Automated Hematoma Detection and Outcome Prediction in Patients With Traumatic Brain Injury.

ABSTRACT:

Purpose

To develop a tool for automated subtype classification and segmentation of intracranial hemorrhages (ICH) on CT scans of patients with traumatic brain injury (TBI). Furthermore, outcome prediction for patients can effectively facilitate patient management.

Methods

This study presents a cascade framework for two-stage segmentation and multi-label classification. The hematoma region of interest (ROI) is localized, and then the ROI is cropped and resized to the original pixel size before being input into the model again to obtain the segmentation results. In multilabel classification, the mask obtained from automatic segmentation is superimposed onto the corresponding ROI and CT slices, respectively, to constitute the input image. Subsequently, the ROI image is employed

SUBMITTER: Xu Y 

PROVIDER: S-EPMC11557439 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature