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 (corresponding to the Welcome to Slicer section in the drop-down menu) select Load DICOM Data. Alternatively, select the DCM icon in the upper left corner of the toolbar.</li>
	<li>In the upper left corner of the DICOM Browser panel select Import, then select the location of the HRCT study. Select the folder with the DCM images, and then select Import.</li>
	<li>Now that the HRCT study has been successfully imported, click on the Load button.</li>
</ol>
3. Creating the Segments
<ol>
	<li>Create segments in the Segmentations section of the drop-down menu or directly in the sub-section Segment Editor found in the drop-down menu&#8217;s Segmentation section, or again as a dedicated icon in the toolbar.</li>
	<li>In the drop-down menu next to Master Volume, select the HRCT study.</li>
	<li>Select Add and create three new segments, which will be automatically named Segment 1, 2 and 3. Double click on each one and rename them respectively &#8220;tlv&#8221; (Total Lung Parenchyma), &#8220;ggo&#8221; (Ground Glass Opacities) and &#8220;cons&#8221; (Consolidations). If in the HRCT study additional pathological findings coexist, such as pleural effusion, lung cancer, fibrotic areas and so on, create additional segments. The same applies to artifacts.</li>
</ol>
4. Definition of the TLP segment
NOTE: An accurate definition of the TLP segment is fundamental, as it will be used for masking the HRCT during the definition of the GGO and CDs segments.
<ol>
	<li>As a first step, in the Segment Editor section, after selecting the TLP segment, choose the Threshold instrument.</li>
	<li>Set a threshold large enough to include both the healthy lung parenchyma and the ground glass opacities. Using the values reported in scientific literature with the necessary adjustments to comply with the textbook definition of GGO, we found that setting the threshold between -1000 HU and -250 HU works well6,14,15,16,17. Then select Apply.</li>
	<li>The segment so defined will include both the air inside the lungs and the air outside the chest (i.e., the air outside the patient). In order to isolate the lung parenchyma, use the Islands tool, found in the Segment Editor, and choose Keep selected island. Left click inside the chest so that anything outside the chest will be excluded from the segment.</li>
	<li>Next, include any area of consolidation inside the TLP segment. The consolidations usually have attenuation values (Hounsfield Units: HU) similar to those of soft tissues that constitute the chest wall and the mediastinum. This is the reason why the Threshold instrument cannot be used for this purpose and the consolidations will need to be added manually, using the Scissors and Paint tools found in the segment editor.
	<ol>
		<li>Use the Scissors tool to add portions of lung parenchyma to the segment. Choose the Fill Inside operation, select Free Form as the shape and Symmetric as the slice cut. Choose a thickness appropriate to the size of the consolidation (e.g., from 3 to 20 mm); and then, start progressively adding small portions of TLP, until the consolidation has been completely included.</li>
		<li>The Paint tool is easier and sometimes faster to use, but might be less precise, depending on the area to include. When the Sphere Brush option is active, this tool can add 3D portions of lung parenchyma to the segment. The size of the brush can be easily modified.</li>
		<li>While adding the consolidations to the TLP segment, do not include portions of thoracic wall or mediastinum. This could prove difficult as in patients affected by COVID19 consolidations adjacent to the posterior thoracic wall are quite common. Use the Erase tool to correct the segmentation or select Undo to discard the last action.</li>
		<li>At this stage, use the smoothing algorithm once more to delete small imperfections that might have occurred during the manual part of the segmentation. Now the TLP segment definition is concluded.</li>
	</ol>
	</li>
</ol>
5. Definition of the GGO segment
<ol>
	<li>To define the GGO segment, use the threshold tool.
	<ol>
		<li>Select the GGO segment.</li>
		<li>Set the threshold between -750 HU and -150 HU.</li>
		<li>Before selecting Apply, go to the Masking section right underneath and select TLP in the Editable Area drop down menu. In the Overwrite Other Segment drop-down menu, select None. This is extremely important as, if skipped, once defined the GGO segment, also the TLP segment would be modified. This must be avoided, as the TLP segment is the mask on which all the other segments are defined.</li>
		<li>Now select Apply.</li>
	</ol>
	</li>
	<li>At this point it will probably be necessary to use the smoothing algorithm once again, in order to exclude all the small vessels and the physiological interstitial elements that have a density similar to that of ground glass from the segment. Start with a Kernel size of 3 mm and, if needed, increase it progressively, to a maximum of 6-7 mm. Note that increasing the Kernel size too much might determine the loss of small areas of ground glass, which would be left outside the GGO segment. Because of this, the smoothing algorithm should be used with the proper caution.</li>
	<li>In this case too, apply needed corrections with the Paint, Scissors and Erase tool. During this phase remember to keep the TLP mask active at all times to avoid including portions of chest wall adjacent to the &#8220;ground glass&#8221; areas inside the segment. This is because, if erroneously segmented, they would automatically be excluded.</li>
	<li>When obtaining this segment, pay close attention to the possible presence of artifacts generated by movements of hearth and diaphragm (this happens if the patient could not hold his breath during the exam). If present and previously included in the TLP segment, eliminate these artifacts from the &#8220;ground glass&#8221; segment, for example through the Islands tool with the Remove Selected Island option or using the Scissors and Erase tools. Depending on the study&#8217;s goal, the artifacts might be segmented separately, using a dedicated segment or included in the TLP segment and excluded from other segments. Now the GGO segment has been defined.</li>
</ol>
6. Definition of the CDs segment
<ol>
	<li>To define the CDs segment, proceed the same way as to define the GGO segment.
	<ol>
		<li>Select the CDs segment. Be sure to work with the TLP masking always active.</li>
		<li>Set an adequate threshold. For consolidations the range varies, roughly, from -150 HU to 100 HU.</li>
		<li>Apply the smoothing algorithm varying the Kernel size as needed.</li>
		<li>Use a combination of the tools Scissors, Erase, Paint and Islands in order to keep inside the segment only the real consolidations, excluding large vessels, pleural effusions, artifacts, disventilatory bands and other non COVID-related lesions.</li>
		<li>If needed, apply a final smoothing might be applied.</li>
	</ol>
	</li>
</ol>
7. Saving the segments
<ol>
	<li>Save segments as an &#8220;.nrrd&#8221; file, or convert to binary label maps from the &#34;Data&#34; module.</li>
</ol>
8. Extracting volumes from the defined segments.
<ol>
	<li>From the Segment Statistics module, obtain a table with details on volume and surfaces of the segments.</li>
</ol>

