Combining model- and deep-learning-based methods for the accurate and robust segmentation of the intra-cochlear anatomy in clinical head CT images

Yubo Fan; Dongqing Zhang; Jianing Wang; Jack H. Noble; Benoit M. Dawant

doi:10.1117/12.2549390

10 March 2020 Combining model- and deep-learning-based methods for the accurate and robust segmentation of the intra-cochlear anatomy in clinical head CT images

Yubo Fan, Dongqing Zhang, Jianing Wang, Jack H. Noble, Benoit M. Dawant

Author Affiliations +

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 113131D (2020) https://doi.org/10.1117/12.2549390
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Cochlear implants (CIs) are neuroprosthetic devices that can improve hearing in patients with severe-to-profound hearing loss. Postoperatively, a CI device needs to be programmed by an audiologist to determine parameter settings that lead to the best outcomes. Recently, our group has developed an image-guided cochlear implant programming (IGCIP) system to simplify the traditionally tedious post-programming procedure and improve hearing outcomes. IGCIP requires image processing techniques to analyze the location of inserted electrode arrays (EAs) with respect to the intra-cochlear anatomy (ICA), and robust and accurate segmentation methods for the ICA are a critical step in the process. We have proposed active shape model (ASM)-based method and deep learning (DL)-based method for this task, and we have observed that DL methods tend to be more accurate than ASM methods while ASM methods tend to be more robust. In this work, we propose a U-Net-like architecture that incorporates ASM segmentation into the network so that it can refine the provided ASM segmentation based on the CT intensity image. Results we have obtained show that the proposed method can achieve the same segmentation accuracy as that of the DL-based method and the same robustness as that of the ASM-based method.

Conference Presentation

Citation Download Citation

Yubo Fan, Dongqing Zhang, Jianing Wang, Jack H. Noble, and Benoit M. Dawant "Combining model- and deep-learning-based methods for the accurate and robust segmentation of the intra-cochlear anatomy in clinical head CT images", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113131D (10 March 2020); https://doi.org/10.1117/12.2549390

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