BruNet: A Cross-Domain Transfer Framework for Bruise Segmentation

2026-09-10

AI Summary

本文提出BruNet框架，通过结合视觉编码器和掩码解码器解决医学图像中因数据有限、边界模糊等问题导致的瘀伤分割难题。

Abstract

Segmenting bruises is a challenging task in medical imaging due to limited data and annotations, diffuse boundaries, and highly variable appearance. In this work, we propose BruNet, a segmentation framework that combines a ViT-based visual encoder (a self-supervised DINOv3 or a pretrained LingBot-Vision backbone) with a SAM-based mask decoder. BruNet is trained on the HAM10000 skin lesion dataset and evaluated on a separate bruise dataset without additional fine-tuning. Although a small number of prior studies have explored machine learning and computer vision for bruise analysis, existing work has primarily focused on detection, classification, or colour analysis rather than pixel-level localisation. To the best of our knowledge, this is the first study to address automatic bruise segmentation. Our results show that BruNet outperforms CNN-based models, state-of-the-art segmentation models, ChatGPT-4o/5-assisted SAM2 zero-shot baselines, and the medical-oriented MedSAM model, demonstrating strong cross-domain generalisation to bruise segmentation.

Problem

• bruise segmentation
• medical imaging
• limited data
• diffuse boundaries

Innovation

• BruNet
• Cross-Domain Generalisation
• Automatic Bruise Segmentation
• ViT-based Visual Encoder
• SAM-based Mask Decoder