Deep learning based automated diagnosis of bone metastases with SPECT thoracic bone images

Abstract SPECT nuclear medicine imaging is widely used for treating, diagnosing, evaluating and preventing various serious diseases. The automated classification of medical images is becoming increasingly important in developing computer-aided diagnosis systems. Deep learning, particularly for the c...

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Autores principales:	Qiang Lin, Tongtong Li, Chuangui Cao, Yongchun Cao, Zhengxing Man, Haijun Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/afc5c4313ac74df1930818e5cc5bd931
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spelling	oai:doaj.org-article:afc5c4313ac74df1930818e5cc5bd9312021-12-02T12:11:12ZDeep learning based automated diagnosis of bone metastases with SPECT thoracic bone images10.1038/s41598-021-83083-62045-2322https://doaj.org/article/afc5c4313ac74df1930818e5cc5bd9312021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83083-6https://doaj.org/toc/2045-2322Abstract SPECT nuclear medicine imaging is widely used for treating, diagnosing, evaluating and preventing various serious diseases. The automated classification of medical images is becoming increasingly important in developing computer-aided diagnosis systems. Deep learning, particularly for the convolutional neural networks, has been widely applied to the classification of medical images. In order to reliably classify SPECT bone images for the automated diagnosis of metastasis on which the SPECT imaging solely focuses, in this paper, we present several deep classifiers based on the deep networks. Specifically, original SPECT images are cropped to extract the thoracic region, followed by a geometric transformation that contributes to augment the original data. We then construct deep classifiers based on the widely used deep networks including VGG, ResNet and DenseNet by fine-tuning their parameters and structures or self-defining new network structures. Experiments on a set of real-world SPECT bone images show that the proposed classifiers perform well in identifying bone metastasis with SPECT imaging. It achieves 0.9807, 0.9900, 0.9830, 0.9890, 0.9802 and 0.9933 for accuracy, precision, recall, specificity, F-1 score and AUC, respectively, on the test samples from the augmented dataset without normalization.Qiang LinTongtong LiChuangui CaoYongchun CaoZhengxing ManHaijun WangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Qiang Lin Tongtong Li Chuangui Cao Yongchun Cao Zhengxing Man Haijun Wang Deep learning based automated diagnosis of bone metastases with SPECT thoracic bone images
description	Abstract SPECT nuclear medicine imaging is widely used for treating, diagnosing, evaluating and preventing various serious diseases. The automated classification of medical images is becoming increasingly important in developing computer-aided diagnosis systems. Deep learning, particularly for the convolutional neural networks, has been widely applied to the classification of medical images. In order to reliably classify SPECT bone images for the automated diagnosis of metastasis on which the SPECT imaging solely focuses, in this paper, we present several deep classifiers based on the deep networks. Specifically, original SPECT images are cropped to extract the thoracic region, followed by a geometric transformation that contributes to augment the original data. We then construct deep classifiers based on the widely used deep networks including VGG, ResNet and DenseNet by fine-tuning their parameters and structures or self-defining new network structures. Experiments on a set of real-world SPECT bone images show that the proposed classifiers perform well in identifying bone metastasis with SPECT imaging. It achieves 0.9807, 0.9900, 0.9830, 0.9890, 0.9802 and 0.9933 for accuracy, precision, recall, specificity, F-1 score and AUC, respectively, on the test samples from the augmented dataset without normalization.
format	article
author	Qiang Lin Tongtong Li Chuangui Cao Yongchun Cao Zhengxing Man Haijun Wang
author_facet	Qiang Lin Tongtong Li Chuangui Cao Yongchun Cao Zhengxing Man Haijun Wang
author_sort	Qiang Lin
title	Deep learning based automated diagnosis of bone metastases with SPECT thoracic bone images
title_short	Deep learning based automated diagnosis of bone metastases with SPECT thoracic bone images
title_full	Deep learning based automated diagnosis of bone metastases with SPECT thoracic bone images
title_fullStr	Deep learning based automated diagnosis of bone metastases with SPECT thoracic bone images
title_full_unstemmed	Deep learning based automated diagnosis of bone metastases with SPECT thoracic bone images
title_sort	deep learning based automated diagnosis of bone metastases with spect thoracic bone images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/afc5c4313ac74df1930818e5cc5bd931
work_keys_str_mv	AT qianglin deeplearningbasedautomateddiagnosisofbonemetastaseswithspectthoracicboneimages AT tongtongli deeplearningbasedautomateddiagnosisofbonemetastaseswithspectthoracicboneimages AT chuanguicao deeplearningbasedautomateddiagnosisofbonemetastaseswithspectthoracicboneimages AT yongchuncao deeplearningbasedautomateddiagnosisofbonemetastaseswithspectthoracicboneimages AT zhengxingman deeplearningbasedautomateddiagnosisofbonemetastaseswithspectthoracicboneimages AT haijunwang deeplearningbasedautomateddiagnosisofbonemetastaseswithspectthoracicboneimages
_version_	1718394649498877952

Deep learning based automated diagnosis of bone metastases with SPECT thoracic bone images

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