Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model
The use of spectral-domain optical coherence tomography (SD-OCT) is becoming commonplace for the in vivo longitudinal study of murine models of ophthalmic disease. Longitudinal studies, however, generate large quantities of data, the manual analysis of which is very challenging due to the time-consu...
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description | The use of spectral-domain optical coherence tomography (SD-OCT) is becoming commonplace for the in vivo longitudinal study of murine models of ophthalmic disease. Longitudinal studies, however, generate large quantities of data, the manual analysis of which is very challenging due to the time-consuming nature of generating delineations. Thus, it is of importance that automated algorithms be developed to facilitate accurate and timely analysis of these large datasets. Furthermore, as the models target a variety of diseases, the associated structural changes can also be extremely disparate. For instance, in the light damage (LD) model, which is frequently used to study photoreceptor degeneration, the outer retina appears dramatically different from the normal retina. To address these concerns, we have developed a flexible graph-based algorithm for the automated segmentation of mouse OCT volumes (ASiMOV). This approach incorporates a machine-learning component that can be easily trained for different disease models. To validate ASiMOV, the automated results were compared to manual delineations obtained from three raters on healthy and BALB/cJ mice post LD. It was also used to study a longitudinal LD model, where five control and five LD mice were imaged at four timepoints post LD. The total retinal thickness and the outer retina (comprising the outer nuclear layer, and inner and outer segments of the photoreceptors) were unchanged the day after the LD, but subsequently thinned significantly (p < 0.01). The retinal nerve fiber-ganglion cell complex and the inner plexiform layers, however, remained unchanged for the duration of the study. |
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Longitudinal studies, however, generate large quantities of data, the manual analysis of which is very challenging due to the time-consuming nature of generating delineations. Thus, it is of importance that automated algorithms be developed to facilitate accurate and timely analysis of these large datasets. Furthermore, as the models target a variety of diseases, the associated structural changes can also be extremely disparate. For instance, in the light damage (LD) model, which is frequently used to study photoreceptor degeneration, the outer retina appears dramatically different from the normal retina. To address these concerns, we have developed a flexible graph-based algorithm for the automated segmentation of mouse OCT volumes (ASiMOV). This approach incorporates a machine-learning component that can be easily trained for different disease models. To validate ASiMOV, the automated results were compared to manual delineations obtained from three raters on healthy and BALB/cJ mice post LD. It was also used to study a longitudinal LD model, where five control and five LD mice were imaged at four timepoints post LD. The total retinal thickness and the outer retina (comprising the outer nuclear layer, and inner and outer segments of the photoreceptors) were unchanged the day after the LD, but subsequently thinned significantly (p < 0.01). The retinal nerve fiber-ganglion cell complex and the inner plexiform layers, however, remained unchanged for the duration of the study.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0181059</identifier><identifier>PMID: 28817571</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Analysis ; Animal models ; Animals ; Apoptosis ; Biology and Life Sciences ; Clinical trials ; Coherence (Optics) ; Computer engineering ; Correlation analysis ; Damage assessment ; Data processing ; Degeneration ; Disease Models, Animal ; Female ; Image processing ; Image segmentation ; Immunohistochemistry ; In vivo methods and tests ; Information management ; Learning algorithms ; Light ; Light - adverse effects ; Longitudinal Studies ; Machine learning ; Medical imaging ; Medicine ; Medicine and Health Sciences ; Mice ; Optical Coherence Tomography ; Optics ; Photoreceptors ; Reproducibility of Results ; Research and Analysis Methods ; Retina ; Retina - diagnostic imaging ; Retina - pathology ; Retina - radiation effects ; Retinal degeneration ; Retinal Diseases - diagnostic imaging ; Retinal Diseases - etiology ; Retinal Diseases - pathology ; Segmentation ; Social Sciences ; Technology application ; Tomography ; Tomography, Optical Coherence - methods</subject><ispartof>PloS one, 2017-08, Vol.12 (8), p.e0181059</ispartof><rights>COPYRIGHT 2017 Public Library of Science</rights><rights>2017 Antony et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2017 Antony et al 2017 Antony et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c593t-bb076ea7638a5a3f0b8aaf47d99065e63afb286a74da937a0867cc50aa4ceeec3</citedby><cites>FETCH-LOGICAL-c593t-bb076ea7638a5a3f0b8aaf47d99065e63afb286a74da937a0867cc50aa4ceeec3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5560565/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5560565/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28817571$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Stieger, Knut</contributor><creatorcontrib>Antony, Bhavna Josephine</creatorcontrib><creatorcontrib>Kim, Byung-Jin</creatorcontrib><creatorcontrib>Lang, Andrew</creatorcontrib><creatorcontrib>Carass, Aaron</creatorcontrib><creatorcontrib>Prince, Jerry L</creatorcontrib><creatorcontrib>Zack, Donald J</creatorcontrib><title>Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>The use of spectral-domain optical coherence tomography (SD-OCT) is becoming commonplace for the in vivo longitudinal study of murine models of ophthalmic disease. Longitudinal studies, however, generate large quantities of data, the manual analysis of which is very challenging due to the time-consuming nature of generating delineations. Thus, it is of importance that automated algorithms be developed to facilitate accurate and timely analysis of these large datasets. Furthermore, as the models target a variety of diseases, the associated structural changes can also be extremely disparate. For instance, in the light damage (LD) model, which is frequently used to study photoreceptor degeneration, the outer retina appears dramatically different from the normal retina. To address these concerns, we have developed a flexible graph-based algorithm for the automated segmentation of mouse OCT volumes (ASiMOV). This approach incorporates a machine-learning component that can be easily trained for different disease models. To validate ASiMOV, the automated results were compared to manual delineations obtained from three raters on healthy and BALB/cJ mice post LD. It was also used to study a longitudinal LD model, where five control and five LD mice were imaged at four timepoints post LD. The total retinal thickness and the outer retina (comprising the outer nuclear layer, and inner and outer segments of the photoreceptors) were unchanged the day after the LD, but subsequently thinned significantly (p < 0.01). The retinal nerve fiber-ganglion cell complex and the inner plexiform layers, however, remained unchanged for the duration of the study.</description><subject>Algorithms</subject><subject>Analysis</subject><subject>Animal models</subject><subject>Animals</subject><subject>Apoptosis</subject><subject>Biology and Life Sciences</subject><subject>Clinical trials</subject><subject>Coherence (Optics)</subject><subject>Computer engineering</subject><subject>Correlation analysis</subject><subject>Damage assessment</subject><subject>Data processing</subject><subject>Degeneration</subject><subject>Disease Models, Animal</subject><subject>Female</subject><subject>Image processing</subject><subject>Image segmentation</subject><subject>Immunohistochemistry</subject><subject>In vivo methods and tests</subject><subject>Information management</subject><subject>Learning algorithms</subject><subject>Light</subject><subject>Light - adverse effects</subject><subject>Longitudinal Studies</subject><subject>Machine learning</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Mice</subject><subject>Optical Coherence Tomography</subject><subject>Optics</subject><subject>Photoreceptors</subject><subject>Reproducibility of Results</subject><subject>Research and Analysis Methods</subject><subject>Retina</subject><subject>Retina - diagnostic imaging</subject><subject>Retina - pathology</subject><subject>Retina - radiation effects</subject><subject>Retinal degeneration</subject><subject>Retinal Diseases - diagnostic imaging</subject><subject>Retinal Diseases - etiology</subject><subject>Retinal Diseases - pathology</subject><subject>Segmentation</subject><subject>Social Sciences</subject><subject>Technology application</subject><subject>Tomography</subject><subject>Tomography, Optical Coherence - methods</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNptUk1r3DAQNaWlSdP-g9IaCiU97HZkWx_uobAs_Qik7KFprmIsjx0F2dpYdiD_vtquE7Il6CAxeu-N5uklyVsGS5ZL9vnaT0OPbrn1PS2BKQa8fJYcszLPFiKD_Pmj81HyKoRrAJ4rIV4mR5lSTHLJjhO7mkbf4Uh1GqjtqB9xtL5PfZN2fgqUbtYX6a13U0chPV39tr82l5--pJfobL1HfkyNs7016NIwTvXdjoqps-3VmNbYYUtRqSb3OnnRoAv0Zt5Pkj_fv12sfy7ONz_O1qvzheFlPi6qCqQglCJXyDFvoFKITSHrsgTBSeTYVJkSKIsay1wiKCGN4YBYGCIy-Unyfq-7dT7o2aWgoxdQFEwVZUSc7RG1x2u9HWyHw532aPW_gh9ajcNojSMNVWVUU5UMMlYAUMVNAxmIjGcVIe60vs7dpqqj2kQDB3QHooc3vb3Srb_VnAvggkeB01lg8DcThVF3NhhyDnuKH7B_t1QKWIR--A_69HQzqsU4gO0bH_uanahecWBSQLQsopZPoOKqqbMmRqqxsX5AKPYEM_gQBmoeZmSgd4G8f4zeBVLPgYy0d4_9eSDdJzD_C0LH3SU</recordid><startdate>20170817</startdate><enddate>20170817</enddate><creator>Antony, Bhavna