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Proceedings Paper

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

Paper Details

Date Published: 10 March 2020
PDF: 8 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113131D (10 March 2020); doi: 10.1117/12.2549390
Show Author Affiliations
Yubo Fan, Vanderbilt Univ. (United States)
Dongqing Zhang, Google LLC (United States)
Jianing Wang, Vanderbilt Univ. (United States)
Jack H. Noble, Vanderbilt Univ. (United States)
Benoit M. Dawant, Vanderbilt Univ. (United States)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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