Deep-learning-assisted detection and segmentation of rib fractures from CT scans: Development and validation of FracNet.

ABSTRACT:

Background

Diagnosis of rib fractures plays an important role in identifying trauma severity. However, quickly and precisely identifying the rib fractures in a large number of CT images with increasing number of patients is a tough task, which is also subject to the qualification of radiologist. We aim at a clinically applicable automatic system for rib fracture detection and segmentation from CT scans.

Methods

A total of 7,473 annotated traumatic rib fractures from 900 patients in a single center were enrolled into our dataset, named RibFrac Dataset, which were annotated with a human-in-the-loop labeling procedure. We developed a deep learning model, named FracNet, to detect and segment rib fractures. 720, 60 and 120 patients were randomly split as training cohort, tuning c

SUBMITTER: Jin L 

PROVIDER: S-EPMC7670192 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature