BrainIAC：面向异构MRI模态的交互式三维脑病灶分割与在线自适应

中文摘要 AI 辅助

脑病灶分割是医学图像分析中的一项基础任务，在诊断、治疗规划和纵向疾病监测中发挥着关键作用。然而，现有模型仍难以满足真实临床应用的需求，在部署中会出现数据分布偏移，这些偏移源于扫描仪硬件差异、成像协议（不同的MRI模态组合）以及新出现的病理类型。我们提出BrainIAC（脑病灶交互式自适应持续学习分割），这是一个统一框架，整合了：(i) 一个多模态骨干网络，通过零填充和随机模态丢弃来训练以分割多种类型的脑病灶并处理异构模态集合；(ii) 三维交互式分割，支持边界框和点击提示，在没有提示时保持全自动预测；(iii) 一种在线自适应机制，结合了交互中自适应和交互后自适应，以网络自身的预测作为伪标签进行监督，并由额外的点击中心高斯损失引导。据我们所知，这是首个面向交互式分割的三维在线自适应方法之一，也是首个将异构模态集合处理与在线自适应相结合的方法。在七个脑MRI数据集上的实验表明，所提出的组件提供了互补和协同的益处。该方法持续优于现有方法，并在异构成像模态（包括训练中未见过的模态）以及先前未见的脑病理类型上具有良好的泛化能力。代码和3D Slicer插件将在发表后于该https URL发布。

英文摘要

Brain lesion segmentation is a fundamental task in medical image analysis, playing a critical role in diagnosis, treatment planning, and longitudinal disease monitoring. Yet existing models still struggle to meet the demands of real clinical use, where deployments contain data distribution shifts, arising from differences in scanner hardware, imaging protocol (varying MRI modality sets), and new pathologies. We present BrainIAC (Brain lesion Interactive Adaptive Continuously learning segmentation), a unified framework that integrates (i) a multi-modal backbone network trained to segment multiple types of brain lesions and handle heterogeneous sets of modalities via zero-filling and random modality dropping; (ii) 3D interactive segmentation with bounding-box and click prompts that preserves fully automatic prediction when no prompt is given; and (iii) an online adaptation mechanism combining Mid-Interaction adaptation and Post-Interaction adaptation, supervised by the network's own predictions as pseudo labels and guided by an extra Click-Centered Gaussian loss. To our knowledge, this is one of the first 3D online adaptation methods for interactive segmentation, and the first to combine handling of heterogeneous modality sets with online adaptation. Experiments across seven brain MRI datasets demonstrate that the proposed components provide complementary and synergistic benefits. The method consistently outperforms existing approaches and generalizes well across heterogeneous imaging modalities, including those unseen during training, as well as previously unseen brain pathology types. The code and a 3D Slicer plug-in will be released at https://github.com/WenTXuL/BrainIAC upon publication.

发表机构

• University of Oxford（牛津大学）

机构由 AI 辅助整理，请以论文原文为准。