TY - GEN
T1 - Optic disc and cup segmentation based on enhanced segnet
AU - Wu, Lianyi
AU - Liu, Yiming
AU - Shi, Yelin
AU - Sheng, Bin
AU - Li, Ping
AU - Bi, Lei
AU - Kim, Jinman
PY - 2019/7/1
Y1 - 2019/7/1
N2 - Due to imbalanced distributed and restricted medical resources, reliable analysis for medical images is hard to come by, and it is impractical to only rely on human beings to do all the analysis, which is time-consuming and not economic. Application of computer vision techniques in such fields emerges as the situation requires. In this paper, we use deep learning segmentation algorithm to segment the optic disc and the cup from each other and from the rest of the ophthalmoscopy photographs. For a better performance, we change the loss function and crop as a way of data augmentation. The segmentation results can be used to calculate the cup-to-disc ratio (CDR), which is further used to diagnose glaucoma. Challenges such as over-fitting, biased dataset, and poor generalization of the model exist in front of us. We illustrate our model and associated methods dealing with these challenges.
AB - Due to imbalanced distributed and restricted medical resources, reliable analysis for medical images is hard to come by, and it is impractical to only rely on human beings to do all the analysis, which is time-consuming and not economic. Application of computer vision techniques in such fields emerges as the situation requires. In this paper, we use deep learning segmentation algorithm to segment the optic disc and the cup from each other and from the rest of the ophthalmoscopy photographs. For a better performance, we change the loss function and crop as a way of data augmentation. The segmentation results can be used to calculate the cup-to-disc ratio (CDR), which is further used to diagnose glaucoma. Challenges such as over-fitting, biased dataset, and poor generalization of the model exist in front of us. We illustrate our model and associated methods dealing with these challenges.
KW - Ophthalmoscopy
KW - Optic disc and cup
KW - Segmentation
KW - SegNet
UR - http://www.scopus.com/inward/record.url?scp=85069219509&partnerID=8YFLogxK
U2 - 10.1145/3328756.3328774
DO - 10.1145/3328756.3328774
M3 - Conference article published in proceeding or book
AN - SCOPUS:85069219509
T3 - ACM International Conference Proceeding Series
SP - 33
EP - 36
BT - Proceedings of the 32nd International Conference on Computer Animation and Social Agents, CASA 2019
PB - Association for Computing Machinery
T2 - 32nd International Conference on Computer Animation and Social Agents, CASA 2019
Y2 - 1 July 2019 through 3 July 2019
ER -