Name: lung ct segmentation to identify consolidations and ground glass areas for quantitative assesment of sars-cov pneumonia
Uploaded: 2020-12-19T14:00:00
Description: Watch this Scientific Journal Video about Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia at JoVE.com

This work was supported by funding from the Department of Radiology of the University of Bologna.

This protocol follows the guidelines of the institutional human research ethics committee.
1. Downloading the DICOM images
<ol>
	<li>Download the DICOM images and transfer them in the workstation dedicated to the segmentation, with the 3D software installed. If planning to work on a personal computer, anonymize the DICOM data.</li>
</ol>
2. Importing the HRCT study on the 3D Slicer software
<ol>
	<li>In the software opening screen (...

<ol>
	<li>Zheng, Y., 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=COVID-19+and+the+cardiovascular+system.">COVID-19 and the cardiovascular system.</a> Nature Reviews Cardiology. 17, 259-260 (2020).</li><li>Simpson, S., 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=Radiological+Society+of+North+America+Expert+Consensus+Statement+on+Reporting+Chest+CT+Findings+Related+to+COVID-19.+Endorsed+by+the+Society+of+Thoracic+Radiology,+the+American+College+of+Radiology,+and+RSNA.">Radiological Society of North America Expert Consensus Statement on Reporting Chest CT Findings Related to COVID-19. Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA.</a> Radiology: Cardiothoracic Imaging. 2, 2 (2020).</li><li>Xie, X., 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=Chest+CT+for+Typical+2019-nCoV+Pneumonia:+Relationship+to+Negative+RT-PCR+Testing.">Chest CT for Typical 2019-nCoV Pneumonia: Relationship to Negative RT-PCR Testing.</a> Radiology. , 200343 (2020).</li><li>Huang, P., 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=Use+of+Chest+CT+in+Combination+with+Negative+RT-PCR+Assay+for+the+2019+Novel+Coronavirus+but+High+Clinical+Suspicion.">Use of Chest CT in Combination with Negative RT-PCR Assay for the 2019 Novel Coronavirus but High Clinical Suspicion.</a> Radiology. 295 (1), 22-23 (2020).</li><li>Fang, Y., 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=Sensitivity+of+Chest+CT+for+COVID-19:+Comparison+to+RT-PCR.">Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR.</a> Radiology. , 200432 (2020).</li><li>Colombi, 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=Well-aerated+Lung+on+Admitting+Chest+CT+to+Predict+Adverse+Outcome+in+COVID-19+Pneumonia.">Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia.</a> Radiology. , 201433 (2020).</li><li>. 3D Slicer [software] Available from: <a target="_blank" href="https://www.slicer.org">https://www.slicer.org</a> (2020)</li><li>Kikinis, R., Pieper, S. D., Vosburgh, K., Jolesz, F. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=3D+Slicer:+a+platform+for+subject-specific+image+analysis,+visualization,+and+clinical+support.">3D Slicer: a platform for subject-specific image analysis, visualization, and clinical support.</a> Intraoperative Imaging Image-Guided Therapy. 3 (19), 277-289 (2014).</li><li>Kapur, T., 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=Increasing+the+impact+of+medical+image+computing+using+community-based+open-access+hackathons:+The+NA-MIC+and+3D+Slicer+experience.">Increasing the impact of medical image computing using community-based open-access hackathons: The NA-MIC and 3D Slicer experience.</a> Medical Image Analysis. 33, 176-180 (2016).</li><li>Fedorov, A., 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=3D+Slicer+as+an+Image+Computing+Platform+for+the+Quantitative+Imaging+Network.">3D Slicer as an Image Computing Platform for the Quantitative Imaging Network.</a> Magnetic Resonance Imaging. 30 (9), 1323-1341 (2012).</li><li>. Chest Imaging Platform [software] Available from: <a target="_blank" href="https://chestimagingplatform.org">https://chestimagingplatform.org</a> (2020)</li><li>Wang, J., Li, F., Li, Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+segmentation+of+lungs+with+severe+interstitial+lung+disease+in+CT.">Automated segmentation of lungs with severe interstitial lung disease in CT.</a> Medical Physics. 36 (10), 4592-4599 (2009).</li><li>Lanza, E., 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+chest+CT+analysis+in+COVID-19+to+predict+the+need+for+oxygenation+support+and+intubation.">Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation.</a> European Radiology. , (2020).</li><li>Yao, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Value+of+window+technique+in+diagnosis+of+the+ground+glass+opacities+in+patients+with+non-small+cell+pulmonary+cancer.">Value of window technique in diagnosis of the ground glass opacities in patients with non-small cell pulmonary cancer.</a> Oncology Letters. 12 (5), 3933-3935 (2016).</li><li>Kauczor, H. U., 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=Automatic+detection+and+quantification+of+ground-glass+opacities+on+high-resolution+CT+using+multiple+neural+networks:+comparison+with+a+density+mask.">Automatic detection and quantification of ground-glass opacities on high-resolution CT using multiple neural networks: comparison with a density mask.</a> American Journal of Roentgenology. 175 (5), 1329-1334 (2000).</li><li>Funama, Y., 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=Detection+of+nodules+showing+ground-glass+opacity+in+the+lungs+at+low-dose+multidetector+computed+tomography:+phantom+and+clinical+study.">Detection of nodules showing ground-glass opacity in the lungs at low-dose multidetector computed tomography: phantom and clinical study.</a> Journal of Computed Assisted Tomography. 33 (1), 49-53 (2009).</li><li>Hansell, D. 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Segmentation represents a fundamental step for performing modern quantitative radiology studies, and is necessary to apply radiomics or texture analysis techniques. Pathological findings in the lungs represent one of the most challenging to segment, for the lack of defined anatomical borders and a small difference in attenuation value when compared to the healthy areas.
The source images must present with a minimum of artifacts if possible, especially on the pathological areas, and this is som...

In the current year, the world is facing a health emergency, the pandemic caused by the novel Coronavirus, Sars-CoV2. Even if, up to the present date, many aspects regarding the physiopathology of the COVID-19 infection are still unclear, it shares several characteristics with its &#8220;ancestors&#8221; SARS1 and MERS. In particular, it has been proven that the virion spike proteins interact with the Angiotensin Converting Enzyme Type-2, a receptor well represented on the alveolar endothelial cells, but ubiquitous in the human organism, thus having the potentiality to give systemic symptoms1.
For diagnosis, the current standard is the real-time reverse transcriptase-polymerase chain reaction (rt-PCR), a test performed on pharyngeal swabs. Although radiological imaging is not officially recognized in the diagnostic path for the detection of the disease, high resolution computed tomography (HRCT) proved to be a valuable aid to the clinical and epidemiological management of affected patients, due to the relatively low sensitivity of the rt-PCR, the current scarcity of specialized laboratories and of the necessary reagents, and the high operator-dependency.
The Radiological Society of North America (RSNA) released a consensus statement, endorsed by the society of Thoracic Radiology and the American College of Radiology (ACR), that classifies the CT appearance of COVID19 into four categories in order to standardize the reporting, dividing the interstitial pneumonia patterns into &#8220;typical&#8221;, &#8220;atypical&#8221;, &#8220;indetermined&#8221;, and &#8220;negative&#8221;2.
The &#8220;typical&#8221; pattern is characterized by the presence of round-shaped Ground Glass Opacities (GGO), usually with a sub-pleural location on the dorsal basal segments. The GGO can be associated with &#8220;Crazy Paving&#8221; areas of thickened septa, or other signs of organizing pneumonia. The &#8220;indetermined&#8221; pattern is characterized by the absence of the &#8220;typical&#8221; pattern findings, with diffuse GGO areas with a perihilar distribution, with or without consolidative areas. The &#8220;atypical&#8221; pattern is characterized by either the absence of the &#8220;typical&#8221; or &#8220;undetermined&#8221; signs, and the presence of lobar consolidations, &#8220;tree in bud&#8221;, smooth thickening of the septa and pleural effusion; in this presentation no GGO are detectable. The &#8220;negative&#8221; pattern is characterized by the absence of the aforementioned pathological findings.
According to literature, some patients may present with a high clinical suspect of COVID-19 supported by epidemiological criteria and imaging finding with negative rt-PCR3,4. On the other hand, it has been reported that patients with a positive rt-PCR and suggestive clinical findings, do not present pathological findings on HRCT5.
Nowadays, it is of paramount interest for the scientific community to apply image analysis techniques when quantitatively studying the characteristics of this disease. A recent study has applied automated segmentation technique of the lung parenchyma to identify the percentage of aerated lung in patients affected by COVID-19, correlating this value with the prognosis, and demonstrating that patients with a more severe lung involvement presented more risk of being admitted in the Intensive Care Unit (ICU), and having worse outcomes6.
The segmentation is the contouring of regions of interest (ROIs) inside a volume acquired through an imaging technique, such as HRCT. This activity may be carried out though three methods: manual, semi-automatic and automatic. Manual segmentation, thanks to the experience of a trained radiologist, consists of labelling voxels belonging to the pathological area. The main disadvantages of this method are the large amount of time required and the fact that it is operator-dependent.
Semi-automatic methods allow to speed up the segmentation as the operator can modify a segmentation mask obtained through the classic methods of image processing (e.g., threshold on pixel intensity, clustering, etc.). However, these techniques are not easy to implement in clinical practice as they require extensive manual intervention in the most complicated cases18.
Automatic segmentation methods, currently of limited use, employ artificial intelligence to obtain ROIs. In particular, a recent study aims to use automatic segmentation in the quantification of ground glass areas in patients suffering from COVID-19 interstitial pneumonia19. The definition of a segmentation protocol for the pathological areas on the HRCT images is the real first step toward the subsequent radiomics analysis, in order to identify features that could help to further understand the physiopathology of the disease, and serve as an accurate prognostic factor potentially influencing the treatment.
This paper offers a guide to obtain accurate and efficient segments representing the pathological findings of COVID-19 pneumonia using &#8220;3D Slicer&#8221;7,8,9,10.

<table border="0" cellpadding="0" cellspacing="0" style="width:592px;" width="592">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="63">
			<td height="63" style="height:63px;width:216px;">CT Scanner</td>
			<td style="width:91px;">General Electrics Healthcare</td>
			<td style="width:119px;">64-MDCT VCT lightSpeed</td>
			<td style="width:167px;">The CT scanner used for HRCT acquisitions</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Desktop Computer</td>
			<td style="width:91px;">ThinkCentre</td>
			<td style="width:119px;"></td>
			<td>The computer used to download the DICOM files and run 3D Slicer</td>
		</tr>
	</tbody>
</table>

lung ct segmentation to identify consolidations and ground glass areas for quantitative assesment of sars-cov pneumonia

Segmentation is a complex task, faced by radiologists and researchers as radiomics and machine learning grow in potentiality. The process can either be automatic, semi-automatic, or manual, the first often not being sufficiently precise or easily reproducible, and the last being excessively time consuming when involving large districts with high-resolution acquisitions.
A high-resolution CT of the chest is composed of hundreds of images, and this makes the manual approach excessively time consuming. Furthermore, the parenchymal alterations require an expert evaluation to be discerned from the normal appearance; thus, a semi-automatic approach to the segmentation process is, to the best of our knowledge, the most suitable when segmenting pneumonias, especially when their features are still unknown.
For the studies conducted in our institute on the imaging of COVID-19, we adopted 3D Slicer, a freeware software produced by the Harvard University, and combined the threshold with the paint brush instruments to achieve fast and precise segmentation of aerated lung, ground glass opacities, and consolidations. When facing complex cases, this method still requires a considerable amount of time for proper manual adjustments, but provides an extremely efficient mean to define segments to use for further analysis, such as the calculation of the percentage of the affected lung parenchyma or texture analysis of the ground glass areas.

The proposed method has been refined through trials and errors, testing it on 117 patients affected by COVID-19 pneumonia with a positive rt-PCR test.
After a short learning curve, the time needed to obtain the segments can vary from 5 to 15 minutes, depending on the presentation pattern.
As shown on Figure 1, the method yields precise segments: this can be observed by noticing the exact correspondence with the HRCT. The 3D rendering helps to assess...

Watch this Scientific Journal Video about Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia at JoVE.com

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

The aim of this protocol is to provide a time efficient way to segment volumes of interest on high-resolution CT scans to use for further radiomics analysis.

Segmentation is a complex task, faced by radiologists and researchers as radiomics and machine learning grow in potentiality. The process can either be ...

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