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Deep Learning-Based Segmentation of Post-Mortem Human's Olfactory Bulb Structures in X-ray Phase-Contrast Tomography.

Alexandr Meshkov¹, Anvar Khafizov^2,3, Alexey Buzmakov^2,4

¹The Moscow Institute of Physics and Technology, 9 Institutskiy per., 141701 Moscow, Russia.

Tomography (Ann Arbor, Mich.)

|July 27, 2022

Summary

This summary is machine-generated.

This study introduces a new pipeline using convolutional neural networks (CNNs) for automated segmentation of human olfactory bulb (OB) layers from X-ray phase contrast tomography (XPCT) images. This method enables accurate morphometric analysis for smell impairment research.

Keywords:

X-ray phase-contrast tomography convolutional neural network deep learning olfactory bulb segmentation

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The Olfactory System as a Model to Study Axonal Growth Patterns and Morphology In Vivo

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Published on: October 30, 2014

Area of Science:

Neuroscience
Medical Imaging
Computational Biology

Background:

The human olfactory bulb (OB) exhibits a laminar structure, but indistinct boundaries complicate cell population segregation.
Standard 3D visualization tools lack the resolution for accurate morphometric analysis of OB layers.
X-ray phase contrast tomography (XPCT) provides high resolution for brain imaging but presents challenges in manual delineation of OB microanatomy.

Purpose of the Study:

To develop a fully automated segmentation pipeline for human OB morphological layers using XPCT data.
To enable accurate morphometric analysis of OB layers for research into smell impairment.
To provide a tool for assessing OB layer-specific degeneration in olfactory disorders.

Main Methods:

Proposed a novel pipeline for tomographic data processing of human OB XPCT images.
Utilized convolutional neural networks (CNNs) for segmenting native, unstained human OB images.
Achieved virtual segmentation of the entire OB and precise delineation of individual neuronal cell layers.

Main Results:

Successfully segmented XPCT images of the human olfactory bulb with high accuracy.
Demonstrated the capability of the CNN-based pipeline for automated layer delineation.
Enabled accurate virtual segmentation of OB structures, crucial for further analysis.

Conclusions:

The developed pipeline offers an effective tool for automated segmentation of human OB layers from XPCT data.
This approach facilitates accurate morphometric analysis essential for understanding OB changes in smell impairment.
The tool aids in investigating OB layer-specific degeneration in patients with olfactory disorders.