# Prostate MR image segmentation using a multi-stage network approach

## TL;DR

This paper presents a multi-stage deep learning approach to improve prostate cancer detection using MR images, enhancing diagnostic accuracy.

## Contribution

The study introduces a multi-stage segmentation framework using deep learning to improve prostate boundary delineation in MR images.

## Key findings

- The MultiResUNet model in a multi-stage framework significantly improved prostate boundary delineation.
- The end-to-end two-stage method outperformed other segmentation strategies in diagnostic accuracy.
- The approach was tested on a large dataset of over 61,000 T2-weighted MR images from 1151 patients.

## Abstract

Prostate cancer (PCa) remains one of the most prevalent cancers among men, with over 1.4 million new cases and 375,304 deaths reported globally in 2020. Current diagnostic approaches, such as prostate-specific antigen (PSA) testing and trans-rectal ultrasound (TRUS)-guided biopsies, are often Limited by low specificity and accuracy. This study addresses these Limitations by leveraging deep learning-based image segmentation techniques on a dataset comprising 61,119 T2-weighted MR images from 1151 patients to enhance PCa detection and characterisation. A multi-stage segmentation approach, including one-stage, sequential two-stage, and end-to-end two-stage methods, was evaluated using various deep learning architectures. The MultiResUNet model, integrated into a multi-stage segmentation framework, demonstrated significant improvements in delineating prostate boundaries. The study utilised a dataset of over 61,000 T2-weighted magnetic resonance (MR) images from more than 1100 patients, employing three distinct segmentation strategies: one-stage, sequential two-stage, and end-to-end two-stage methods. The end-to-end approach, leveraging shared feature representations, consistently outperformed other methods, underscoring its effectiveness in enhancing diagnostic accuracy. These findings highlight the potential of advanced deep learning architectures in streamlining prostate cancer detection and treatment planning. Future work will focus on further optimisation of the models and assessing their generalisability to diverse medical imaging contexts.