SAM-OCTA2: Layer Sequence OCTA Segmentation with Fine-tuned Segment Anything Model 2
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912028671606784 |
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| author | Chen, Xinrun Wang, Chengliang Ning, Haojian Zhang, Mengzhan Shen, Mei Li, Shiying |
| author_facet | Chen, Xinrun Wang, Chengliang Ning, Haojian Zhang, Mengzhan Shen, Mei Li, Shiying |
| contents | Segmentation of indicated targets aids in the precise analysis of optical coherence tomography angiography (OCTA) samples. Existing segmentation methods typically perform on 2D projection targets, making it challenging to capture the variance of segmented objects through the 3D volume. To address this limitation, the low-rank adaptation technique is adopted to fine-tune the Segment Anything Model (SAM) version 2, enabling the tracking and segmentation of specified objects across the OCTA scanning layer sequence. To further this work, a prompt point generation strategy in frame sequence and a sparse annotation method to acquire retinal vessel (RV) layer masks are proposed. This method is named SAM-OCTA2 and has been experimented on the OCTA-500 dataset. It achieves state-of-the-art performance in segmenting the foveal avascular zone (FAZ) on regular 2D en-face and effectively tracks local vessels across scanning layer sequences. The code is available at: https://github.com/ShellRedia/SAM-OCTA2. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09286 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SAM-OCTA2: Layer Sequence OCTA Segmentation with Fine-tuned Segment Anything Model 2 Chen, Xinrun Wang, Chengliang Ning, Haojian Zhang, Mengzhan Shen, Mei Li, Shiying Computer Vision and Pattern Recognition Segmentation of indicated targets aids in the precise analysis of optical coherence tomography angiography (OCTA) samples. Existing segmentation methods typically perform on 2D projection targets, making it challenging to capture the variance of segmented objects through the 3D volume. To address this limitation, the low-rank adaptation technique is adopted to fine-tune the Segment Anything Model (SAM) version 2, enabling the tracking and segmentation of specified objects across the OCTA scanning layer sequence. To further this work, a prompt point generation strategy in frame sequence and a sparse annotation method to acquire retinal vessel (RV) layer masks are proposed. This method is named SAM-OCTA2 and has been experimented on the OCTA-500 dataset. It achieves state-of-the-art performance in segmenting the foveal avascular zone (FAZ) on regular 2D en-face and effectively tracks local vessels across scanning layer sequences. The code is available at: https://github.com/ShellRedia/SAM-OCTA2. |
| title | SAM-OCTA2: Layer Sequence OCTA Segmentation with Fine-tuned Segment Anything Model 2 |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.09286 |