Segmentation of prostate and prostate zones using deep learning : A multi-MRI vendor analysis.

ABSTRACT:

Purpose

Develop a deep-learning-based segmentation algorithm for prostate and its peripheral zone (PZ) that is reliable across multiple MRI vendors.

Methods

This is a retrospective study. The dataset consisted of 550 MRIs (Siemens-330, General Electric[GE]-220). A multistream 3D convolutional neural network is used for automatic segmentation of the prostate and its PZ using T2-weighted (T2-w) MRI. Prostate and PZ were manually contoured on axial T2‑w. The network uses axial, coronal, and sagittal T2‑w series as input. The preprocessing of the input data includes bias correction, resampling, and image normalization. A dataset from two MRI vendors (Siemens and GE) is used to test the proposed network. Six different models were trained, three for the prostate and three for the

SUBMITTER: Zavala-Romero O 

PROVIDER: S-EPMC8418872 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature