Deep learning-based multiclass segmentation in aneurysmal subarachnoid hemorrhage.

ABSTRACT:

Introduction

Radiological scores used to assess the extent of subarachnoid hemorrhage are limited by intrarater and interrater variability and do not utilize all available information from the imaging. Image segmentation enables precise identification and delineation of objects or regions of interest and offers the potential for automatization of score assessments using precise volumetric information. Our study aims to develop a deep learning model that enables automated multiclass segmentation of structures and pathologies relevant for aneurysmal subarachnoid hemorrhage outcome prediction.

Methods

A set of 73 non-contrast CT scans of patients with aneurysmal subarachnoid hemorrhage were included. Six target classes were manually segmented to create a multiclass segmentation

SUBMITTER: Kiewitz J 

PROVIDER: S-EPMC11671301 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature