Background: Apparent Diffusion Coefficient (ADC) values and Total Diffusion Volume (TDV) from Whole-body diffusion-weighted MRI (WB-DWI) are recognized cancer imaging biomarkers. However, manual disease delineation for ADC and TDV measurements is unfeasible in clinical practice, demanding automation. As a first step, we propose an algorithm to generate fast and reproducible probability maps of the skeleton, adjacent internal organs (liver, spleen, urinary bladder, and kidneys), and spinal canal. Methods: We developed an automated deep-learning pipeline based on a 3D patch-based Residual U-Net architecture that localizes and delineates these anatomical structures on WB-DWI. The algorithm was trained using "soft-labels" (non-binary segmentations) derived from a computationally intensive atlas-based approach. For training and validation, we employed a multi-center WB-DWI dataset comprising 532 scans from patients with Advanced Prostate Cancer (APC) or Multiple Myeloma (MM), with testing on 45 patients. Results: Our weakly-supervised deep learning model achieved an average dice score/precision/recall of 0.66/0.6/0.73 for skeletal delineations, 0.8/0.79/0.81 for internal organs, and 0.85/0.79/0.94 for spinal canal, with surface distances consistently below 3 mm. Relative median ADC and log-transformed volume differences between automated and manual expert-defined full-body delineations were below 10% and 4%, respectively. The computational time for generating probability maps was 12x faster than the atlas-based registration algorithm (25 s vs. 5 min). An experienced radiologist rated the model's accuracy "good" or "excellent" on test datasets. Conclusion: Our model offers fast and reproducible probability maps for localizing and delineating body regions on WB-DWI, enabling ADC and TDV quantification, potentially supporting clinicians in disease staging and treatment response assessment.

翻译：背景：全身扩散加权磁共振成像（WB-DWI）的表观扩散系数（ADC）值与总扩散体积（TDV）是公认的癌症影像学生物标志物。然而，临床实践中无法通过手动勾画病灶进行ADC与TDV测量，亟需自动化解决方案。作为第一步，我们提出一种算法，用于生成骨骼、邻近内脏（肝脏、脾脏、膀胱及肾脏）和椎管的快速可重复概率图。方法：我们开发了一种基于三维分块残差U-Net架构的自动化深度学习流程，可在WB-DWI上定位并勾画这些解剖结构。该算法使用通过计算密集型基于图谱方法生成的“软标签”（非二值分割）进行训练。训练与验证采用包含532例晚期前列腺癌（APC）或多发性骨髓瘤（MM）患者扫描的多中心WB-DWI数据集，并在45例患者数据上进行测试。结果：我们的弱监督深度学习模型在骨骼勾画上达到平均Dice分数/精确率/召回率为0.66/0.6/0.73，内脏为0.8/0.79/0.81，椎管为0.85/0.79/0.94，表面距离始终低于3毫米。自动化与专家手动全身勾画之间的相对中位ADC差异及对数转换体积差异分别低于10%和4%。生成概率图的计算时间较基于图谱的配准算法快12倍（25秒对比5分钟）。经验丰富的放射科医师在测试数据集上评定模型准确度为“良好”或“优秀”。结论：本模型能为WB-DWI上的身体区域定位与勾画提供快速可重复的概率图，支持ADC与TDV量化，有望辅助临床医生进行疾病分期与治疗反应评估。