Modality-Projection Universal Model for Comprehensive Full-Body Medical Imaging Segmentation

TL;DR

This paper introduces MPUM, a universal deep learning model for full-body medical imaging segmentation that adapts to various modalities, improves accuracy, and enhances interpretability through a novel modality-projection strategy.

Contribution

The study presents a novel modality-projection approach enabling a universal model to effectively handle multiple medical imaging modalities with improved accuracy and interpretability.

Findings

Superior accuracy in anatomical segmentation across modalities

Effective identification of metabolic brain-body correlations

Enhanced model interpretability via saliency map visualization

Abstract

The integration of deep learning in medical imaging has shown great promise for enhancing diagnostic, therapeutic, and research outcomes. However, applying universal models across multiple modalities remains challenging due to the inherent variability in data characteristics. This study aims to introduce and evaluate a Modality Projection Universal Model (MPUM). MPUM employs a novel modality-projection strategy, which allows the model to dynamically adjust its parameters to optimize performance across different imaging modalities. The MPUM demonstrated superior accuracy in identifying anatomical structures, enabling precise quantification for improved clinical decision-making. It also identifies metabolic associations within the brain-body axis, advancing research on brain-body physiological correlations. Furthermore, MPUM's unique controller-based convolution layer enables visualization of saliency maps across all network layers, significantly enhancing the model's interpretability.