<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. M., 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=Fleischner+Society:+glossary+of+terms+for+thoracic+imaging.">Fleischner Society: glossary of terms for thoracic imaging.</a> Radiology. 246, 697-722 (2008).</li><li>Heye, 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=Reproducibility+of+Dynamic+Part+II.+Comparison+of+Intra-+and+Interobserver+Variability+with+Manual+Region+of+Interest+Placement+versus+semiautomatic+lesion+segmentation+and+histogram+analysis.">Reproducibility of Dynamic Part II. Comparison of Intra- and Interobserver Variability with Manual Region of Interest Placement versus semiautomatic lesion segmentation and histogram analysis.</a> Radiology. 266, 812-821 (2013).</li><li>Zhang, H. 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=Automated+detection+and+quantification+of+COVID-19+pneumonia:+CT+imaging+analysis+by+a+deep+learning-based+software.">Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software.</a> Nuclear Medicine and Molecular Imaging. 14, 1-8 (2020).</li><li>Alhazzani, 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=Surviving+Sepsis+Campaign:+guidelines+on+the+management+of+critically+ill+adults+with+Coronavirus+Disease+2019(COVID-19).">Surviving Sepsis Campaign: guidelines on the management of critically ill adults with Coronavirus Disease 2019(COVID-19).</a> Intensive Care Medicine. 46 (5), 854-887 (2020).</li></ol>

None of the authors have conflicts of interests.

