Revealing hidden potentials of the q-space signal in breast cancer

Jäger PF, Bickelhaupt S, Laun FB, Lederer W, Heidi D, Kuder TA, Paech D, Bonekamp D, Radbruch A, Delorme S, Schlemmer HP, Steudle F, Maier-Hein KH (2017)

Publication Type: Conference contribution

Publication year: 2017

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Book Volume: 10433 LNCS

Pages Range: 664-671

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Quebec City, QC CA

ISBN: 9783319661810

DOI: 10.1007/978-3-319-66182-7_76

Abstract

Mammography screening for early detection of breast lesions currently suffers from high amounts of false positive findings, which result in unnecessary invasive biopsies. Diffusion-weighted MR images (DWI) can help to reduce many of these false-positive findings prior to biopsy. Current approaches estimate tissue properties by means of quantitative parameters taken from generative, biophysical models fit to the q-space encoded signal under certain assumptions regarding noise and spatial homogeneity. This process is prone to fitting instability and partial information loss due to model simplicity. We reveal unexplored potentials of the signal by integrating all data processing components into a convolutional neural network (CNN) architecture that is designed to propagate clinical target information down to the raw input images. This approach enables simultaneous and target-specific optimization of image normalization, signal exploitation, global representation learning and classification. Using a multicentric data set of 222 patients, we demonstrate that our approach significantly improves clinical decision making with respect to the current state of the art.

Authors with CRIS profile

Frederik Bernd Laun Professur für quantitative MR-Bildgebung

Involved external institutions

Deutsches Krebsforschungszentrum (DKFZ) DE Germany (DE) ATOS-Praxisklinik Heidelberg DE Germany (DE) RadiologieZentrumMannheim DE Germany (DE)

How to cite

APA:

Jäger, P.F., Bickelhaupt, S., Laun, F.B., Lederer, W., Heidi, D., Kuder, T.A.,... Maier-Hein, K.H. (2017). Revealing hidden potentials of the q-space signal in breast cancer. In Maxime Descoteaux, Simon Duchesne, Alfred Franz, Pierre Jannin, D. Louis Collins, Lena Maier-Hein (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 664-671). Quebec City, QC, CA: Springer Verlag.

MLA:

Jäger, Paul F., et al. "Revealing hidden potentials of the q-space signal in breast cancer." Proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, Quebec City, QC Ed. Maxime Descoteaux, Simon Duchesne, Alfred Franz, Pierre Jannin, D. Louis Collins, Lena Maier-Hein, Springer Verlag, 2017. 664-671.

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