Publikation
Deep learning-based simultaneous registration and unsupervised non-correspondence segmentation of medical images with pathologies
Julia Andresen; Timo Kepp; Jan Ehrhardt; Claus von der Burchard; Johann Roider; Heinz Handels
In: International Journal of Computer Assisted Radiology and Surgery (IJCARS), Vol. 17, No. 4, Pages 699-710, Springer, 2022.
Zusammenfassung
Purpose The registration of medical images often suffers from missing correspondences due to inter-patient variations,
pathologies and their progression leading to implausible deformations that cause misregistrations and might eliminate valu-
able information. Detecting non-corresponding regions simultaneously with the registration process helps generating better
deformations and has been investigated thoroughly with classical iterative frameworks but rarely with deep learning-based
methods.
Methods We present the joint non-correspondence segmentation and image registration network (NCR-Net), a convolu-
tional neural network (CNN) trained on a Mumford–Shah-like functional, transferring the classical approach to the field of
deep learning. NCR-Net consists of one encoding and two decoding parts allowing the network to simultaneously generate
diffeomorphic deformations and segment non-correspondences. The loss function is composed of a masked image distance
measure and regularization of deformation field and segmentation output. Additionally, anatomical labels are used for weak
supervision of the registration task. No manual segmentations of non-correspondences are required.
Results The proposed network is evaluated on the publicly available LPBA40 dataset with artificially added stroke lesions and
a longitudinal optical coherence tomography (OCT) dataset of patients with age-related macular degeneration. The LPBA40
data are used to quantitatively assess the segmentation performance of the network, and it is shown qualitatively that NCR-
Net can be used for the unsupervised segmentation of pathologies in OCT images. Furthermore, NCR-Net is compared to a
registration-only network and state-of-the-art registration algorithms showing that NCR-Net achieves competitive performance
and superior robustness to non-correspondences.
Conclusion NCR-Net, a CNN for simultaneous image registration and unsupervised non-correspondence segmentation, is
presented. Experimental results show the network’s ability to segment non-correspondence regions in an unsupervised manner
and its robust registration performance even in the presence of large pathologies.