Prostate cancer and benign prostatic hyperplasia lesions segmentation using diffusion kurtosis imaging, T2*, and R2* mapping with U-Net++ algorithm.
Authors
Affiliations (5)
Affiliations (5)
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
- Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran. [email protected].
- Department of Radiology, Askarieh Hospital, Isfahan, Iran.
- Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, Iran.
Abstract
This study aimed to propose a deep learning-based segmentation framework to delineate prostate lesions across multiple MRI acquisitions and derived parametric maps, including apparent diffusion coefficient (ADC) map, diffusion kurtosis imaging (DKI)-derived parameter maps (D map and K map), T2-weighted imaging (T2WI), and T2*-weighted imaging-derived parameter maps (T2* map and R2* map). Then, a comparison was conducted among the model's segmentation performance across MRI-derived images to identify those that provide the most discriminative information for accurate lesion identification. 51 patients underwent multiparametric MRI sequences, which included T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and T2*-weighted images. Three expert radiologists conducted manual lesion annotations. All images were preprocessed, labeled, and augmented before training the U-Net++ model. The segmentation model's performance was evaluated using Dice similarity coefficient, Intersection over Union (IoU), sensitivity, and specificity metrics. The IoU values for the ADC map, D map, K map, T2WI, T2* map, and R2* map were 0.8907, 0.8559, 0.9504, 0.9250, 0.9441, and 0.8781, respectively. The corresponding Dice coefficient scores were 0.9416, 0.9211, 0.9744, 0.9604, 0.9709, and 0.9342. These results indicate a significant degree of overlap between the predicted and ground truth segmentation masks. These findings emphasize the complementary value of combining optimized deep learning architectures with advanced MRI-derived images, which could enhance diagnostic precision and facilitate more informed clinical decision-making.