Next Article in Journal
Direct Visualization of Cervical Interlaminar Epidural Injections Using Sonography
Previous Article in Journal
Finding a Good Balance between Pressure to Publish and Scientific Integrity and How to Overcome Temptation of Scientific Misconduct
 
 
Search for Articles:
Title / Keyword
Author / Affiliation
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Tomography
Volume 8
Issue 4
10.3390/tomography8040156
tomography-logo
Submit to this Journal Review for this Journal Edit a Special Issue
► Article Menu

Article Menu

share announcement question_answer thumb_up
...
textsms
...
Article

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

by
Alexandr Meshkov
1,
Anvar Khafizov
2,3,
Alexey Buzmakov
2,4,
Inna Bukreeva
5,6,
Olga Junemann
7,
Michela Fratini
5,8,
Alessia Cedola
5,
Marina Chukalina
2,9,10,*,
Andrei Yamaev
9,
Giuseppe Gigli
11,
Fabian Wilde
12,
Elena Longo
13,
Victor Asadchikov
2,
Sergey Saveliev
7 and
Dmitry Nikolaev
9,10
1
The Moscow Institute of Physics and Technology, 9 Institutskiy per., 141701 Moscow, Russia
2
FSRC «Crystallography and Photonics» RAS, Leninskiy pr. 59, 119333 Moscow, Russia
3
Croc Inc. Company, Volochayevskaya Ulitsa 5/3, 111033 Moscow, Russia
4
Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Vavilova Str. 44b2, 119333 Moscow, Russia
5
Institute of Nanotechnology—CNR, c/o Department of Physics, La Sapienza University, Piazzale Aldo Moro 5, 00185 Rome, Italy
6
P.N. Lebedev Physical Institute, RAS, Leninskiy pr. 53, 119991 Moscow, Russia
7
FSSI Research Institute of Human Morphology, Tsyurupy Str. 3, 117418 Moscow, Russia
8
IRCCS Santa Lucia Foundation, Via Ardeatina 306/354, 00142 Rome, Italy
9
Smart Engines Service LLC, 60-Letiya Oktyabrya pr. 9, 117312 Moscow, Russia
10
Institute for Information Transmission Problems of Russian Academy of Sciences (Kharkevich Institute), Bol’shoi Karetnii per. 19 Str. 1, 127051 Moscow, Russia
11
Institute of Nanotechnology—CNR, c/o Campus Ecotekne—Universita del Salento, Via Monteroni, 73100 Lecce, Italy
12
Institute of Materials Research, Helmholtz-Zentrum Hereon, Max-Planck-Str. 1, 21502 Geesthacht, Germany
13
Elettra-Sincrotrone Trieste S.C.p.A., 34149 Trieste, Italy
 
add Show full affiliation list
 
remove Hide full affiliation list
*
Author to whom correspondence should be addressed.
Academic Editors: Emilio Quaia and Li Yueh Hsu
Tomography 2022, 8(4), 1854-1868; https://doi.org/10.3390/tomography8040156
Received: 2 May 2022 / Revised: 12 July 2022 / Accepted: 18 July 2022 / Published: 22 July 2022
View Full-Text Download PDF
Browse Figures
Review Reports

Abstract

The human olfactory bulb (OB) has a laminar structure. The segregation of cell populations in the OB image poses a significant challenge because of indistinct boundaries of the layers. Standard 3D visualization tools usually have a low resolution and cannot provide the high accuracy required for morphometric analysis. X-ray phase contrast tomography (XPCT) offers sufficient resolution and contrast to identify single cells in large volumes of the brain. The numerous microanatomical structures detectable in XPCT image of the OB, however, greatly complicate the manual delineation of OB neuronal cell layers. To address the challenging problem of fully automated segmentation of XPCT images of human OB morphological layers, we propose a new pipeline for tomographic data processing. Convolutional neural networks (CNN) were used to segment XPCT image of native unstained human OB. Virtual segmentation of the whole OB and an accurate delineation of each layer in a healthy non-demented OB is mandatory as the first step for assessing OB morphological changes in smell impairment research. In this framework, we proposed an effective tool that could help to shed light on OB layer-specific degeneration in patients with olfactory disorder. View Full-Text
Keywords: olfactory bulb; deep learning; convolutional neural network; segmentation; X-ray phase-contrast tomography olfactory bulb; deep learning; convolutional neural network; segmentation; X-ray phase-contrast tomography
►▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Supplementary Material

SciFeed

Share and Cite

MDPI and ACS Style

Meshkov, A.; Khafizov, A.; Buzmakov, A.; Bukreeva, I.; Junemann, O.; Fratini, M.; Cedola, A.; Chukalina, M.; Yamaev, A.; Gigli, G.; Wilde, F.; Longo, E.; Asadchikov, V.; Saveliev, S.; Nikolaev, D. Deep Learning-Based Segmentation of Post-Mortem Human’s Olfactory Bulb Structures in X-ray Phase-Contrast Tomography. Tomography 2022, 8, 1854-1868. https://doi.org/10.3390/tomography8040156

AMA Style

Meshkov A, Khafizov A, Buzmakov A, Bukreeva I, Junemann O, Fratini M, Cedola A, Chukalina M, Yamaev A, Gigli G, Wilde F, Longo E, Asadchikov V, Saveliev S, Nikolaev D. Deep Learning-Based Segmentation of Post-Mortem Human’s Olfactory Bulb Structures in X-ray Phase-Contrast Tomography. Tomography. 2022; 8(4):1854-1868. https://doi.org/10.3390/tomography8040156

Chicago/Turabian Style

Meshkov, Alexandr, Anvar Khafizov, Alexey Buzmakov, Inna Bukreeva, Olga Junemann, Michela Fratini, Alessia Cedola, Marina Chukalina, Andrei Yamaev, Giuseppe Gigli, Fabian Wilde, Elena Longo, Victor Asadchikov, Sergey Saveliev, and Dmitry Nikolaev. 2022. "Deep Learning-Based Segmentation of Post-Mortem Human’s Olfactory Bulb Structures in X-ray Phase-Contrast Tomography" Tomography 8, no. 4: 1854-1868. https://doi.org/10.3390/tomography8040156

Find Other Styles

Article Metrics

Article Access Map by Country/Region

1
Back to TopTop