基于样本的分割模型，实现全身磁共振中神经纤维瘤的高效精准交互式分割。
MOIS-SAM2: Exemplar-based Segment Anything Model 2 for multilesion interactive segmentation of neurofibromas in whole-body MRI
- 引入样本驱动语义传播，提升多病灶交互分割精度与可扩展性。
- 在域内测试集上达到0.60的扫描级DSC，优于基线模型30%以上。
- 对扫描仪差异、低肿瘤负荷等场景保持稳定，适合临床部署。
神经纤维瘤病1型是一种遗传性疾病，表现为全身多发神经纤维瘤（NFs）。全身MRI（WB-MRI）是检测和长期监测肿瘤生长的临床标准。现有交互式分割方法难以同时兼顾高病灶级精度与对数百个病灶的可扩展性。本研究提出一种新型交互式分割模型——MOIS-SAM2，专为此挑战设计。该模型在84名患者共119例WB-MRI扫描数据上训练与评估，采用T2加权脂肪抑制序列，按患者级别划分为训练集及四个测试集（一个域内，三个反映不同域偏移场景，如磁场强度变化、低肿瘤负荷、不同临床中心与扫描仪厂商）。结果表明，在域内测试集上，MOIS-SAM2的扫描级DSC达0.60，显著优于基线3D nnU-Net（DSC: 0.54）和SAM2（DSC: 0.35）。模型在磁场强度变化（DSC: 0.53）和扫描仪厂商差异（DSC: 0.50）下性能保持稳定，并在低肿瘤负荷情况下表现更优（DSC: 0.61）。各测试集的病灶检测F1得分在0.62至0.78之间。初步读者间变异分析显示，模型与专家一致性（DSC: 0.62–0.68）接近专家间一致性（DSC: 0.57–0.69）。结论：所提MOIS-SAM2可在极少用户输入下实现高效、可扩展的神经纤维瘤交互式分割，具备良好泛化能力，适用于临床工作流程集成。
原文摘要 · Abstract (English)
Background and Objectives: Neurofibromatosis type 1 is a genetic disorder characterized by the development of numerous neurofibromas (NFs) throughout the body. Whole-body MRI (WB-MRI) is the clinical standard for detection and longitudinal surveillance of NF tumor growth. Existing interactive segmentation methods fail to combine high lesion-wise precision with scalability to hundreds of lesions. This study proposes a novel interactive segmentation model tailored to this challenge. Methods: We introduce MOIS-SAM2, a multi-object interactive segmentation model that extends the state-of-the-art, transformer-based, promptable Segment Anything Model 2 (SAM2) with exemplar-based semantic propagation. MOIS-SAM2 was trained and evaluated on 119 WB-MRI scans from 84 NF1 patients acquired using T2-weighted fat-suppressed sequences. The dataset was split at the patient level into a training set and four test sets (one in-domain and three reflecting different domain shift scenarios, e.g., MRI field strength variation, low tumor burden, differences in clinical site and scanner vendor). Results: On the in-domain test set, MOIS-SAM2 achieved a scan-wise DSC of 0.60 against expert manual annotations, outperforming baseline 3D nnU-Net (DSC: 0.54) and SAM2 (DSC: 0.35). Performance of the proposed model was maintained under MRI field strength shift (DSC: 0.53) and scanner vendor variation (DSC: 0.50), and improved in low tumor burden cases (DSC: 0.61). Lesion detection F1 scores ranged from 0.62 to 0.78 across test sets. Preliminary inter-reader variability analysis showed model-to-expert agreement (DSC: 0.62-0.68), comparable to inter-expert agreement (DSC: 0.57-0.69). Conclusions: The proposed MOIS-SAM2 enables efficient and scalable interactive segmentation of NFs in WB-MRI with minimal user input and strong generalization, supporting integration into clinical workflows.
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