Automatic slice identification in 3D medical images with a ConvNet regressor

Bob D. de Vos; Max A. Viergever; Pim A. de Jong; Ivana Išgum

doi:https://doi.org/10.1007/978-3-319-46976-8_17

Automatic slice identification in 3D medical images with a ConvNet regressor

Bob D. de Vos, Max A. Viergever, Pim A. de Jong, Ivana Išgum

Amsterdam UMC

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

7 Citations (Scopus)

Abstract

Identification of anatomical regions of interest is a prerequisite in many medical image analysis tasks. We propose a method that automatically identifies a slice of interest (SOI) in 3D images with a convolutional neural network (ConvNet) regressor. In 150 chest CT scans two reference slices were manually identified: one containing the aortic root and another superior to the aortic arch. In two independent experiments, the ConvNet regressor was trained with 100 CTs to determine the distance between each slice and the SOI in a CT. To identify the SOI, a first order polynomial was fitted through the obtained distances. In 50 test scans, the mean distances between the reference and the automatically identified slices were 5.7mm (4.0 slices) for the aortic root and 5.6mm (3.7 slices) for the aortic arch. The method shows similar results for both tasks and could be used for automatic slice identification.

Original language	English
Title of host publication	Deep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings
Editors	Zhi Lu, Vasileios Belagiannis, Joao Manuel R.S. Tavares, Jaime S. Cardoso, Andrew Bradley, Joao Paulo Papa, Jacinto C. Nascimento, Marco Loog, Julien Cornebise, Gustavo Carneiro, Diana Mateus, Loic Peter
Publisher	Springer Verlag
Pages	161-169
Volume	10008 LNCS
ISBN (Print)	9783319469751
DOIs	https://doi.org/10.1007/978-3-319-46976-8_17
Publication status	Published - 2016
Event	1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016 and 2nd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016 held in conjunction with 19th International Conference ... - Athens, Greece Duration: 21 Oct 2016 → 21 Oct 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Conference

Conference	1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016 and 2nd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016 held in conjunction with 19th International Conference ...
Country/Territory	Greece
City	Athens
Period	21/10/2016 → 21/10/2016

Access to Document

https://doi.org/10.1007/978-3-319-46976-8_17

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de Vos, B. D., Viergever, M. A., de Jong, P. A., & Išgum, I. (2016). Automatic slice identification in 3D medical images with a ConvNet regressor. In Z. Lu, V. Belagiannis, J. M. R. S. Tavares, J. S. Cardoso, A. Bradley, J. P. Papa, J. C. Nascimento, M. Loog, J. Cornebise, G. Carneiro, D. Mateus, & L. Peter (Eds.), Deep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings (Vol. 10008 LNCS, pp. 161-169). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Springer Verlag. https://doi.org/10.1007/978-3-319-46976-8_17

de Vos, Bob D. ; Viergever, Max A. ; de Jong, Pim A. et al. / Automatic slice identification in 3D medical images with a ConvNet regressor. Deep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings. editor / Zhi Lu ; Vasileios Belagiannis ; Joao Manuel R.S. Tavares ; Jaime S. Cardoso ; Andrew Bradley ; Joao Paulo Papa ; Jacinto C. Nascimento ; Marco Loog ; Julien Cornebise ; Gustavo Carneiro ; Diana Mateus ; Loic Peter. Vol. 10008 LNCS Springer Verlag, 2016. pp. 161-169 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{012a425554b840af862fda278881c058,

title = "Automatic slice identification in 3D medical images with a ConvNet regressor",

abstract = "Identification of anatomical regions of interest is a prerequisite in many medical image analysis tasks. We propose a method that automatically identifies a slice of interest (SOI) in 3D images with a convolutional neural network (ConvNet) regressor. In 150 chest CT scans two reference slices were manually identified: one containing the aortic root and another superior to the aortic arch. In two independent experiments, the ConvNet regressor was trained with 100 CTs to determine the distance between each slice and the SOI in a CT. To identify the SOI, a first order polynomial was fitted through the obtained distances. In 50 test scans, the mean distances between the reference and the automatically identified slices were 5.7mm (4.0 slices) for the aortic root and 5.6mm (3.7 slices) for the aortic arch. The method shows similar results for both tasks and could be used for automatic slice identification.",

author = "{de Vos}, {Bob D.} and Viergever, {Max A.} and {de Jong}, {Pim A.} and Ivana I{\v s}gum",

year = "2016",

doi = "https://doi.org/10.1007/978-3-319-46976-8_17",

language = "English",

isbn = "9783319469751",

volume = "10008 LNCS",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "161--169",

editor = "Zhi Lu and Vasileios Belagiannis and Tavares, {Joao Manuel R.S.} and Cardoso, {Jaime S.} and Andrew Bradley and Papa, {Joao Paulo} and Nascimento, {Jacinto C.} and Marco Loog and Julien Cornebise and Gustavo Carneiro and Diana Mateus and Loic Peter",

booktitle = "Deep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings",

note = "1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016 and 2nd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016 held in conjunction with 19th International Conference ... ; Conference date: 21-10-2016 Through 21-10-2016",

