to perform a qualitative and quantitative analysis directly in 3D, also using virtual reality to overcome view occlusion.

Serial sectioning and subsequent high-resolution imaging of biological tissue using electron microscopy (EM) allow for the segmentation and reconstruction ...

Neuroscience

Simplified Volumetric Models as an Effective Strategy for Segmenting Actin Networks in Cryo-Electron Tomograms

Efficient methods for the extraction of features of interest remain one of the biggest challenges for the interpretation of cryo-electron tomograms. Various automated approaches have been proposed, many of which work well for high-contrast datasets where the features of interest can be easily detected and are clearly separated from one another. Our inner ear stereocilia cryo-electron tomographic datasets are characterized by a dense array of hexagonally packed actin filaments that are frequently cross-connected. These features make automated segmentation very challenging, further aggravated by the high-noise environment of cryo-electron tomograms and the high complexity of the densely packed features. Using prior knowledge about the actin bundle organization, we have placed layers of a highly simplified ball-and-stick actin model to first obtain a global fit to the density map, followed by regional and local adjustments of the model. We show that volumetric model building not only allows us to deal with the high complexity, but also provides precise measurements and statistics about the actin bundle. Volumetric models also serve as anchoring points for local segmentation, such as in the case of the actin-actin cross connectors. Volumetric model building, particularly when further augmented by computer-based automated fitting approaches, can be a powerful alternative when conventional automated segmentation approaches are not successful.

Here, we present a protocol to place simplified volumetric models into noisy, complex, tomographic 3D volumes. This allows the fast segmentation of actin filament densities, detection of systematic filament bending and of gaps in hair bundle filaments, as well as convenient quantification of volumetric model properties, such as distances.

Efficient methods for the extraction of features of interest remain one of the biggest challenges for the interpretation of cryo-electron tomograms. ...

Biological Sample Preparation by High-pressure Freezing, Microwave-assisted Contrast Enhancement, and Minimal Resin Embedding for Volume Imaging

The described sample preparation technique is designed to combine the best quality of ultrastructural preservation with the most suitable contrast for the imaging modality in a focused ion beam scanning electron microscope (FIB-SEM), which is used to obtain stacks of sequential images for 3D reconstruction and modelling. High-pressure freezing (HPF) allows close to native structural preservation, but the subsequent freeze substitution often does not provide sufficient contrast, especially for a bigger specimen, which is needed for high-quality imaging in the SEM required for 3D reconstruction. Therefore, in this protocol, after the freeze substitution, additional contrasting steps are carried out at room temperature. Although these steps are performed in a microwave, it is also possible to follow traditional bench processing, which requires longer incubation times.The subsequent embedding in minimal amounts of resin allows for faster and more precise targeting and preparation inside the FIB-SEM. This protocol is especially useful for samples that require preparation by high-pressure freezing for a reliable ultrastructural preservation but do not gain enough contrast during the freeze substitution for volume imaging using FIB-SEM. In combination with the minimal resin embedding, this protocol provides an efficient workflow for the acquisition of high-quality volume data.

Here we present a protocol for combining two sample processing techniques, high-pressure freezing and microwave-assisted sample processing, followed by minimal resin embedding for acquiring data with a focused ion beam scanning electron microscope (FIB-SEM). This is demonstrated using a mouse tibial nerve sample and Caenorhabditis elegans.

The described sample preparation technique is designed to combine the best quality of ultrastructural preservation with the most suitable contrast for the ...

Serial Block-Face Scanning Electron Microscopy (SBF-SEM) of Biological Tissue Samples

Serial block-face scanning electron microscopy (SBF-SEM) allows for the collection of hundreds to thousands of serially-registered ultrastructural images, offering an unprecedented three-dimensional view of tissue microanatomy. While SBF-SEM has seen an exponential increase in use in recent years, technical aspects such as proper tissue preparation and imaging parameters are paramount for the success of this imaging modality. This imaging system benefits from the automated nature of the device, allowing one to leave the microscope unattended during the imaging process, with the automated collection of hundreds of images possible in a single day. However, without appropriate tissue preparation cellular ultrastructure can be altered in such a way that incorrect or misleading conclusions might be drawn. Additionally, images are generated by scanning the block-face of a resin-embedded biological sample and this often presents challenges and considerations that must be addressed. The accumulation of electrons within the block during imaging, known as "tissue charging," can lead to a loss of contrast and an inability to appreciate cellular structure. Moreover, while increasing electron beam intensity/voltage or decreasing beam-scanning speed can increase image resolution, this can also have the unfortunate side effect of damaging the resin block and distorting subsequent images in the imaging series. Here we present a routine protocol for the preparation of biological tissue samples that preserves cellular ultrastructure and diminishes tissue charging. We also provide imaging considerations for the rapid acquisition of high-quality serial-images with minimal damage to the tissue block.

Serial Block-Face Scanning Electron Microscopy SBF-SEM of Biological Tissue Samples

This protocol outlines a routine method for using serial block-face scanning electron microscopy (SBF-SEM), a powerful 3D imaging technique. Successful application of SBF-SEM hinges on proper fixation and tissue staining techniques, as well as careful consideration of imaging settings. This protocol contains practical considerations for the entirety of this process.

Serial block-face scanning electron microscopy (SBF-SEM) allows for the collection of hundreds to thousands of serially-registered ultrastructural images, ...

Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench

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