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Related Concept Videos

Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Anatomy of the Ear

Auditory sensation, commonly called hearing, involves the transformation of sonic waves into neural impulses facilitated by the structures of the auditory organ. The prominent, flesh-like structure on the side of the head, called the auricle, directs sound waves towards the auditory canal. The auricle is often mislabeled as the pinna, a term more aligned with mobile structures like a feline's external ear. The auditory canal penetrates the cranium via the external auditory meatus of the...
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Hair cells are the sensory receptors of the auditory system—they transduce mechanical sound waves into electrical energy that the nervous system can understand. Hair cells are located in the organ of Corti within the cochlea of the inner ear, between the basilar and tectorial membranes. The actual sensory receptors are called inner hair cells. The outer hair cells serve other functions, such as sound amplification in the cochlea, and are not discussed in detail here.

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Related Experiment Video

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Whole Neonatal Cochlear Explants as an In vitro Model
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Synchrotron-Based Deep Learning Network of the Inner Ear: Development and Expert Validation.

Ashley Micuda1, Kyle Rioux2, Luke Helpard3,4

  • 1Department of Medical Biophysics, Western University, London, Ontario, Canada.

The Laryngoscope
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Summary

A new deep learning network accurately segments the inner ear in CT scans, outperforming human experts. This automated approach sets a new clinical standard for inner ear imaging analysis.

Keywords:
STAPLEdeep learninginner earsegmentationsynchrotron imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Anatomy

Background:

  • Accurate segmentation of the inner ear is crucial for diagnosing and treating various otological conditions.
  • Manual segmentation of the inner ear on computed tomography (CT) scans is time-consuming and subject to inter-observer variability.

Purpose of the Study:

  • To develop and validate a deep learning (DL) network for automated inner ear segmentation in clinical CT scans.
  • To compare the DL network's performance against domain experts and consensus segmentation methods.

Main Methods:

  • A DL segmentation network was trained on 4,784 paired synchrotron-radiation phase contrast imaging (SR-PCI) and clinical CT datasets.
  • The network was developed using 100 cadaveric specimens with diverse CT acquisition protocols and resolutions.
  • External validation was performed against seven domain experts and STAPLE consensus segmentation on an unseen dataset.

Main Results:

  • The DL network achieved a Dice similarity coefficient of 0.922 and Hausdorff distances of 0.329 mm (max) and 0.006 mm (avg) compared to SR-PCI ground truth.
  • The automated segmentation significantly outperformed individual experts, average expert performance, and STAPLE consensus.
  • The network demonstrated high accuracy on cone-beam CT and helical CT with resolutions down to 625 μm.

Conclusions:

  • This study presents the first automated inner ear segmentation algorithm that surpasses expert performance.
  • The developed DL network establishes a new potential clinical gold standard for inner ear segmentation.
  • Automated segmentation offers a reliable and efficient alternative to manual delineation in clinical practice.