IterNet: retinal image segmentation utilizing structural redundancy in vessel networks

概要

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$backslashtimes$ 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$backslashsim$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.

論文種別
発表文献
Proceedings - The IEEE Winter Conference on Applications of Computer Vision (WACV)
Liangzhi Li
Liangzhi Li
招へい助教

His research interests lie in deep learning, computer vision, robotics, and medical images.

Manisha Verma
Manisha Verma
特任研究員

Manisha’s research interest broadly lies in computer vision and image processing. Currently, she is working on micro facial expression recognition using multi-model deep learning frameworks.

中島悠太
中島悠太
准教授

コンピュータビジョン・パターン認識などの研究。ディープニューラルネットワークなどを用いた画像・映像の認識・理解を主に、自然言語処理を援用した応用研究などに従事。

長原一
長原一
教授

コンピューテーショナルフォトグラフィ、コンピュータビジョンを専門とし実世界センシングや情報処理技術、画像認識技術の研究を行う。さらに、画像センシングにとどまらず様々なセンサに拡張したコンピュテーショナルセンシング手法の開発や高次元で冗長な実世界ビッグデータから意味のある情報を計測するスパースセンシングへの転換を目指す。