YoloCurvSeg: You only label one noisy skeleton for vessel-style curvilinear structure segmentation

Lin，Li1,2,3; Peng，Linkai1; He，Huaqing1,3; Cheng，Pujin1,3; Wu，Jiewei1; Wong，Kenneth K.Y.2; Tang，Xiaoying1,3

2023-12-01

10.1016/j.media.2023.102937

Medical Image Analysis   Impact Factor & Quartile

1361-8415

1361-8423

Weakly-supervised learning (WSL) has been proposed to alleviate the conflict between data annotation cost and model performance through employing sparsely-grained (i.e., point-, box-, scribble-wise) supervision and has shown promising performance, particularly in the image segmentation field. However, it is still a very challenging task due to the limited supervision, especially when only a small number of labeled samples are available. Additionally, almost all existing WSL segmentation methods are designed for star-convex structures which are very different from curvilinear structures such as vessels and nerves. In this paper, we propose a novel sparsely annotated segmentation framework for curvilinear structures, named YoloCurvSeg. A very essential component of YoloCurvSeg is image synthesis. Specifically, a background generator delivers image backgrounds that closely match the real distributions through inpainting dilated skeletons. The extracted backgrounds are then combined with randomly emulated curves generated by a Space Colonization Algorithm-based foreground generator and through a multilayer patch-wise contrastive learning synthesizer. In this way, a synthetic dataset with both images and curve segmentation labels is obtained, at the cost of only one or a few noisy skeleton annotations. Finally, a segmenter is trained with the generated dataset and possibly an unlabeled dataset. The proposed YoloCurvSeg is evaluated on four publicly available datasets (OCTA500, CORN, DRIVE and CHASEDB1) and the results show that YoloCurvSeg outperforms state-of-the-art WSL segmentation methods by large margins. With only one noisy skeleton annotation (respectively 0.14%, 0.03%, 1.40%, and 0.65% of the full annotation), YoloCurvSeg achieves more than 97% of the fully-supervised performance on each dataset. Code and datasets will be released at https://github.com/llmir/YoloCurvSeg.

Curvilinear structure segmentation Medical image synthesis One-shot Sparse annotation Weakly-supervised learning

SCI

English

First ; Corresponding

National Natural Science Foundation of China[62071210];

Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging

Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging

WOS:001073424000001

ELSEVIER

COMPUTER SCIENCE

2-s2.0-85169976722

Scopus

1.Department of Electronic and Electrical Engineering,Southern University of Science and Technology,Shenzhen,China
2.Department of Electrical and Electronic Engineering,University of Hong Kong,Hong Kong
3.Jiaxing Research Institute,Southern University of Science and Technology,Jiaxing,China

Lin，Li,Peng，Linkai,He，Huaqing,et al. YoloCurvSeg: You only label one noisy skeleton for vessel-style curvilinear structure segmentation[J]. Medical Image Analysis,2023,90.

Lin，Li.,Peng，Linkai.,He，Huaqing.,Cheng，Pujin.,Wu，Jiewei.,...&Tang，Xiaoying.(2023).YoloCurvSeg: You only label one noisy skeleton for vessel-style curvilinear structure segmentation.Medical Image Analysis,90.

Lin，Li,et al."YoloCurvSeg: You only label one noisy skeleton for vessel-style curvilinear structure segmentation".Medical Image Analysis 90(2023).