2.5D and 3D segmentation of brain metastases with deep learning on multinational MRI data.

ABSTRACT:

Introduction

Management of patients with brain metastases is often based on manual lesion detection and segmentation by an expert reader. This is a time- and labor-intensive process, and to that end, this work proposes an end-to-end deep learning segmentation network for a varying number of available MRI available sequences.

Methods

We adapt and evaluate a 2.5D and a 3D convolution neural network trained and tested on a retrospective multinational study from two independent centers, in addition, nnU-Net was adapted as a comparative benchmark. Segmentation and detection performance was evaluated by: (1) the dice similarity coefficient, (2) a per-metastases and the average detection sensitivity, and (3) the number of false positives.

Results

The 2.5D and 3D models achie

SUBMITTER: Ottesen JA 

PROVIDER: S-EPMC9889663 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature