MRI breast segmentation using unsupervised neural networks for biomechanical models
Sarah Said, Michael Meyling, Rémi Huguenot, Marcel Hörning, Paola Clauser, Nicole V. Ruiter, Pascal Baltzer, Torsten Hopp · 2022
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.