Enhanced Prostate Tumor Segmentation in MRI Using Hybrid Optimization and Adaptive Thresholding

Accurate segmentation of prostate tumors in magnetic resonance imaging (MRI) is critical for improving diagnostic accuracy and supporting clinical decision making. However, many existing approaches rely on supervised learning methods that require large annotated datasets and substantial computational resources, limiting their clinical applicability. This study aims to develop and evaluate a fully unsupervised framework for prostate tumor segmentation in multiparametric MRI using hybrid optimization and adaptive thresholding techniques.