Josephine</creator><creator>Kim, Byung-Jin</creator><creator>Lang, Andrew</creator><creator>Carass, Aaron</creator><creator>Prince, Jerry L</creator><creator>Zack, Donald J</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20170817</creationdate><title>Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model</title><author>Antony, Bhavna Josephine ; Kim, Byung-Jin ; Lang, Andrew ; Carass, Aaron ; Prince, Jerry L ; Zack, Donald J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c593t-bb076ea7638a5a3f0b8aaf47d99065e63afb286a74da937a0867cc50aa4ceeec3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Algorithms</topic><topic>Analysis</topic><topic>Animal models</topic><topic>Animals</topic><topic>Apoptosis</topic><topic>Biology and Life Sciences</topic><topic>Clinical trials</topic><topic>Coherence (Optics)</topic><topic>Computer engineering</topic><topic>Correlation analysis</topic><topic>Damage assessment</topic><topic>Data processing</topic><topic>Degeneration</topic><topic>Disease Models, Animal</topic><topic>Female</topic><topic>Image processing</topic><topic>Image segmentation</topic><topic>Immunohistochemistry</topic><topic>In vivo methods and tests</topic><topic>Information management</topic><topic>Learning algorithms</topic><topic>Light</topic><topic>Light - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Antony, Bhavna Josephine</au><au>Kim, Byung-Jin</au><au>Lang, Andrew</au><au>Carass, Aaron</au><au>Prince, Jerry L</au><au>Zack, Donald J</au><au>Stieger, Knut</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2017-08-17</date><risdate>2017</risdate><volume>12</volume><issue>8</issue><spage>e0181059</spage><pages>e0181059-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>The use of spectral-domain optical coherence tomography (SD-OCT) is becoming commonplace for the in vivo longitudinal study of murine models of ophthalmic disease. Longitudinal studies, however, generate large quantities of data, the manual analysis of which is very challenging due to the time-consuming nature of generating delineations. Thus, it is of importance that automated algorithms be developed to facilitate accurate and timely analysis of these large datasets. Furthermore, as the models target a variety of diseases, the associated structural changes can also be extremely disparate. For instance, in the light damage (LD) model, which is frequently used to study photoreceptor degeneration, the outer retina appears dramatically different from the normal retina. To address these concerns, we have developed a flexible graph-based algorithm for the automated segmentation of mouse OCT volumes (ASiMOV). This approach incorporates a machine-learning component that can be easily trained for different disease models. To validate ASiMOV, the automated results were compared to manual delineations obtained from three raters on healthy and BALB/cJ mice post LD. It was also used to study a longitudinal LD model, where five control and five LD mice were imaged at four timepoints post LD. The total retinal thickness and the outer retina (comprising the outer nuclear layer, and inner and outer segments of the photoreceptors) were unchanged the day after the LD, but subsequently thinned significantly (p < 0.01). The retinal nerve fiber-ganglion cell complex and the inner plexiform layers, however, remained unchanged for the duration of the study.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>28817571</pmid><doi>10.1371/journal.pone.0181059</doi><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Analysis Animal models Animals Apoptosis Biology and Life Sciences Clinical trials Coherence (Optics) Computer engineering Correlation analysis Damage assessment Data processing Degeneration Disease Models, Animal Female Image processing Image segmentation Immunohistochemistry In vivo methods and tests Information management Learning algorithms Light Light - adverse effects Longitudinal Studies Machine learning Medical imaging Medicine Medicine and Health Sciences Mice Optical Coherence Tomography Optics Photoreceptors Reproducibility of Results Research and Analysis Methods Retina Retina - diagnostic imaging Retina - pathology Retina - radiation effects Retinal degeneration Retinal Diseases - diagnostic imaging Retinal Diseases - etiology Retinal Diseases - pathology Segmentation Social Sciences Technology application Tomography Tomography, Optical Coherence - methods |
title | Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model |
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