Optic Disc and Cup Image Segmentation Utilizing Contour-Based Transformation and Sequence Labeling Networks.
Optic disc (OD) and optic cup (OC) segmentation are important steps for automatic screening and diagnosing of optic nerve head abnormalities such as glaucoma. Many recent works formulated the OD and OC segmentation as a pixel classification task. However, it is hard for these methods to explicitly m...
| Publicado en: | Journal of Medical Systems Vol. 44; no. 5; pp. 1 - 14 |
|---|---|
| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
May2020
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143018940&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143018940 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: May2020 vid: 44 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143018940 143018940 143018940 10.1007/s10916-020-01561-2 143018940 ppf: 1 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Optic Disc and Cup Image Segmentation Utilizing Contour-Based Transformation and Sequence Labeling Networks. aug: au: Xie, Zhe Ling, Tonghui Yang, Yuanyuan Shu, Rong Liu, Brent J. affil: Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, 200083, Shanghai, China sug: subj: Optic Nerve Glaucoma Diagnosis Image Processing, Computer Assisted Natural Language Processing Neural Networks (Computer) Human Algorithms Image Enhancement Methods ROC Curve Models, Statistical Software Weights and Measures Health Screening ab: Optic disc (OD) and optic cup (OC) segmentation are important steps for automatic screening and diagnosing of optic nerve head abnormalities such as glaucoma. Many recent works formulated the OD and OC segmentation as a pixel classification task. However, it is hard for these methods to explicitly model the spatial relations between the labels in the output mask. Furthermore, the proportion of the background, OD and OC are unbalanced which also may result in a biased model as well as introduce more noise. To address these problems, we developed an approach that follows a coarse-to-fine segmentation process. We start with a U-Net to obtain a rough segmenting boundary and then crop the area around the boundary to form a boundary contour centered image. Second, inspired by sequence labeling tasks in natural language processing, we regard the OD and OC segmentation as a sequence labeling task and propose a novel fully convolutional network called SU-Net and combine it with the Viterbi algorithm to jointly decode the segmentation boundary. We also introduced a geometric parameter-based data augmentation method to generate more training samples in order to minimize the differences between training and test sets and reduce overfitting. Experimental results show that our method achieved state-of-the-art results on 2 datasets for both OD and OC segmentation and our method outperforms most of the ophthalmologists in terms of achieving agreement out of 6 ophthalmologists on the MESSIDOR dataset for both OD and OC segmentation. In terms of glaucoma screening, we achieved the best cup-to-disc ratio (CDR) error and area under the ROC curve (AUC) for glaucoma classification on the Drishti-GS dataset. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|