Journal Article

A deep learning framework for the localization of landmarks on the lateral semi circular canals

Copson, Bridget, Wijewickrema, Sudanthi, Wei, Zhixuan, Gerard, Jean-Marc, O’Leary, Stephen

2026

Overview

This paper introduces a Deep Learning (DL) framework to localize landmark coordinates within the semicircular canals in Computed Tomography (CT) scans of the temporal bone. These landmarks can be consistently defined across patients and imaging modalities and as such can serve as a means of forming a common coordinate system. We propose a DL based framework for automating the landmark selection process. We establish the accuracy of the methods using Bone Beam CT scans of the temporal bone of 20 patients and landmarks selected by 3 human experts as the ground truth. We show that the error rates are similar to the levels of variation in landmark selection achieved by human experts. We further validated the method on CT scans from 14 additional patients, demonstrating that the accuracy remains within clinically acceptable parameters.

• Publisher: Public Library of Science,PLOS,Public Library of Science (PLoS)
• Subject: Anatomic Landmarks - diagnostic imaging / Artificial intelligence / Biology and Life Sciences / Bone imaging / Canals / Computed tomography / Computer and Information Sciences / Coordinate systems / Coordinates / CT imaging / Data collection / Datasets / Deep Learning / Evaluation / Ground truth / Humans / Image Processing, Computer-Assisted - methods / Localization / Machine learning / Medical imaging / Medical imaging equipment / Medicine and Health Sciences / Neural networks / Research and Analysis Methods / Semicircular canals / Semicircular Canals - anatomy & histology / Semicircular Canals - diagnostic imaging / Surgeons / Surgery / Technology application / Temporal bone / Temporal Bone - diagnostic imaging / Tomography, X-Ray Computed - methods