Clinical imaging is routinely used for cochlear implant surgical planning yet lacks the resolution and contrast necessary to visualize the fine intracochlear structures critical for individualized intervention. To address this limitation, an ensemble deep learning model was developed to automatically segment cochlear micro-anatomy from standard clinical scans. The model was trained and validated using an independent internal dataset comprised of paired synchrotron and clinical scans of the same cochlea across various acquisition protocols. Performance was evaluated quantitatively on an unseen internal test dataset and a multi-institutional external test dataset. The deep learning model achieved accurate segmentation of intracochlear anatomy across all tested modalities, outperformed all previously published models, and demonstrated strong viability on the multi-institutional external dataset. Furthermore, anatomical measurements on the automatic segmentations closely matched those obtained from high-resolution ground truth segmentations, confirming reliable estimation of clinically relevant metrics. By bridging the gap between high-resolution imaging and routine clinical imaging, this work provides a practical solution for patient-specific cochlear implant surgical planning and postoperative assessment, advancing the goals of atraumatic insertions and more effective hearing restoration.
@article{arxiv.2603.24476,
title = {Robust synchrotron-based deep learning algorithm for intracochlear segmentation in clinical scans: development and international validation},
author = {Ashley Micuda and Daniel Newsted and Nastaran Shakourifar and Sachin Pandey and Asma Alahmadi and Kevin D. Brown and Abdulrahman Hagr and Jacob B. Hunter and Joachim Müller and Kristen Rak and Hanif M. Ladak and Sumit K. Agrawal},
journal= {arXiv preprint arXiv:2603.24476},
year = {2026}
}