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 sometimes hard to achieve when studying a disease that compromises breath holding; therefore, researchers can consider excluding compromised HRCTs or defining a segment dedicated to artifacts to be eliminated from further analysis.
It is possible to install an extension to 3D Slicer, called the &#8220;chest imaging platform&#8221;11, which allows faster, more automated operations on lung segments, with particular interest on aerated lung. It was decided not to adopt this method, since it requires extensive manual intervention when GGO and consolidations have a sub-pleural distribution, which is the case of the pathology explored in this paper.
An automatic segmentation method for interstitial lung diseases has been reported12; even so, this method required having previous knowledge of the affected areas&#8217; features. The technique proposed in this study represents an easy to learn and reproducible approach to segmentation of pathological findings of the lung, the features extracted from which could provide future means of automatically segment viral interstitial lung diseases, and represent accurate prognostic factors.
The segmentation method proposed has some limitations.
First of all, downloading the DICOM images from the workstations is a process that might require a variable amount of time, thus doing so for vast numbers of patients might be a troublesome work. Any potential application of this segmentation method to the clinic practice must consider this critical issue, until the point when segmentation plugins become widely available on PACS platforms.
Secondly, the segmentation of the COVID-19 related pathological areas might be complicated in patients with concurrent chronic lung disease (e.g., lung cancer, lung fibrosis, etc.) whose radiological findings consist in areas with the same densities of those typical of COVID-19 patterns. The same concern should be considered in CT scans with respiratory artifacts. These artifacts are quite common in patients with COVID-19 infection, being commonly related to dyspnea and respiratory failure, especially in old/middle aged patients.
Moreover, patients with a severe interstitial pneumonia (characterized by many lung consolidations and crazy paving opacities) require a more extensive manual segmentation and consequently, a vast amount of time. In general, the higher the severity of the interstitial pneumonia, the more extensive the manual segmentation required, the longer the segmentation time.
However, the degree of precision offered by advanced evaluation techniques such as the ones proposed by Lanza and Colombi could add limited information on patients with already severe lung conditions compared to the standard clinical and radiological evaluations20.
Finally, it should be noted that any radiologist who has no experience with 3D slicer needs an adequate training time, as it is not an intuitive software and requires some time to be mastered even in its basilar functions.

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 the correspondence and quickly review the segmentation results. A quantitative assessment of the amount of lung parenchyma affected can be obtained, to replicate the results reported from Colombi et al.6 and Lanza et al.13.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61737/61737fig1.jpg" /> 
Figure 1: Representative results volume and surface analysis. The screenshot from the 3D Slicer interface represents the results obtained from the &#8220;Segment Statistics&#8221; module, which can be used to quantitatively evaluate the volume of the affected lung parenchyma. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61737/61737fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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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14.0K Views.  University of Bologna. 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.

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

The Measurement and Treatment of Suppression in Amblyopia

Amblyopia, a developmental disorder of the visual cortex, is one of the leading causes of visual dysfunction in the working age population. Current estimates put the prevalence of amblyopia at approximately 1-3%1-3, the majority of cases being monocular2. Amblyopia is most frequently caused by ocular misalignment (strabismus), blur induced by unequal refractive error (anisometropia), and in some cases by form deprivation.
Although amblyopia is initially caused by abnormal visual input in infancy, once established, the visual deficit often remains when normal visual input has been restored using surgery and/or refractive correction. This is because amblyopia is the result of abnormal visual cortex development rather than a problem with the amblyopic eye itself4,5 . Amblyopia is characterized by both monocular and binocular deficits6,7 which include impaired visual acuity and poor or absent stereopsis respectively. The visual dysfunction in amblyopia is often associated with a strong suppression of the inputs from the amblyopic eye under binocular viewing conditions8. Recent work has indicated that suppression may play a central role in both the monocular and binocular deficits associated with amblyopia9,10 .
Current clinical tests for suppression tend to verify the presence or absence of suppression rather than giving a quantitative measurement of the degree of suppression. Here we describe a technique for measuring amblyopic suppression with a compact, portable device11,12 . The device consists of a laptop computer connected to a pair of virtual reality goggles. The novelty of the technique lies in the way we present visual stimuli to measure suppression. Stimuli are shown to the amblyopic eye at high contrast while the contrast of the stimuli shown to the non-amblyopic eye are varied. Patients perform a simple signal/noise task that allows for a precise measurement of the strength of excitatory binocular interactions. The contrast offset at which neither eye has a performance advantage is a measure of the "balance point" and is a direct measure of suppression. This technique has been validated psychophysically both in control13,14 and patient6,9,11 populations.
In addition to measuring suppression this technique also forms the basis of a novel form of treatment to decrease suppression over time and improve binocular and often monocular function in adult patients with amblyopia12,15,16 . This new treatment approach can be deployed either on the goggle system described above or on a specially modified iPod touch device15.

Amblyopia is a developmental disorder of the visual cortex that is often accompanied by strong suppression of one eye. We present a new technique for measuring and treating interocular suppression in patients with amblyopia that can be deployed using virtual reality goggles or a portable iPod Touch device.

Amblyopia, a developmental disorder of the visual cortex, is one of the leading causes of visual dysfunction in the working age population. Current ...

Osteopathic Manipulative Treatment as a Useful Adjunctive Tool for Pneumonia

Pneumonia, the inflammatory state of lung tissue primarily due to microbial infection, claimed 52,306 lives in the United States in 20071 and resulted in the hospitalization of 1.1 million patients2. With an average length of in-patient hospital stay of five days2, pneumonia and influenza comprise significant financial burden costing the United States $40.2 billion in 20053. Under the current Infectious Disease Society of America/American Thoracic Society guidelines, standard-of-care recommendations include the rapid administration of an appropriate antibiotic regiment, fluid replacement, and ventilation (if necessary). Non-standard therapies include the use of corticosteroids and statins; however, these therapies lack conclusive supporting evidence4. (Figure 1)

Osteopathic Manipulative Treatment (OMT) is a cost-effective adjunctive treatment of pneumonia that has been shown to reduce patients&#8217; length of hospital stay, duration of intravenous antibiotics, and incidence of respiratory failure or death when compared to subjects who received conventional care alone5. The use of manual manipulation techniques for pneumonia was first recorded as early as the Spanish influenza pandemic of 1918, when patients treated with standard medical care had an estimated mortality rate of 33%, compared to a 10% mortality rate in patients treated by osteopathic physicians6. When applied to the management of pneumonia, manual manipulation techniques bolster lymphatic flow, respiratory function, and immunological defense by targeting anatomical structures involved in the these systems7,8, 9, 10.

The objective of this review video-article is three-fold: a) summarize the findings of randomized controlled studies on the efficacy of OMT in adult patients with diagnosed pneumonia, b) demonstrate established protocols utilized by osteopathic physicians treating pneumonia, c) elucidate the physiological mechanisms behind manual manipulation of the respiratory and lymphatic systems. Specifically, we will discuss and demonstrate four routine techniques that address autonomics, lymph drainage, and rib cage mobility: 1) Rib Raising, 2) Thoracic Pump, 3) Doming of the Thoracic Diaphragm, and 4) Muscle Energy for Rib 1.5,11

Pneumonia is one of the top ten leading causes of death in the U.S. Osteopathic manipulative techniques (OMT) may be utilized as an adjunctive therapy in the treatment of pneumonia to enhance biomechanical and immune function; and ultimately, to reduce hospital stay, duration of antibiotics, incidence of respiratory failure, and mortality. In this video-article, we will review randomized controlled studies on the use of OMT in pneumonia patients, as well as demonstrate specific hands-on techniques that are routinely practiced by osteopathic physicians when treating pulmonary infections.

Pneumonia, the inflammatory state of lung tissue primarily due to microbial infection, claimed 52,306 lives in the United States in 20071 and resulted in ...

Evaluation of Lung Metastasis in Mouse Mammary Tumor Models by Quantitative Real-time PCR

Metastatic disease is the spread of malignant tumor cells from the primary cancer site to a distant organ and is the primary cause of cancer associated death 1. Common sites of metastatic spread include lung, lymph node, brain, and bone 2. Mechanisms that drive metastasis are intense areas of cancer research. Consequently, effective assays to measure metastatic burden in distant sites of metastasis are instrumental for cancer research. Evaluation of lung metastases in mammary tumor models is generally performed by gross qualitative observation of lung tissue following dissection. Quantitative methods of evaluating metastasis are currently limited to ex vivo and in vivo imaging based techniques that require user defined parameters. Many of these techniques are at the whole organism level rather than the cellular level 3-6. Although newer imaging methods utilizing multi-photon microscopy are able to evaluate metastasis at the cellular level 7, these highly elegant procedures are more suited to evaluating mechanisms of dissemination rather than quantitative assessment of metastatic burden. Here, a simple in vitro method to quantitatively assess metastasis is presented. Using quantitative Real-time PCR (QRT-PCR), tumor cell specific mRNA can be detected within the mouse lung tissue.

This protocol describes how to use quantitative Real-time PCR (QRT-PCR) to detect tumor cell specific mRNA representing metastasis within the mouse lung tissue.

Metastatic disease is the spread of malignant tumor cells from the primary cancer site to a distant organ and is the primary cause of cancer associated ...

Apical Resection Mouse Model to Study Early Mammalian Heart Regeneration

Cardiovascular disease plagues the whole world due to intensive lifestyle changes. Heart regeneration holds great promise for repairing and restoring cardiomyocytes lost due to injury and disease. In contrast to the robust cardiac regeneration of certain lower vertebrates, adult mammalian hearts typically show minimal capacity for heart regeneration and repair. However, recent studies have sparked considerable scientific interest with the finding that, between postnatal day 1 to 7 (P1 to P7), the neonatal mouse heart retains significant regenerative potential after apical resection (i.e., surgical amputation and exposure of left ventricular apex). One major controversy over this finding might be due to the diverse surgery-related procedures used in efforts to replicate or expand upon this important finding. These instructions dynamically present the materials and methodology for apical resection in a mouse model. The salient steps of this rodent survival surgery involve hypothermia anesthesia, thoracotomy, surgical amputation of heart ventricular apex, and suture and recovery of mice. The approach described could expand the application of the apical resection mouse model for cardiovascular research.

A step-by-step video protocol of apical resection is demonstrated in this study. Apical resection is a recently highlighted surgical approach in mammalian heart regeneration research. This study may promote the application of apical resection as a standard methodology in research into the mechanism underlying heart regeneration.

Cardiovascular disease plagues the whole world due to intensive lifestyle changes. Heart regeneration holds great promise for repairing and restoring ...

Proximal Cadaveric Femur Preparation for Fracture Strength Testing and Quantitative CT-based Finite Element Analysis

Cadaveric fracture testing is routinely used to understand factors that affect proximal femur strength. Because ex vivo biological tissues are prone to lose their mechanical properties over time, specimen preparation for experimental testing must be performed carefully to obtain reliable results that represent in vivo conditions. For that reason, we designed a protocol and a set of fixtures to prepare the femoral specimens such that their mechanical properties experienced minimal changes. The femora were kept in a frozen state except during preparation steps and mechanical testing. The relevant clinical measures of total hip and femoral neck bone mineral density (BMD) were obtained with a clinical dual X-ray absorptiometry (DXA) bone densitometer, and the 3D geometry and distribution of bone mineral were obtained using CT with a calibration phantom for quantitative estimations based on the greyscale values. Any possible bone disease, fracture, or the presence of implants or artifacts affecting the bone structure, was ruled out with X-ray scans. For preparation, all bones were carefully cleaned of excess soft tissue, and were cut and potted at the internal rotation angle of interest. A cutting fixture allowed the distal end of the bone to be cut off leaving the proximal femur at a desired length. To allow positioning of the femoral neck at prescribed angles during later CT scanning and mechanical testing, the proximal femoral shafts were potted in polymethylmethacrylate (PMMA) using a fixture designed specifically for desired orientations. The data collected from our experiments were then used for validation of quantitative computed tomography (QCT)-based finite element analysis (FEA), as described in a different protocol. In this manuscript, we present the protocol for the precise bone preparation for mechanical testing and subsequent QCT/FEA modeling. The current protocol was successfully applied to prepare about 200 cadaveric femora over a 6-year time period.

We present a robust protocol on how to carefully preserve and prepare cadaveric femora for fracture testing and quantitative computed tomography imaging. The method provides precise control over input conditions for the purpose of determining relationships between bone mineral density, fracture strength, and defining finite element model geometry and properties.

Cadaveric fracture testing is routinely used to understand factors that affect proximal femur strength.  Because ex vivo biological tissues are prone to ...

