Training and validation of a deep learning U-net architecture general model for automated segmentation of inner ear from CT.

ABSTRACT:

Background

The intricate three-dimensional anatomy of the inner ear presents significant challenges in diagnostic procedures and critical surgical interventions. Recent advancements in deep learning (DL), particularly convolutional neural networks (CNN), have shown promise for segmenting specific structures in medical imaging. This study aimed to train and externally validate an open-source U-net DL general model for automated segmentation of the inner ear from computed tomography (CT) scans, using quantitative and qualitative assessments.

Methods

In this multicenter study, we retrospectively collected a dataset of 271 CT scans to train an open-source U-net CNN model. An external set of 70 CT scans was used to evaluate the performance of the trained model. The model's effica

SUBMITTER: Lim J 

PROVIDER: S-EPMC11393264 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature