Deep learning segmentation-based bone removal from computed tomography of the brain improves subdural hematoma detection.

ABSTRACT:

Purpose

Timely identification of intracranial blood products is clinically impactful, however the detection of subdural hematoma (SDH) on non-contrast CT scans of the head (NCCTH) is challenging given interference from the adjacent calvarium. This work explores the utility of a NCCTH bone removal algorithm for improving SDH detection.

Methods

A deep learning segmentation algorithm was designed/trained for bone removal using 100 NCCTH. Segmentation accuracy was evaluated on 15 NCCTH from the same institution and 22 NCCTH from an independent external dataset using quantitative overlap analysis between automated and expert manual segmentations. The impact of bone removal on detecting SDH by junior radiology trainees was evaluated with a reader study comparing detection performa

SUBMITTER: Isikbay M 

PROVIDER: S-EPMC11771325 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature