Multiple Myeloma Lesion Segmentation on Whole-Body Diffusion-Weighted Imaging via Efficient Anatomical Anticipation and Multimodal Confirmation

2026-09-05

AI Summary

本文针对多发性骨髓瘤病灶在全身扩散加权成像上自动分割的难题，提出了一种结合高效解剖预测和多模态确认的两阶段框架。

Abstract

Whole-body diffusion-weighted imaging (WB-DWI) is widely used for multiple myeloma (MM) assessment, yet automated lesion segmentation remains challenging due to limited anatomical delineation and the low specificity of marrow hyperintensity. Existing studies have introduced bone region-of-interest (ROI) information and apparent diffusion coefficient (ADC) maps to mitigate these ambiguities, but practical limitations remain. Bone ROI construction often relies on costly manual annotation, image registration, or dedicated bone models, while ADC is usually incorporated only through simple channel fusion, limiting its ability to provide complementary structural and lesion-discriminative cues. To address these limitations, we propose a two-stage framework for MM lesion segmentation on WB-DWI. In the first stage, we train a bone ROI generation model from ADC images without dedicated bone labels, providing an efficient and practical anatomical prior for lesion analysis. In the second stage, we propose Anatomy-guided Multimodal U-Net (AMU-Net), which leverages ADC in a manner consistent with clinical lesion assessment rather than treating it as a generic auxiliary modality. Extensive experiments demonstrate the effectiveness and practicality of the proposed method. It achieves the best overall performance among the evaluated methods, with a mean Dice score of 76.2%.

Problem

• Multiple Myeloma
• Whole-Body Diffusion-Weighted Imaging
• Lesion Segmentation
• Anatomical Delineation
• Apparent Diffusion Coefficient

Innovation

• Anatomy-guided Multimodal U-Net
• Bone ROI generation model
• Whole-body diffusion-weighted imaging
• Multiple myeloma lesion segmentation