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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Published on: April 13, 2013

DABS-MS: deep atlas-based segmentation using the Mumford-Shah functional.

Hannah G Mason¹, Jack H Noble^1,2

¹Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States.

Journal of Medical Imaging (Bellingham, Wash.)

|October 23, 2025

Summary

This summary is machine-generated.

A new deep learning method, DABS-MS, accurately segments the internal auditory canal (IAC) to help locate auditory nerve fibers (ANFs). This improves cochlear implant (CI) programming for patients with hearing loss.

Keywords:

Mumford–Shah functional VoxelMorph atlas-based registration cochlear implants deformation nonrigid deformation segmentation

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Area of Science:

Medical Imaging
Deep Learning
Computational Neuroscience

Background:

Cochlear implants (CIs) are crucial for treating severe-to-profound hearing loss.
Accurate patient-specific modeling of CI stimulation requires precise localization of auditory nerve fibers (ANFs).
ANF localization is challenging due to their small size and invisibility in clinical imaging.

Purpose of the Study:

To develop a method for accurately inferring ANF positions by segmenting the internal auditory canal (IAC).
To leverage the high contrast of the IAC in CT scans for improved ANF localization.
To enhance cochlear implant programming through patient-specific ANF modeling.

Main Methods:

Proposed a deep atlas-based segmentation network, DABS-MS, inspired by VoxelMorph.
Utilized a single atlas with pre-localized IAC and ANFs.
Implemented a self-supervised training scheme with a Mumford-Shah functional-inspired loss function.
Generated deformation fields (DFs) for accurate IAC segmentation and subsequent ANF localization.

Main Results:

DABS-MS demonstrated superior performance in IAC segmentation compared to VoxelMorph.
The method showed significant improvements in segmentation accuracy on public datasets for trachea and kidney segmentation.
Results indicate the generalizability of the DABS-MS approach.

Conclusions:

The DABS-MS method accurately segments the IAC, facilitating ANF localization.
Improved ANF localization supports patient-specific modeling of CI stimulation.
This advancement can lead to enhanced CI programming and better outcomes for patients with hearing loss.