Neurofibromatosis Type 1 is a genetic disorder characterized by the development of neurofibromas (NFs), which exhibit significant variability in size, morphology, and anatomical location. Accurate and automated segmentation of these tumors in whole-body magnetic resonance imaging (WB-MRI) is crucial to assess tumor burden and monitor disease progression. In this study, we present and analyze a fully automated pipeline for NF segmentation in fat-suppressed T2-weighted WB-MRI, consisting of three stages: anatomy segmentation, NF segmentation, and tumor candidate classification. In the first stage, we use the MRSegmentator model to generate an anatomy segmentation mask, extended with a high-risk zone for NFs. This mask is concatenated with the input image as anatomical context information for NF segmentation. The second stage employs an ensemble of 3D anisotropic anatomy-informed U-Nets to produce an NF segmentation confidence mask. In the final stage, tumor candidates are extracted from the confidence mask and classified based on radiomic features, distinguishing tumors from non-tumor regions and reducing false positives. We evaluate the proposed pipeline on three test sets representing different conditions: in-domain data (test set 1), varying imaging protocols and field strength (test set 2), and low tumor burden cases (test set 3). Experimental results show a 68% improvement in per-scan Dice Similarity Coefficient (DSC), a 21% increase in per-tumor DSC, and a two-fold improvement in F1 score for tumor detection in high tumor burden cases by integrating anatomy information. The method is integrated into the 3D Slicer platform for practical clinical use, with the code publicly accessible.

翻译：1型神经纤维瘤病是一种遗传性疾病，其特征是神经纤维瘤（NFs）的生长，这些肿瘤在大小、形态和解剖位置上表现出显著的变异性。在全身体磁共振成像（WB-MRI）中准确、自动地分割这些肿瘤对于评估肿瘤负荷和监测疾病进展至关重要。在本研究中，我们提出并分析了一个用于脂肪抑制T2加权WB-MRI中NF分割的全自动流程，该流程包含三个阶段：解剖结构分割、NF分割和肿瘤候选区域分类。在第一阶段，我们使用MRSegmentator模型生成解剖结构分割掩码，并扩展了NF的高风险区域。该掩码与输入图像拼接，作为NF分割的解剖学上下文信息。第二阶段采用一组3D各向异性解剖信息引导的U-Net集成模型，生成NF分割置信度掩码。在最后阶段，从置信度掩码中提取肿瘤候选区域，并基于影像组学特征进行分类，以区分肿瘤与非肿瘤区域，从而减少假阳性。我们在代表不同条件的三个测试集上评估了所提出的流程：域内数据（测试集1）、不同成像协议和场强（测试集2）以及低肿瘤负荷病例（测试集3）。实验结果表明，通过整合解剖信息，在高肿瘤负荷病例中，每扫描Dice相似系数（DSC）提高了68%，每肿瘤DSC提高了21%，肿瘤检测的F1分数提高了两倍。该方法已集成到3D Slicer平台中，以供实际临床使用，代码已公开可访问。