Two-level training of a 3D U-Net for accurate segmentation of the intra-cochlear anatomy in head CTs with limited ground truth training data

Dongqing Zhang; Rueben Banalagay; Jianing Wang; Yiyuan Zhao; Jack H. Noble; Benoit M. Dawant

doi:10.1117/12.2512529

15 March 2019 Two-level training of a 3D U-Net for accurate segmentation of the intra-cochlear anatomy in head CTs with limited ground truth training data

Dongqing Zhang, Rueben Banalagay, Jianing Wang, Yiyuan Zhao, Jack H. Noble, Benoit M. Dawant

Author Affiliations +

Proceedings Volume 10949, Medical Imaging 2019: Image Processing; 1094907 (2019) https://doi.org/10.1117/12.2512529
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Cochlear implants (CIs) use electrode arrays that are surgically inserted into the cochlea to treat patients with hearing loss. For CI recipients, sound bypasses the natural transduction mechanism and directly stimulates the neural regions, thus creating a sense of hearing. Post-operatively, CIs need to be programmed. Traditionally, this is done by an audiologist who is blind to the positions of the electrodes relative to the cochlea and only relies on the subjective response of the patient. Multiple programming sessions are usually needed, which can take a frustratingly long time. We have developed an imageguided cochlear implant programming (IGCIP) system to facilitate the process. In IGCIP, we segment the intra-cochlear anatomy and localize the electrode arrays in the patient’s head CT image. By utilizing their spatial relationship, we can suggest programming settings that can significantly improve hearing outcomes. To segment the intra-cochlear anatomy, we use an active shape model (ASM)-based method. Though it produces satisfactory results in most cases, sub-optimal segmentation still happens. As an alternative, herein we explore using a deep learning method to perform the segmentation task. Large image sets with accurate ground truth (in our case manual delineation) are typically needed to train a deep learning model for segmentation but such a dataset does not exist for our application. To tackle this problem, we use segmentations generated by the ASM-based method to pre-train the model and fine-tune it on a small image set for which accurate manual delineation is available. Using this method, we achieve better results than the ASM-based method.

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Dongqing Zhang, Rueben Banalagay, Jianing Wang, Yiyuan Zhao, Jack H. Noble, and Benoit M. Dawant "Two-level training of a 3D U-Net for accurate segmentation of the intra-cochlear anatomy in head CTs with limited ground truth training data", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094907 (15 March 2019); https://doi.org/10.1117/12.2512529

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