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The aim is to enhance the accuracy of colorectal polyp segmentation by employing a SegFormer-based framework for better edge and region detection.

April 26, 2026Scientific ReportsOpen Access

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

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The aim is to enhance the accuracy of colorectal polyp segmentation by employing a SegFormer-based framework for better edge and region detection.
Utilizes a SegFormer-B4 transformer encoder and multi-branch fusion head at different scales.
Employs a composite objective function combining soft dice similarity loss, Lovász–Hinge loss, and edge-aware binary cross-entropy.
Involves pre-processing to remove textual overlays and spurious cues using OCR-inpainting.
On Kvasir-Seg, achieved mean Dice = 0.946 and mean IoU = 0.899.
On CVC-ClinicDB, mean Dice = 0.961 and mean IoU = 0.926.
For ETIS, mean Dice = 0.799 and mean IoU = 0.705, with high boundary-sensitive measures.

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Randomized trial demonstrates superior boundary detection in colorectal polyp segmentation, suggesting improved clinical reliability.

Ouria et al. (2026) studied this question.

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