An active learning approach to train a deep learning algorithm for tumor segmentation from brain MR images.

ABSTRACT:

Purpose

This study focuses on assessing the performance of active learning techniques to train a brain MRI glioma segmentation model.

Methods

The publicly available training dataset provided for the 2021 RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge was used in this study, consisting of 1251 multi-institutional, multi-parametric MR images. Post-contrast T1, T2, and T2 FLAIR images as well as ground truth manual segmentation were used as input for the model. The data were split into a training set of 1151 cases and testing set of 100 cases, with the testing set remaining constant throughout. Deep convolutional neural network segmentation models were trained using the NiftyNet platform. To test the viability of active learning in training a segmentation model, an

SUBMITTER: Boehringer AS 

PROVIDER: S-EPMC10449747 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature