Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation

• Published in: IEEE transactions on biomedical engineering Vol. 65; no. 9; pp. 1912 - 1923
• Main Authors: Yan, Zengqiang, Yang, Xin, Cheng, Kwang-Ting
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Blood vessels; Databases, Factual; Deep Learning; Diagnostic Techniques, Ophthalmological; Humans; Image Interpretation, Computer-Assisted - methods; Image processing; Image segmentation; Loss measurement; Machine learning; Manuals; Pixels; Retina; retinal image analysis; Retinal images; Retinal vessels; Retinal Vessels - diagnostic imaging; Segment-level loss; Skeleton; Training; vessel segmentation
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2018.2828137

Objective: Deep learning based methods for retinal vessel segmentation are usually trained based on pixel-wise losses, which treat all vessel pixels with equal importance in pixel-to-pixel matching between a predicted probability map and the corresponding manually annotated segmentation. However, due to the highly imbalanced pixel ratio between thick and thin vessels in fundus images, a pixel-wise loss would limit deep learning models to learn features for accurate segmentation of thin vessels, which is an important task for clinical diagnosis of eye-related diseases. Methods: In this paper, we propose a new segment-level loss which emphasizes more on the thickness consistency of thin vessels in the training process. By jointly adopting both the segment-level and the pixel-wise losses, the importance between thick and thin vessels in the loss calculation would be more balanced. As a result, more effective features can be learned for vessel segmentation without increasing the overall model complexity. Results: Experimental results on public data sets demonstrate that the model trained by the joint losses outperforms the current state-of-the-art methods in both separate-training and cross-training evaluations. Conclusion: Compared to the pixel-wise loss, utilizing the proposed joint-loss framework is able to learn more distinguishable features for vessel segmentation. In addition, the segment-level loss can bring consistent performance improvement for both deep and shallow network architectures. Significance: The findings from this study of using joint losses can be applied to other deep learning models for performance improvement without significantly changing the network architectures.