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Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation
Abstract
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.
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Metadata
- Authors
- Yan, Z., Yang, X., & Cheng, K.-T.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2018-09
- Volume
- 65
- Issue
- 9
- Pages
- 1912-1923
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2802388893
- Cited by
- 428
- References
- 41
- FWCI
- 27.40
- Type
- journal article
- Language
- en
- Open access
- closed
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