Deep learning-based segmentation of brain parenchyma and ventricular system in CT scans in the presence of anomalies.

ABSTRACT:

Introduction

The automatic segmentation of brain parenchyma and cerebrospinal fluid-filled spaces such as the ventricular system is the first step for quantitative and qualitative analysis of brain CT data. For clinical practice and especially for diagnostics, it is crucial that such a method is robust to anatomical variability and pathological changes such as (hemorrhagic or neoplastic) lesions and chronic defects. This study investigates the increase in overall robustness of a deep learning algorithm that is gained by adding hemorrhage training data to an otherwise normal training cohort.

Methods

A 2D U-Net is trained on subjects with normal appearing brain anatomy. In a second experiment the training data includes additional subjects with brain hemorrhage on image data of

SUBMITTER: Gerken A 

PROVIDER: S-EPMC10406198 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature