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<p>Accurate definition of colorectal polyps for the prevention of cancer is challenging due to high variability in appearance and indefinite boundaries. In this work, we present a SegFormer-aided framework comprising a transformer encoder (SegFormer-B4) and a multi-branch fusion head in low-, high-, and all-level scales. Both tasks (edges and segmentation) are predicted by each branch and task-specific, learnable softmax weights then fuse branch logits. The framework is optimized under a composite objective comprising (i) deeply supervised soft dice similarity coefficient (DSC) loss for region overlap, (ii) Lovász–Hinge loss on the fusion head for compatibility for intersection over union (IoU) loss, and (iii) an edge-aware binary cross-entropy (BCE) term supervised by a Laplacian-based thin contour map. A lightweight pre-processing step removes textual overlays (OCR-inpainting-guided and specular highlights attenuation) for the removal of spurious cues. On three public datasets, the approach reaches excellent region and boundary precision: on Kvasir-Seg, mDice=0.946 and mIoU=0.899; on CVC-ClinicDB, mDice=0.961 and mIoU=0.926; and for ETIS (small low-contrast lesions), mDice=0.799 and mIoU=0.705, accompanied by top boundary-sensitive measures (S-measure, weighted <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(F_\beta\)</EquationSource> </InlineEquation>, and E-measure). The system is end-to-end, post-processing and any external prompting-free, and generalizes across datasets of varying difficulty, improving regional precision and boundary definition–two qualities desirable for clinical reliability.</p>

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SegFormer-based boundary-aware polyp segmentation with adaptive multi-branch fusion

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月24日

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  • Mahdi Ouria,
  • Akbar Asgharzadeh-Bonab,
  • Hashem Kalbkhani,
  • Vahid Rezaei

摘要

Accurate definition of colorectal polyps for the prevention of cancer is challenging due to high variability in appearance and indefinite boundaries. In this work, we present a SegFormer-aided framework comprising a transformer encoder (SegFormer-B4) and a multi-branch fusion head in low-, high-, and all-level scales. Both tasks (edges and segmentation) are predicted by each branch and task-specific, learnable softmax weights then fuse branch logits. The framework is optimized under a composite objective comprising (i) deeply supervised soft dice similarity coefficient (DSC) loss for region overlap, (ii) Lovász–Hinge loss on the fusion head for compatibility for intersection over union (IoU) loss, and (iii) an edge-aware binary cross-entropy (BCE) term supervised by a Laplacian-based thin contour map. A lightweight pre-processing step removes textual overlays (OCR-inpainting-guided and specular highlights attenuation) for the removal of spurious cues. On three public datasets, the approach reaches excellent region and boundary precision: on Kvasir-Seg, mDice=0.946 and mIoU=0.899; on CVC-ClinicDB, mDice=0.961 and mIoU=0.926; and for ETIS (small low-contrast lesions), mDice=0.799 and mIoU=0.705, accompanied by top boundary-sensitive measures (S-measure, weighted \(F_\beta\) , and E-measure). The system is end-to-end, post-processing and any external prompting-free, and generalizes across datasets of varying difficulty, improving regional precision and boundary definition–two qualities desirable for clinical reliability.