提出首个针对多发性硬化病灶的端到端实例分割模型，解决病灶融合导致的误分割问题。
ConfLUNet: Multiple sclerosis lesion instance segmentation in presence of confluent lesions
- 设计端到端网络ConfLUNet，联合优化病灶检测与边界分割。
- 在13例测试集上，病灶检测F1达67.3%，融合病灶召回率提升12.5%。
- 引入融合病灶单位（CLU）新定义和评估指标，推动临床可解释性
准确的脑部MRI病灶级分割对多发性硬化（MS）的诊断、预后和疾病监测至关重要。然而，现有评估方法主要依赖连通域（CC）后处理的语义分割，因依赖空间连通性，无法区分融合病灶（由多个融合病灶单元，CLU构成）。为此，本文首次正式定义了CLU及相应的CLU感知评估指标，并构建了全面的实例分割评估框架。在该框架下，系统评估了基于连通域（CC）和自动融合分割（ACLS）两种现有方法，发现CC始终低估CLU数量，而ACLS则易过度分割，导致病灶计数虚高且精度下降。为克服上述局限，提出首个针对MS病灶的端到端实例分割框架ConfLUNet，可从单张FLAIR图像中联合优化病灶检测与轮廓划分。在50名患者训练数据上，其在独立测试集（n=13）上的表现显著优于CC与ACLS：全景质量（Panoptic Quality）达42.0%（对比CC 37.5%、ACLS 36.8%；p=0.017/0.005），病灶检测F1为67.3%（对比61.6%/59.9%；p=0.028/0.013）。对于CLU检测，ConfLUNet F1[CLU]达81.5%，召回率较CC提升12.5%（p=0.015），精度高于ACLS 31.2%（p=0.003）。本研究通过严谨定义、新指标、可复现框架与首个专用模型，为多发性硬化病灶实例分割奠定基础。
原文摘要 · Abstract (English)
Accurate lesion-level segmentation on MRI is critical for multiple sclerosis (MS) diagnosis, prognosis, and disease monitoring. However, current evaluation practices largely rely on semantic segmentation post-processed with connected components (CC), which cannot separate confluent lesions (aggregates of confluent lesion units, CLUs) due to reliance on spatial connectivity. To address this misalignment with clinical needs, we introduce formal definitions of CLUs and associated CLU-aware detection metrics, and include them in an exhaustive instance segmentation evaluation framework. Within this framework, we systematically evaluate CC and post-processing-based Automated Confluent Splitting (ACLS), the only existing methods for lesion instance segmentation in MS. Our analysis reveals that CC consistently underestimates CLU counts, while ACLS tends to oversplit lesions, leading to overestimated lesion counts and reduced precision. To overcome these limitations, we propose ConfLUNet, the first end-to-end instance segmentation framework for MS lesions. ConfLUNet jointly optimizes lesion detection and delineation from a single FLAIR image. Trained on 50 patients, ConfLUNet significantly outperforms CC and ACLS on the held-out test set (n=13) in instance segmentation (Panoptic Quality: 42.0% vs. 37.5%/36.8%; p = 0.017/0.005) and lesion detection (F1: 67.3% vs. 61.6%/59.9%; p = 0.028/0.013). For CLU detection, ConfLUNet achieves the highest F1[CLU] (81.5%), improving recall over CC (+12.5%, p = 0.015) and precision over ACLS (+31.2%, p = 0.003). By combining rigorous definitions, new CLU-aware metrics, a reproducible evaluation framework, and the first dedicated end-to-end model, this work lays the foundation for lesion instance segmentation in MS.
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