Towards fully automated inner ear analysis with deep-learning-based joint segmentation and landmark detection framework.

ABSTRACT: Automated analysis of the inner ear anatomy in radiological data instead of time-consuming manual assessment is a worthwhile goal that could facilitate preoperative planning and clinical research. We propose a framework encompassing joint semantic segmentation of the inner ear and anatomical landmark detection of helicotrema, oval and round window. A fully automated pipeline with a single, dual-headed volumetric 3D U-Net was implemented, trained and evaluated using manually labeled in-house datasets from cadaveric specimen ([Formula: see text]) and clinical practice ([Formula: see text]). The model robustness was further evaluated on three independent open-source datasets ([Formula: see text] scans) consisting of cadaveric specimen scans. For the in-house datasets, Dice scores of [Formula:

SUBMITTER: Stebani J 

PROVIDER: S-EPMC10625555 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature