$Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images$
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Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images

Abstract Pelvic fracture is one of the leading causes of death in the elderly, carrying a high risk of death within 1 year of fracture. This study proposes an automated method to detect pelvic fractures on 3-dimensional computed tomography (3D-CT). Deep convolutional neural networks (DCNNs) have bee...

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Autores principales:	Kazutoshi Ukai, Rashedur Rahman, Naomi Yagi, Keigo Hayashi, Akihiro Maruo, Hirotsugu Muratsu, Syoji Kobashi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9cb7536b26cf47998aabcaf4303085c6
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spelling	oai:doaj.org-article:9cb7536b26cf47998aabcaf4303085c62021-12-02T17:51:06ZDetecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images10.1038/s41598-021-91144-z2045-2322https://doaj.org/article/9cb7536b26cf47998aabcaf4303085c62021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91144-zhttps://doaj.org/toc/2045-2322Abstract Pelvic fracture is one of the leading causes of death in the elderly, carrying a high risk of death within 1 year of fracture. This study proposes an automated method to detect pelvic fractures on 3-dimensional computed tomography (3D-CT). Deep convolutional neural networks (DCNNs) have been used for lesion detection on 2D and 3D medical images. However, training a DCNN directly using 3D images is complicated, computationally costly, and requires large amounts of training data. We propose a method that evaluates multiple, 2D, real-time object detection systems (YOLOv3 models) in parallel, in which each YOLOv3 model is trained using differently orientated 2D slab images reconstructed from 3D-CT. We assume that an appropriate reconstruction orientation would exist to optimally characterize image features of bone fractures on 3D-CT. Multiple YOLOv3 models in parallel detect 2D fracture candidates in different orientations simultaneously. The 3D fracture region is then obtained by integrating the 2D fracture candidates. The proposed method was validated in 93 subjects with bone fractures. Area under the curve (AUC) was 0.824, with 0.805 recall and 0.907 precision. The AUC with a single orientation was 0.652. This method was then applied to 112 subjects without bone fractures to evaluate over-detection. The proposed method successfully detected no bone fractures in all except 4 non-fracture subjects (96.4%).Kazutoshi UkaiRashedur RahmanNaomi YagiKeigo HayashiAkihiro MaruoHirotsugu MuratsuSyoji KobashiNature 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 Kazutoshi Ukai Rashedur Rahman Naomi Yagi Keigo Hayashi Akihiro Maruo Hirotsugu Muratsu Syoji Kobashi Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images
description	Abstract Pelvic fracture is one of the leading causes of death in the elderly, carrying a high risk of death within 1 year of fracture. This study proposes an automated method to detect pelvic fractures on 3-dimensional computed tomography (3D-CT). Deep convolutional neural networks (DCNNs) have been used for lesion detection on 2D and 3D medical images. However, training a DCNN directly using 3D images is complicated, computationally costly, and requires large amounts of training data. We propose a method that evaluates multiple, 2D, real-time object detection systems (YOLOv3 models) in parallel, in which each YOLOv3 model is trained using differently orientated 2D slab images reconstructed from 3D-CT. We assume that an appropriate reconstruction orientation would exist to optimally characterize image features of bone fractures on 3D-CT. Multiple YOLOv3 models in parallel detect 2D fracture candidates in different orientations simultaneously. The 3D fracture region is then obtained by integrating the 2D fracture candidates. The proposed method was validated in 93 subjects with bone fractures. Area under the curve (AUC) was 0.824, with 0.805 recall and 0.907 precision. The AUC with a single orientation was 0.652. This method was then applied to 112 subjects without bone fractures to evaluate over-detection. The proposed method successfully detected no bone fractures in all except 4 non-fracture subjects (96.4%).
format	article
author	Kazutoshi Ukai Rashedur Rahman Naomi Yagi Keigo Hayashi Akihiro Maruo Hirotsugu Muratsu Syoji Kobashi
author_facet	Kazutoshi Ukai Rashedur Rahman Naomi Yagi Keigo Hayashi Akihiro Maruo Hirotsugu Muratsu Syoji Kobashi
author_sort	Kazutoshi Ukai
title	Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images
title_short	Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images
title_full	Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images
title_fullStr	Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images
title_full_unstemmed	Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images
title_sort	detecting pelvic fracture on 3d-ct using deep convolutional neural networks with multi-orientated slab images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9cb7536b26cf47998aabcaf4303085c6
work_keys_str_mv	AT kazutoshiukai detectingpelvicfractureon3dctusingdeepconvolutionalneuralnetworkswithmultiorientatedslabimages AT rashedurrahman detectingpelvicfractureon3dctusingdeepconvolutionalneuralnetworkswithmultiorientatedslabimages AT naomiyagi detectingpelvicfractureon3dctusingdeepconvolutionalneuralnetworkswithmultiorientatedslabimages AT keigohayashi detectingpelvicfractureon3dctusingdeepconvolutionalneuralnetworkswithmultiorientatedslabimages AT akihiromaruo detectingpelvicfractureon3dctusingdeepconvolutionalneuralnetworkswithmultiorientatedslabimages AT hirotsugumuratsu detectingpelvicfractureon3dctusingdeepconvolutionalneuralnetworkswithmultiorientatedslabimages AT syojikobashi detectingpelvicfractureon3dctusingdeepconvolutionalneuralnetworkswithmultiorientatedslabimages
_version_	1718379301172150272

Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images

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