Deep negative volume segmentation

Abstract Clinical examination of three-dimensional image data of compound anatomical objects, such as complex joints, remains a tedious process, demanding the time and the expertise of physicians. For instance, automation of the segmentation task of the TMJ (temporomandibular joint) has been hindere...

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Autores principales:	Kristina Belikova, Oleg Y. Rogov, Aleksandr Rybakov, Maxim V. Maslov, Dmitry V. Dylov
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/facf56d5699a455aaece8a3512fb8e38
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spelling	oai:doaj.org-article:facf56d5699a455aaece8a3512fb8e382021-12-02T16:27:44ZDeep negative volume segmentation10.1038/s41598-021-95526-12045-2322https://doaj.org/article/facf56d5699a455aaece8a3512fb8e382021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95526-1https://doaj.org/toc/2045-2322Abstract Clinical examination of three-dimensional image data of compound anatomical objects, such as complex joints, remains a tedious process, demanding the time and the expertise of physicians. For instance, automation of the segmentation task of the TMJ (temporomandibular joint) has been hindered by its compound three-dimensional shape, multiple overlaid textures, an abundance of surrounding irregularities in the skull, and a virtually omnidirectional range of the jaw’s motion—all of which extend the manual annotation process to more than an hour per patient. To address the challenge, we invent a new workflow for the 3D segmentation task: namely, we propose to segment empty spaces between all the tissues surrounding the object—the so-called negative volume segmentation. Our approach is an end-to-end pipeline that comprises a V-Net for bone segmentation, a 3D volume construction by inflation of the reconstructed bone head in all directions along the normal vector to its mesh faces. Eventually confined within the skull bones, the inflated surface occupies the entire “negative” space in the joint, effectively providing a geometrical/topological metric of the joint’s health. We validate the idea on the CT scans in a 50-patient dataset, annotated by experts in maxillofacial medicine, quantitatively compare the asymmetry given the left and the right negative volumes, and automate the entire framework for clinical adoption.Kristina BelikovaOleg Y. RogovAleksandr RybakovMaxim V. MaslovDmitry V. DylovNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kristina Belikova Oleg Y. Rogov Aleksandr Rybakov Maxim V. Maslov Dmitry V. Dylov Deep negative volume segmentation
description	Abstract Clinical examination of three-dimensional image data of compound anatomical objects, such as complex joints, remains a tedious process, demanding the time and the expertise of physicians. For instance, automation of the segmentation task of the TMJ (temporomandibular joint) has been hindered by its compound three-dimensional shape, multiple overlaid textures, an abundance of surrounding irregularities in the skull, and a virtually omnidirectional range of the jaw’s motion—all of which extend the manual annotation process to more than an hour per patient. To address the challenge, we invent a new workflow for the 3D segmentation task: namely, we propose to segment empty spaces between all the tissues surrounding the object—the so-called negative volume segmentation. Our approach is an end-to-end pipeline that comprises a V-Net for bone segmentation, a 3D volume construction by inflation of the reconstructed bone head in all directions along the normal vector to its mesh faces. Eventually confined within the skull bones, the inflated surface occupies the entire “negative” space in the joint, effectively providing a geometrical/topological metric of the joint’s health. We validate the idea on the CT scans in a 50-patient dataset, annotated by experts in maxillofacial medicine, quantitatively compare the asymmetry given the left and the right negative volumes, and automate the entire framework for clinical adoption.
format	article
author	Kristina Belikova Oleg Y. Rogov Aleksandr Rybakov Maxim V. Maslov Dmitry V. Dylov
author_facet	Kristina Belikova Oleg Y. Rogov Aleksandr Rybakov Maxim V. Maslov Dmitry V. Dylov
author_sort	Kristina Belikova
title	Deep negative volume segmentation
title_short	Deep negative volume segmentation
title_full	Deep negative volume segmentation
title_fullStr	Deep negative volume segmentation
title_full_unstemmed	Deep negative volume segmentation
title_sort	deep negative volume segmentation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/facf56d5699a455aaece8a3512fb8e38
work_keys_str_mv	AT kristinabelikova deepnegativevolumesegmentation AT olegyrogov deepnegativevolumesegmentation AT aleksandrrybakov deepnegativevolumesegmentation AT maximvmaslov deepnegativevolumesegmentation AT dmitryvdylov deepnegativevolumesegmentation
_version_	1718384030512054272

Deep negative volume segmentation

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