MRI breast segmentation using unsupervised neural networks for biomechanical models

S. Said; M. Meyling; R. Huguenot; M. Horning; P. Clauser; N. V. Ruiter; P. A. T. Baltzer; T. Hopp

doi:10.1117/12.2624245

13 July 2022 MRI breast segmentation using unsupervised neural networks for biomechanical models

S. Said, M. Meyling, R. Huguenot, M. Horning, P. Clauser, N. V. Ruiter, P. A. T. Baltzer, T. Hopp

Proceedings Volume 12286, 16th International Workshop on Breast Imaging (IWBI2022); 122860C (2022) https://doi.org/10.1117/12.2624245
Event: Sixteenth International Workshop on Breast Imaging, 2022, Leuven, Belgium

Abstract

In multimodal diagnosis for early breast cancer detection, spatial alignment by means of image registration is an important task. We develop patient-specific biomechanical models of the breast, for which one of the challenges is automatic segmentation for magnetic resonance imaging (MRI) of the breast. In this paper, we propose a novel method using unsupervised neural networks with pre-processing and post-processing to enable automatic breast MRI segmentation for three tissue types simultaneously: fatty, glandular, and muscular tissue. Pre-processing aims at facilitating training of the network. The architecture of neural network is a Kanezaki-net extended to 3D and consists of two sub-networks. Post-processing is enhancing the obtained segmentations by removing common errors. 25 datasets of T2 weighted MRI from the Medical University of Vienna have been evaluated qualitatively by two observers while eight datasets have been evaluated quantitatively based on a ground truth annotated by a medical practitioner. As a result of the qualitative evaluation, 22 out of 25 are usable for biomechanical models. Quantitatively, we achieved an average dice coefficient of 0.88 for fatty tissue, 0.5 for glandular tissue, and 0.86 for muscular tissue. The proposed method can serve as a robust method for automatic generation of biomechanical models.

Citation Download Citation

S. Said, M. Meyling, R. Huguenot, M. Horning, P. Clauser, N. V. Ruiter, P. A. T. Baltzer, and T. Hopp "MRI breast segmentation using unsupervised neural networks for biomechanical models", Proc. SPIE 12286, 16th International Workshop on Breast Imaging (IWBI2022), 122860C (13 July 2022); https://doi.org/10.1117/12.2624245

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available