Large Vessel Segmentation and Microvasculature Quantification Based on Dual-Stream Learning in Optic Disc OCTA Images
Quantification of optic disc microvasculature is crucial for diagnosing various ocular diseases. However, accurate quantification of the microvasculature requires the exclusion of large vessels, such as the central artery and vein, when present. To address the challenge of ineffective learning of ed...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Photonics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-6732/12/6/588 |
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| Summary: | Quantification of optic disc microvasculature is crucial for diagnosing various ocular diseases. However, accurate quantification of the microvasculature requires the exclusion of large vessels, such as the central artery and vein, when present. To address the challenge of ineffective learning of edge information, which arises from the adhesion and transposition of large vessels in the optic disc, we developed a segmentation model that generates high-quality edge information in optic disc slices. By integrating dual-stream learning with channel-spatial attention and multi-level attention mechanisms, our model effectively learns both the target’s primary structure and fine details. Compared to state-of-the-art methods, our proposed approach demonstrates superior performance in segmentation accuracy. Superior results were obtained when the model was tested on OCTA images of the optic disc from 10 clinical patients. This underscores the significant contribution of our method in achieving clearly defined multi-task learning while substantially enhancing inference speed. |
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| ISSN: | 2304-6732 |