AI-Based 3D Brain Tumor Segmentation and Tissue Quantification Using Multi-Modal MRI
TL;DR
An AI-based 3D brain tumor segmentation system based on a 3D U-Net architecture to segment brain tumors based on multi-classes with multi-modal MRI volumetric data to enhance interpretability and practical usability is introduced.
Abstract
Segmentation of brain tumors in Magnetic Resonance Imaging (MRI) has many applications in the diagnosis, treatment planning, and monitoring of the disease. The manual delineation of tumor sub-regions is time-consuming and can be influenced by inter-observer variability, requiring automated solutions. This study introduces an AI-based 3D brain tumor segmentation system based on a 3D U-Net architecture to segment brain tumors based on multi-classes with multi-modal MRI volumetric data. Four MRI modalities are processed, and the tumor regions are divided into edema, necrotic core, and enhancing tumor in the proposed model. To address class imbalance and enhance segmentation accuracy, a loss function based on the Dice coefficient is used for training. Besides segmentation, the system consists of quantitative tissue analysis via calculation of the relative distribution of tumor sub-regions and interactive visualization using a web-based dashboard. The experiments demonstrated successful volumetric segmentation and significant tissue quantification. The proposed framework emphasizes a system-level integration of segmentation, quantification, and visualization to enhance interpretability and practical usability.