Multi-Scale deep learning framework for cochlea localization, segmentation and analysis on clinical ultra-high-resolution CT images | Zendy

Floris Heutink | Zendy; Valentin Koch | Zendy; Berit M. Verbist | Zendy; Willem-Jan van der Woude | Zendy; Emmanuel A. M. Mylanus | Zendy; Wendy J. Huinck | Zendy; Ioannis Sechopoulos | Zendy; Marco Caballo | Zendy

Open Access

Multi-Scale deep learning framework for cochlea localization, segmentation and analysis on clinical ultra-high-resolution CT images

Author(s) -

Floris Heutink,

Valentin Koch,

Berit M. Verbist,

Willem-Jan van der Woude,

Emmanuel A. M. Mylanus,

Wendy J. Huinck,

Ioannis Sechopoulos,

Marco Caballo

Publication year - 2020

Publication title -

computer methods and programs in biomedicine

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.924

H-Index - 102

eISSN - 1872-7565

pISSN - 0169-2607

DOI - 10.1016/j.cmpb.2020.105387

Subject(s) - segmentation , cochlea , artificial intelligence , computer science , ground truth , pixel , convolutional neural network , pattern recognition (psychology) , computer vision , medicine , anatomy

The proposed algorithm could successfully segment and analyze the cochlea on UHR-CT images, resulting in accurate measurements of cochlear anatomy. Given the wide variation in cochlear size found in our patient cohort, it may find application as a pre-operative tool in cochlear implant surgery, potentially helping elaborate personalized treatment strategies based on patient-specific, image-based anatomical measurements.

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