FocusSDF: Boundary-Aware Learning for Medical Image Segmentation via Signed Distance Supervision
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917081090359296 |
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| author | Shafique, Muzammal Rahim, Nasir Ahmad, Jamil Siadat, Mohammad Malik, Khalid Malik, Ghaus |
| author_facet | Shafique, Muzammal Rahim, Nasir Ahmad, Jamil Siadat, Mohammad Malik, Khalid Malik, Ghaus |
| contents | Segmentation of medical images constitutes an essential component of medical image analysis, providing the foundation for precise diagnosis and efficient therapeutic interventions in clinical practices. Despite substantial progress, most segmentation models do not explicitly encode boundary information; as a result, making boundary preservation a persistent challenge in medical image segmentation. To address this challenge, we introduce FocusSDF, a novel loss function based on the signed distance functions (SDFs), which redirects the network to concentrate on boundary regions by adaptively assigning higher weights to pixels closer to the lesion or organ boundary, effectively making it boundary aware. To rigorously validate FocusSDF, we perform extensive evaluations against five state-of-the-art medical image segmentation models, including the foundation model MedSAM, using four distance-based loss functions across diverse datasets covering cerebral aneurysm, stroke, liver, and breast tumor segmentation tasks spanning multiple imaging modalities. The experimental results consistently demonstrate the superior performance of FocusSDF over existing distance transform based loss functions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_11864 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FocusSDF: Boundary-Aware Learning for Medical Image Segmentation via Signed Distance Supervision Shafique, Muzammal Rahim, Nasir Ahmad, Jamil Siadat, Mohammad Malik, Khalid Malik, Ghaus Computer Vision and Pattern Recognition Segmentation of medical images constitutes an essential component of medical image analysis, providing the foundation for precise diagnosis and efficient therapeutic interventions in clinical practices. Despite substantial progress, most segmentation models do not explicitly encode boundary information; as a result, making boundary preservation a persistent challenge in medical image segmentation. To address this challenge, we introduce FocusSDF, a novel loss function based on the signed distance functions (SDFs), which redirects the network to concentrate on boundary regions by adaptively assigning higher weights to pixels closer to the lesion or organ boundary, effectively making it boundary aware. To rigorously validate FocusSDF, we perform extensive evaluations against five state-of-the-art medical image segmentation models, including the foundation model MedSAM, using four distance-based loss functions across diverse datasets covering cerebral aneurysm, stroke, liver, and breast tumor segmentation tasks spanning multiple imaging modalities. The experimental results consistently demonstrate the superior performance of FocusSDF over existing distance transform based loss functions. |
| title | FocusSDF: Boundary-Aware Learning for Medical Image Segmentation via Signed Distance Supervision |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.11864 |