TY - GEN
T1 - Deep classification and segmentation model for vessel extraction in retinal images
AU - Wu, Yicheng
AU - Xia, Yong
AU - Zhang, Yanning
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - The shape of retinal blood vessels is critical in the early diagnosis of diabetes and diabetic retinopathy. Segmentation of retinal vessels, particularly the capillaries, remains a significant challenge. To address this challenge, in this paper, we adopt the “divide-and-conque” strategy, and thus propose a deep neural network-based classification and segmentation (CAS) model to extract blood vessels in color retinal images. We first use the network in network (NIN) to divide the retinal patches extracted from preprocessed fundus retinal images into wide-vessel, middle-vessel and capillary patches. Then we train three U-Nets to segment three classes of vessels, respectively. Finally, this algorithm has been evaluated on the digital retinal images for vessel extraction (DRIVE) database against seven existing algorithms and achieved the highest AUC of 97.93% and top three accuracy, sensitivity and specificity. Our comparison results indicate that the proposed algorithm is able to segment blood vessels in retinal images with better performance.
AB - The shape of retinal blood vessels is critical in the early diagnosis of diabetes and diabetic retinopathy. Segmentation of retinal vessels, particularly the capillaries, remains a significant challenge. To address this challenge, in this paper, we adopt the “divide-and-conque” strategy, and thus propose a deep neural network-based classification and segmentation (CAS) model to extract blood vessels in color retinal images. We first use the network in network (NIN) to divide the retinal patches extracted from preprocessed fundus retinal images into wide-vessel, middle-vessel and capillary patches. Then we train three U-Nets to segment three classes of vessels, respectively. Finally, this algorithm has been evaluated on the digital retinal images for vessel extraction (DRIVE) database against seven existing algorithms and achieved the highest AUC of 97.93% and top three accuracy, sensitivity and specificity. Our comparison results indicate that the proposed algorithm is able to segment blood vessels in retinal images with better performance.
KW - Classification and segmentation
KW - Deep learning
KW - Retinal vessels segmentation
UR - http://www.scopus.com/inward/record.url?scp=85057205957&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-03335-4_22
DO - 10.1007/978-3-030-03335-4_22
M3 - 会议稿件
AN - SCOPUS:85057205957
SN - 9783030033347
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 250
EP - 258
BT - Pattern Recognition and Computer Vision - First Chinese Conference, PRCV 2018, Proceedings
A2 - Liu, Cheng-Lin
A2 - Tan, Tieniu
A2 - Zhou, Jie
A2 - Lai, Jian-Huang
A2 - Chen, Xilin
A2 - Zheng, Nanning
A2 - Zha, Hongbin
PB - Springer Verlag
T2 - 1st Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018
Y2 - 23 November 2018 through 26 November 2018
ER -