}

de Vos, BD, Viergever, MA, de Jong, PA & Išgum, I 2016, Automatic slice identification in 3D medical images with a ConvNet regressor. in Z Lu, V Belagiannis, JMRS Tavares, JS Cardoso, A Bradley, JP Papa, JC Nascimento, M Loog, J Cornebise, G Carneiro, D Mateus & L Peter (eds), Deep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings. vol. 10008 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, pp. 161-169, 1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016 and 2nd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016 held in conjunction with 19th International Conference ..., Athens, Greece, 21/10/2016. https://doi.org/10.1007/978-3-319-46976-8_17

Automatic slice identification in 3D medical images with a ConvNet regressor. / de Vos, Bob D.; Viergever, Max A.; de Jong, Pim A. et al.
Deep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings. ed. / Zhi Lu; Vasileios Belagiannis; Joao Manuel R.S. Tavares; Jaime S. Cardoso; Andrew Bradley; Joao Paulo Papa; Jacinto C. Nascimento; Marco Loog; Julien Cornebise; Gustavo Carneiro; Diana Mateus; Loic Peter. Vol. 10008 LNCS Springer Verlag, 2016. p. 161-169 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Automatic slice identification in 3D medical images with a ConvNet regressor

AU - de Vos, Bob D.

AU - Viergever, Max A.

AU - de Jong, Pim A.

AU - Išgum, Ivana

PY - 2016

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N2 - Identification of anatomical regions of interest is a prerequisite in many medical image analysis tasks. We propose a method that automatically identifies a slice of interest (SOI) in 3D images with a convolutional neural network (ConvNet) regressor. In 150 chest CT scans two reference slices were manually identified: one containing the aortic root and another superior to the aortic arch. In two independent experiments, the ConvNet regressor was trained with 100 CTs to determine the distance between each slice and the SOI in a CT. To identify the SOI, a first order polynomial was fitted through the obtained distances. In 50 test scans, the mean distances between the reference and the automatically identified slices were 5.7mm (4.0 slices) for the aortic root and 5.6mm (3.7 slices) for the aortic arch. The method shows similar results for both tasks and could be used for automatic slice identification.

AB - Identification of anatomical regions of interest is a prerequisite in many medical image analysis tasks. We propose a method that automatically identifies a slice of interest (SOI) in 3D images with a convolutional neural network (ConvNet) regressor. In 150 chest CT scans two reference slices were manually identified: one containing the aortic root and another superior to the aortic arch. In two independent experiments, the ConvNet regressor was trained with 100 CTs to determine the distance between each slice and the SOI in a CT. To identify the SOI, a first order polynomial was fitted through the obtained distances. In 50 test scans, the mean distances between the reference and the automatically identified slices were 5.7mm (4.0 slices) for the aortic root and 5.6mm (3.7 slices) for the aortic arch. The method shows similar results for both tasks and could be used for automatic slice identification.

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U2 - https://doi.org/10.1007/978-3-319-46976-8_17

DO - https://doi.org/10.1007/978-3-319-46976-8_17

M3 - Conference contribution

SN - 9783319469751

VL - 10008 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 161

EP - 169

BT - Deep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings

A2 - Lu, Zhi

A2 - Belagiannis, Vasileios

A2 - Tavares, Joao Manuel R.S.

A2 - Cardoso, Jaime S.

A2 - Bradley, Andrew

A2 - Papa, Joao Paulo

A2 - Nascimento, Jacinto C.

A2 - Loog, Marco

A2 - Cornebise, Julien

A2 - Carneiro, Gustavo

A2 - Mateus, Diana

A2 - Peter, Loic

PB - Springer Verlag

T2 - 1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016 and 2nd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016 held in conjunction with 19th International Conference ...

Y2 - 21 October 2016 through 21 October 2016

ER -

de Vos BD, Viergever MA, de Jong PA, Išgum I. Automatic slice identification in 3D medical images with a ConvNet regressor. In Lu Z, Belagiannis V, Tavares JMRS, Cardoso JS, Bradley A, Papa JP, Nascimento JC, Loog M, Cornebise J, Carneiro G, Mateus D, Peter L, editors, Deep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings. Vol. 10008 LNCS. Springer Verlag. 2016. p. 161-169. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: https://doi.org/10.1007/978-3-319-46976-8_17

Automatic slice identification in 3D medical images with a ConvNet regressor

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