IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks

Liangzhi Li, Manisha Verma, Yuta Nakashima, Hajime Nagahara, Ryo Kawasaki

2019-12-12Retinal Vessel Segmentation Segmentation Semantic Segmentation Image Segmentation

Abstract

Retinal vessel segmentation is of great interest for diagnosis of retinal vascular diseases. To further improve the performance of vessel segmentation, we propose IterNet, a new model based on UNet, with the ability to find obscured details of the vessel from the segmented vessel image itself, rather than the raw input image. IterNet consists of multiple iterations of a mini-UNet, which can be 4$\times$ deeper than the common UNet. IterNet also adopts the weight-sharing and skip-connection features to facilitate training; therefore, even with such a large architecture, IterNet can still learn from merely 10$\sim$20 labeled images, without pre-training or any prior knowledge. IterNet achieves AUCs of 0.9816, 0.9851, and 0.9881 on three mainstream datasets, namely DRIVE, CHASE-DB1, and STARE, respectively, which currently are the best scores in the literature. The source code is available.

Results

Task	Dataset	Metric	Value	Model
Medical Image Segmentation	CHASE_DB1	AUC	0.9851	IterNet
Medical Image Segmentation	CHASE_DB1	F1 score	0.8073	IterNet
Medical Image Segmentation	DRIVE	AUC	0.9816	IterNet
Medical Image Segmentation	DRIVE	F1 score	0.8205	IterNet
Retinal Vessel Segmentation	CHASE_DB1	AUC	0.9851	IterNet
Retinal Vessel Segmentation	CHASE_DB1	F1 score	0.8073	IterNet
Retinal Vessel Segmentation	DRIVE	AUC	0.9816	IterNet
Retinal Vessel Segmentation	DRIVE	F1 score	0.8205	IterNet

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