Breast tissue segmentation and mammographic risk scoring using deep learning

Peter Kersten Petersen; Mads Nielsen; Pengfei Diao; Nico Karssemeijer; Martin Lillholm

doi:10.1007/978-3-319-07887-8_13

Breast tissue segmentation and mammographic risk scoring using deep learning

Peter Kersten Petersen, Mads Nielsen, Pengfei Diao, Nico Karssemeijer, Martin Lillholm

Datalogisk Institut

34 Citationer (Scopus)

Abstract

Mammographic scoring of density and texture are established methods to relate to the risk of breast cancer. We present a method that learns descriptive features from unlabeled mammograms and, using these learned features as the input to a simple classifier, address the following tasks: i) breast tissue segmentation ii) scoring of percentage mammographic density (PMD), and iii) scoring of mammographic texture (MT). Our results suggest that the learned PMD scores correlate well to manual ones, and that the learned MT scores are more related to future cancer risk than both manual and automatic PMD scores.

Originalsprog	Engelsk
Titel	Breast imaging : 12th International Workshop, IWDM 2014, Gifu City, Japan, June 29 – July 2, 2014. Proceedings
Redaktører	Hiroshi Fujita, Takeshi Hara, Chisako Muramatsu
Antal sider	7
Forlag	Springer Science+Business Media
Publikationsdato	2014
Sider	88-94
ISBN (Trykt)	978-3-319-07886-1
ISBN (Elektronisk)	978-3-319-07887-8
DOI	https://doi.org/10.1007/978-3-319-07887-8_13
Status	Udgivet - 2014
Begivenhed	International Workshop, IWDM 2014 - Gifu City, Japan Varighed: 29 jun. 2014 → 2 jul. 2014 Konferencens nummer: 12

Konference

Konference	International Workshop, IWDM 2014
Nummer	12
Land/Område	Japan
By	Gifu City
Periode	29/06/2014 → 02/07/2014

Navn	Lecture notes in computer science
Vol/bind	8539
ISSN	0302-9743

Emneord

Unsupervised feature learning
deep learning
breast cancer
mammograms
prognosis
risk factor
segmentation

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10.1007/978-3-319-07887-8_13

Citationsformater

Petersen, P. K., Nielsen, M., Diao, P., Karssemeijer, N., & Lillholm, M. (2014). Breast tissue segmentation and mammographic risk scoring using deep learning. I H. Fujita, T. Hara, & C. Muramatsu (red.), Breast imaging: 12th International Workshop, IWDM 2014, Gifu City, Japan, June 29 – July 2, 2014. Proceedings (s. 88-94). Springer Science+Business Media. Lecture notes in computer science Bind 8539 https://doi.org/10.1007/978-3-319-07887-8_13

Breast tissue segmentation and mammographic risk scoring using deep learning. / Petersen, Peter Kersten; Nielsen, Mads ; Diao, Pengfei et al.

Breast imaging: 12th International Workshop, IWDM 2014, Gifu City, Japan, June 29 – July 2, 2014. Proceedings. red. / Hiroshi Fujita; Takeshi Hara; Chisako Muramatsu. Springer Science+Business Media, 2014. s. 88-94 (Lecture notes in computer science, Bind 8539).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

Petersen, PK, Nielsen, M , Diao, P, Karssemeijer, N & Lillholm, M 2014, Breast tissue segmentation and mammographic risk scoring using deep learning. i H Fujita, T Hara & C Muramatsu (red), Breast imaging: 12th International Workshop, IWDM 2014, Gifu City, Japan, June 29 – July 2, 2014. Proceedings. Springer Science+Business Media, Lecture notes in computer science, bind 8539, s. 88-94, International Workshop, IWDM 2014, Gifu City, Japan, 29/06/2014. https://doi.org/10.1007/978-3-319-07887-8_13

Petersen PK, Nielsen M , Diao P, Karssemeijer N, Lillholm M. Breast tissue segmentation and mammographic risk scoring using deep learning. I Fujita H, Hara T, Muramatsu C, red., Breast imaging: 12th International Workshop, IWDM 2014, Gifu City, Japan, June 29 – July 2, 2014. Proceedings. Springer Science+Business Media. 2014. s. 88-94. (Lecture notes in computer science, Bind 8539). doi: 10.1007/978-3-319-07887-8_13

Petersen, Peter Kersten ; Nielsen, Mads ; Diao, Pengfei et al. / Breast tissue segmentation and mammographic risk scoring using deep learning. Breast imaging: 12th International Workshop, IWDM 2014, Gifu City, Japan, June 29 – July 2, 2014. Proceedings. red. / Hiroshi Fujita ; Takeshi Hara ; Chisako Muramatsu. Springer Science+Business Media, 2014. s. 88-94 (Lecture notes in computer science, Bind 8539).

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title = "Breast tissue segmentation and mammographic risk scoring using deep learning",

abstract = "Mammographic scoring of density and texture are established methods to relate to the risk of breast cancer. We present a method that learns descriptive features from unlabeled mammograms and, using these learned features as the input to a simple classifier, address the following tasks: i) breast tissue segmentation ii) scoring of percentage mammographic density (PMD), and iii) scoring of mammographic texture (MT). Our results suggest that the learned PMD scores correlate well to manual ones, and that the learned MT scores are more related to future cancer risk than both manual and automatic PMD scores.",

keywords = "Unsupervised feature learning, deep learning, breast cancer, mammograms, prognosis, risk factor, segmentation",

author = "Petersen, {Peter Kersten} and Mads Nielsen and Pengfei Diao and Nico Karssemeijer and Martin Lillholm",

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AU - Nielsen, Mads

AU - Diao, Pengfei

AU - Karssemeijer, Nico

AU - Lillholm, Martin

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PY - 2014

Y1 - 2014

N2 - Mammographic scoring of density and texture are established methods to relate to the risk of breast cancer. We present a method that learns descriptive features from unlabeled mammograms and, using these learned features as the input to a simple classifier, address the following tasks: i) breast tissue segmentation ii) scoring of percentage mammographic density (PMD), and iii) scoring of mammographic texture (MT). Our results suggest that the learned PMD scores correlate well to manual ones, and that the learned MT scores are more related to future cancer risk than both manual and automatic PMD scores.

AB - Mammographic scoring of density and texture are established methods to relate to the risk of breast cancer. We present a method that learns descriptive features from unlabeled mammograms and, using these learned features as the input to a simple classifier, address the following tasks: i) breast tissue segmentation ii) scoring of percentage mammographic density (PMD), and iii) scoring of mammographic texture (MT). Our results suggest that the learned PMD scores correlate well to manual ones, and that the learned MT scores are more related to future cancer risk than both manual and automatic PMD scores.

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KW - deep learning

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KW - mammograms

KW - prognosis

KW - risk factor

KW - segmentation

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Breast tissue segmentation and mammographic risk scoring using deep learning

Abstract

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Emneord

FN’s Verdensmål

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Citationsformater