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

Baum, Zachary MC;

Ungi, Tamas;

Schlenger, Christopher;

Hu, Yipeng;

Barratt, Dean C;


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

Proceedings of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (ASMUS: Advances in Simplifying Medical UltraSound).

(pp. pp. 1-11).


(In press).


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.

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