A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images
This paper presents a new supervised method for vessel segmentation in retinal images. This method remolds the task of segmentation as a problem of cross-modality data transformation from retinal image to vessel map. A wide and deep neural network with strong induction ability is proposed to model t...
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| Veröffentlicht in: | IEEE transactions on medical imaging 2016-01, Vol.35 (1), p.109-118 |
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| container_title | IEEE transactions on medical imaging |
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| creator | Li, Qiaoliang Feng, Bowei Xie, LinPei Liang, Ping Zhang, Huisheng Wang, Tianfu |
| description | This paper presents a new supervised method for vessel segmentation in retinal images. This method remolds the task of segmentation as a problem of cross-modality data transformation from retinal image to vessel map. A wide and deep neural network with strong induction ability is proposed to model the transformation, and an efficient training strategy is presented. Instead of a single label of the center pixel, the network can output the label map of all pixels for a given image patch. Our approach outperforms reported state-of-the-art methods in terms of sensitivity, specificity and accuracy. The result of cross-training evaluation indicates its robustness to the training set. The approach needs no artificially designed feature and no preprocessing step, reducing the impact of subjective factors. The proposed method has the potential for application in image diagnosis of ophthalmologic diseases, and it may provide a new, general, high-performance computing framework for image segmentation. |
| doi_str_mv | 10.1109/TMI.2015.2457891 |
| format | Article |
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This method remolds the task of segmentation as a problem of cross-modality data transformation from retinal image to vessel map. A wide and deep neural network with strong induction ability is proposed to model the transformation, and an efficient training strategy is presented. Instead of a single label of the center pixel, the network can output the label map of all pixels for a given image patch. Our approach outperforms reported state-of-the-art methods in terms of sensitivity, specificity and accuracy. The result of cross-training evaluation indicates its robustness to the training set. The approach needs no artificially designed feature and no preprocessing step, reducing the impact of subjective factors. The proposed method has the potential for application in image diagnosis of ophthalmologic diseases, and it may provide a new, general, high-performance computing framework for image segmentation.</description><identifier>ISSN: 0278-0062</identifier><identifier>EISSN: 1558-254X</identifier><identifier>DOI: 10.1109/TMI.2015.2457891</identifier><identifier>PMID: 26208306</identifier><identifier>CODEN: ITMID4</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Accuracy ; Algorithms ; Cross-modality learning ; Databases, Factual ; deep learning ; Deformable models ; Feature extraction ; Humans ; Image Processing, Computer-Assisted - methods ; Image segmentation ; Machine Learning ; Neural networks ; Retina ; retinal image ; Retinal Vessels - anatomy & histology ; Training ; vessel segmentation</subject><ispartof>IEEE transactions on medical imaging, 2016-01, Vol.35 (1), p.109-118</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2016</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c460t-617d3e21e115eb963463e4515007427b035aa3594e9d56b68c7fa31ff99fcea3</citedby><cites>FETCH-LOGICAL-c460t-617d3e21e115eb963463e4515007427b035aa3594e9d56b68c7fa31ff99fcea3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7161344$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7161344$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26208306$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Qiaoliang</creatorcontrib><creatorcontrib>Feng, Bowei</creatorcontrib><creatorcontrib>Xie, LinPei</creatorcontrib><creatorcontrib>Liang, Ping</creatorcontrib><creatorcontrib>Zhang, Huisheng</creatorcontrib><creatorcontrib>Wang, Tianfu</creatorcontrib><title>A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images</title><title>IEEE transactions on medical imaging</title><addtitle>TMI</addtitle><addtitle>IEEE Trans Med Imaging</addtitle><description>This paper presents a new supervised method for vessel segmentation in retinal images. This method remolds the task of segmentation as a problem of cross-modality data transformation from retinal image to vessel map. A wide and deep neural network with strong induction ability is proposed to model the transformation, and an efficient training strategy is presented. Instead of a single label of the center pixel, the network can output the label map of all pixels for a given image patch. Our approach outperforms reported state-of-the-art methods in terms of sensitivity, specificity and accuracy. The result of cross-training evaluation indicates its robustness to the training set. The approach needs no artificially designed feature and no preprocessing step, reducing the impact of subjective factors. The proposed method has the potential for application in image diagnosis of ophthalmologic diseases, and it may provide a new, general, high-performance computing framework for image segmentation.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Cross-modality learning</subject><subject>Databases, Factual</subject><subject>deep learning</subject><subject>Deformable models</subject><subject>Feature extraction</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Image segmentation</subject><subject>Machine Learning</subject><subject>Neural networks</subject><subject>Retina</subject><subject>retinal image</subject><subject>Retinal Vessels - anatomy & histology</subject><subject>Training</subject><subject>vessel segmentation</subject><issn>0278-0062</issn><issn>1558-254X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNpdkEtLw1AQhS-i2FrdC4JccOMmdeY-k2UpPgotghZxF27TSU3Jo-Ymi_57U1u7cHUW853D8DF2jTBEhOhhPpsMBaAeCqVtGOEJ66PWYSC0-jxlfRA2DACM6LEL79cAqDRE56wnjIBQgumz6YiP68r7YFYtXZ41Wz4lV5dZueKjzaauXPLF06rmH-Q95fydVgWVjWuyquRZyd-oyUqX80nhVuQv2Vnqck9Xhxyw-dPjfPwSTF-fJ-PRNEiUgSYwaJeSBBKipkVkpDKSlEYNYJWwC5DaOakjRdFSm4UJE5s6iWkaRWlCTg7Y_X62---7Jd_EReYTynNXUtX6GK1WWihUtkPv_qHrqq27j38pKUHvYsBgTyU7FTWl8abOCldvY4R4JzruRMc70fFBdFe5PQy3i4KWx8Kf2Q642QMZER3PFg1KpeQPmPR_Zw</recordid><startdate>201601</startdate><enddate>201601</enddate><creator>Li, Qiaoliang</creator><creator>Feng, Bowei</creator><creator>Xie, LinPei</creator><creator>Liang, Ping</creator><creator>Zhang, Huisheng</creator><creator>Wang, Tianfu</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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This method remolds the task of segmentation as a problem of cross-modality data transformation from retinal image to vessel map. A wide and deep neural network with strong induction ability is proposed to model the transformation, and an efficient training strategy is presented. Instead of a single label of the center pixel, the network can output the label map of all pixels for a given image patch. Our approach outperforms reported state-of-the-art methods in terms of sensitivity, specificity and accuracy. The result of cross-training evaluation indicates its robustness to the training set. The approach needs no artificially designed feature and no preprocessing step, reducing the impact of subjective factors. The proposed method has the potential for application in image diagnosis of ophthalmologic diseases, and it may provide a new, general, high-performance computing framework for image segmentation.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>26208306</pmid><doi>10.1109/TMI.2015.2457891</doi><tpages>10</tpages></addata></record> |
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| subjects | Accuracy Algorithms Cross-modality learning Databases, Factual deep learning Deformable models Feature extraction Humans Image Processing, Computer-Assisted - methods Image segmentation Machine Learning Neural networks Retina retinal image Retinal Vessels - anatomy & histology Training vessel segmentation |
| title | A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images |
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