Multimodal Image Confidence: A Novel Method for Tumor and Organ Boundary Representation.

The indistinct boundaries of tumors and organs at risk in medical images present significant challenges in treatment planning and other tasks in radiation therapy. This study introduces an innovative analytical algorithm called multimodal image confidence (MMC), which leverages the complementary strengths of various multimodal medical images to assign a confidence measure to each voxel within the region of interest (ROI). MMC enables the generation of modality-specific ROI-enhanced images, providing a detailed depiction of both the boundaries and internal features of the ROI. By employing an interpretable mathematical model that propagates voxel confidence based on intervoxel correlations, MMC circumvents the need for model training, distinguishing it from deep learning-based methods. The alogorithm was evaluated qualitatively and quantitatively on 156 nasopharyngeal carcinoma cases and 1251 glioma cases. Qualitative assessments demonstrated MMC's accuracy in delineating lesion boundaries as well as capturing internal tumor characteristics. Quantitative analyses further revealed strong concordance between MMC and manual delineations. This study presents a cutting-edge algorithm for identifying and illustating ROI boundaries using multimodal 3D medical images. The versatility of the proposed method extends to both targets and organs at risk across various anatomic sites and multiple image modalities, enhancing its potential for accurate delineation of critical structures andmany image-related tasks in radiaton therapy and other fields.