Induction and Micro-CT Imaging of Cerebral Cavernous Malformations in Mouse Model

Mutations in the CCM1 (aka KRIT1), CCM2, or CCM3 (aka PDCD10) gene cause cerebral cavernous malformation (CCM) in humans. Mouse models of CCM disease have been established by tamoxifen induced deletion of Ccm genes in postnatal animals. These mouse models provide invaluable tools to investigate molecular mechanism and therapeutic approaches for CCM disease. An accurate and quantitative method to assess lesion burden and progression is essential to harness the full value of these animal models. Here, we demonstrate the induction of CCM disease in a mouse model and the use of the contrast enhanced X-ray micro computed tomography (micro-CT) method to measure CCM lesion burden in mouse brains. At postnatal day 1 (P1), we used 4-hydroxytamoxifen (4HT) to activate Cre recombinase activity from the Cdh5-CreErt2 transgene to cleave the floxed allele of Ccm2. CCM lesions in mouse brains were analyzed at P8. For micro-CT, iodine based Lugol&#39;s solution was used to enhance contrast in brain tissue. We have optimized the scan parameters and utilized a voxel dimension of 9.5 &#181;m, which lead to a minimum feature size of approximately 25 &#181;m. This resolution is sufficient to measure CCM lesion volume and number globally and accurately, and provide high-quality 3-D mapping of CCM lesions in mouse brains. This method enhances the value of the established mouse models to study the molecular basis and potential therapies for CCM and other cerebrovascular diseases.

This protocol demonstrates the induction of cerebral cavernous malformation disease in a mouse model and uses contrast enhanced micro computed tomography to measure lesion burden. This method enhances the value of established mouse models to study the molecular basis and potential therapies for cerebral cavernous malformation and other cerebrovascular diseases.

Mutations in the CCM1 (aka KRIT1), CCM2, or CCM3 (aka PDCD10) gene cause cerebral cavernous malformation (CCM) in humans. Mouse models of CCM disease have ...

Quantitative Micro-CT Analysis of Aortopathy in a Mouse Model of &#946;-aminopropionitrile-induced Aortic Aneurysm and Dissection

Aortic aneurysm and dissection is associated with significant morbidity and mortality in the population and can be highly lethal. While animal models of aortic disease exist, in vivo imaging of the vasculature has been limited. In recent years, micro-computerized tomography (micro-CT) has emerged as a preferred modality for imaging both large and small vessels both in vivo and ex vivo. In conjunction with a method of vascular casting, we have successfully used micro-CT to characterize the frequency and distribution of aortic pathology in &#946;-aminopropionitrile-treated C57/Bl6 mice. Technical limitations of this method include variations in the quality of the perfusion introduced by poor animal preparation, the application of proper methodologies for vessel size quantification, and the non-survivability of this procedure. This article details a methodology for the intravascular perfusion of a lead-based radiopaque silicone rubber for the quantitative characterization of aortopathy in a mouse model of aneurysm and dissection. In addition to visualizing aortic pathology, this method may be used for examining other vascular beds in vivo or vascular beds removed post-mortem.

This article describes a detailed methodology of using a radiopaque lead-based silicone rubber to perfuse the murine vasculature for aortic diameter quantification in a mouse model of aortic aneurysm and dissection.

Aortic aneurysm and dissection is associated with significant morbidity and mortality in the population and can be highly lethal. While animal models of ...

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 ...

Customizing a Cryolite Glass Prosthetic Eye

In Germany, Austria, and Switzerland, over 90% of ocularists still manufacture customized prostheses using cryolite glass from Thuringia. The present manuscript demonstrates this long-forgotten technique in detail. This manuscript shows some major advantages of manufacturing prosthetic eyes using cryolite glass in comparison to poly(methyl methacrylate) (PMMA). These advantages include a lighter weight of the prosthesis, higher levels of patient satisfaction, and only one appointment necessary for the customized manufacturing. Potential risk of breakage seems not to be a critical disadvantage for glass prosthetic eye wearers. However, in some patients, manufacturing a well-fitting prosthetic eye is not possible or reasonable due to anophthalmic socket complications such as post nucleation socket syndrome, scarred fornices, or an orbital implant exposure. This article gives ophthalmologists a better insight into ocularistic care in order to improve the essential interprofessional collaboration between ocularists and ophthalmologists.

This manuscript shows each step of customizing a cryolite glass prosthetic eye including some major advantages of the use of cryolite glass for manufacturing an eye prosthesis compared to poly(methyl methacrylate). In addition, this manuscript gives ophthalmologists better insight into the ocularistic care that could improve interprofessional collaboration.

In Germany, Austria, and Switzerland, over 90% of ocularists still manufacture customized prostheses using cryolite glass from Thuringia. The present ...

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.

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 ...