Learning Generalized Non-Rigid Multimodal Biomedical Image Registration from Generic Point Set Data

by   Zachary MC Baum, et al.

Free Point Transformer (FPT) has been proposed as a data-driven, non-rigid point set registration approach using deep neural networks. As FPT does not assume constraints based on point vicinity or correspondence, it may be trained simply and in a flexible manner by minimizing an unsupervised loss based on the Chamfer Distance. This makes FPT amenable to real-world medical imaging applications where ground-truth deformations may be infeasible to obtain, or in scenarios where only a varying degree of completeness in the point sets to be aligned is available. To test the limit of the correspondence finding ability of FPT and its dependency on training data sets, this work explores the generalizability of the FPT from well-curated non-medical data sets to medical imaging data sets. First, we train FPT on the ModelNet40 dataset to demonstrate its effectiveness and the superior registration performance of FPT over iterative and learning-based point set registration methods. Second, we demonstrate superior performance in rigid and non-rigid registration and robustness to missing data. Last, we highlight the interesting generalizability of the ModelNet-trained FPT by registering reconstructed freehand ultrasound scans of the spine and generic spine models without additional training, whereby the average difference to the ground truth curvatures is 1.3 degrees, across 13 patients.


Real-time multimodal image registration with partial intraoperative point-set data

We present Free Point Transformer (FPT) - a deep neural network architec...

Evaluating Registration Without Ground Truth

We present a generic method for assessing the quality of non-rigid regis...

Registration of serial sections: An evaluation method based on distortions of the ground truths

Registration of histological serial sections is a challenging task. Seri...

Multimodality Biomedical Image Registration using Free Point Transformer Networks

We describe a point-set registration algorithm based on a novel free poi...

Non-rigid Medical Image Registration using Physics-informed Neural Networks

Biomechanical modelling of soft tissue provides a non-data-driven method...

Shape registration in the time of transformers

In this paper, we propose a transformer-based procedure for the efficien...

The Coherent Point Drift for Clustered Point Sets

The problem of non-rigid point set registration is a key problem for man...

Please sign up or login with your details

Forgot password? Click here to reset