BruNet：用于瘀伤分割的跨域迁移框架

中文摘要 AI 辅助

在医学影像中，由于数据有限、边界模糊以及外观高度多变，瘀伤分割是一项具有挑战性的任务。在本研究中，我们提出了BruNet，一个分割框架，它结合了基于ViT的视觉编码器（自监督的DINOv3或预训练的LingBot-Vision骨干网络）与基于SAM的掩码解码器。BruNet在HAM10000皮肤病变数据集上训练，并在无需额外微调的情况下，在独立的瘀伤数据集上进行评估。尽管少数先前研究已探索了机器学习和计算机视觉用于瘀伤分析，但现有工作主要集中于检测、分类或颜色分析，而非像素级定位。据我们所知，这是首个针对自动瘀伤分割的研究。我们的结果表明，BruNet优于基于CNN的模型、最先进的分割模型、ChatGPT-4o/5辅助的SAM2零样本基线以及面向医学的MedSAM模型，展示了其对瘀伤分割的强大跨域泛化能力。

英文摘要

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.

发表机构

• Cardiff University（卡迪夫大学）

机构由 AI 辅助整理，请以论文原文为准。