用时序差异加权块提升多发性硬化症病灶分割精度
Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting
- 设计差异加权模块，显式融合基线与随访扫描的时序变化
- 在两个数据集上实现更高的病灶分割和检测指标
- 适合医学图像分析与神经退行性疾病研究者参考
多发性硬化症（MS）病变在纵向MRI扫描中的精准分割对于监测疾病进展和治疗效果至关重要。尽管临床实践中会考虑时间变化，但大多数现有深度学习方法仍对不同时间点的扫描独立处理。在利用纵向图像的研究中，主流方法仅采用简单的通道拼接，效率较低。本文提出一种新方法，通过独特的结构归纳偏置——差异加权块（Difference Weighting Block），显式建模基线与随访扫描之间的时序差异，融合双时间点特征并突出变化区域。在两个数据集上的实验表明，该方法在病灶分割（Dice分数、豪斯多夫距离）及病灶检测（病灶级F1分数）方面均优于当前最先进的纵向与单时间点模型。代码已公开于www.github.com/MIC-DKFZ/Longitudinal-Difference-Weighting。
原文摘要 · Abstract (English)
Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level $F_1$ score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at www.github.com/MIC-DKFZ/Longitudinal-Difference-Weighting.
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