利用增强前后MRI差异特征，提升脑瘤与乳腺病灶分割精度
Clinical Inspired MRI Lesion Segmentation
- 通过动态加权融合增强前后的MRI特征，自适应学习病变差异
- 在BraTS2023和自建乳腺MRI数据集上达到当前最优分割性能
- 方法源于临床阅片习惯，适合医学影像自动分割场景
磁共振成像（MRI）是检测多种疾病病灶的有力工具。不同序列因对比机制与敏感性差异，给准确一致的病灶分割带来挑战。临床上，放射科医生常通过对比增强前（pre）与增强后（post）T1加权序列的差异来定位病灶。受此启发，我们提出一种残差融合方法，用于学习MRI病灶的子序列表征。具体而言，我们在多分辨率下迭代、自适应地融合pre与post序列特征，采用动态权重实现最优融合，以应对多样化的病灶强化模式。该方法在BraTS2023脑肿瘤分割数据集及自建乳腺MRI数据集上均取得当前最优表现。方法具有临床启发性，具备在各类应用中辅助病灶分割的潜力。
原文摘要 · Abstract (English)
Magnetic resonance imaging (MRI) is a potent diagnostic tool for detecting pathological tissues in various diseases. Different MRI sequences have different contrast mechanisms and sensitivities for different types of lesions, which pose challenges to accurate and consistent lesion segmentation. In clinical practice, radiologists commonly use the sub-sequence feature, i.e. the difference between post contrast-enhanced T1-weighted (post) and pre-contrast-enhanced (pre) sequences, to locate lesions. Inspired by this, we propose a residual fusion method to learn subsequence representation for MRI lesion segmentation. Specifically, we iteratively and adaptively fuse features from pre- and post-contrast sequences at multiple resolutions, using dynamic weights to achieve optimal fusion and address diverse lesion enhancement patterns. Our method achieves state-of-the-art performances on BraTS2023 dataset for brain tumor segmentation and our in-house breast MRI dataset for breast lesion segmentation. Our method is clinically inspired and has the potential to facilitate lesion segmentation in various applications.
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