SPLNet: Self-Distilled Progressive Learning Network for Deformable Medical Image Registration.

Authors

Yibo Wang, Mingwei Wen, Xuming Zhang

Published in

IEEE transactions on neural networks and learning systems. Volume PP. Aug 17, 2026. Epub Aug 17, 2026.

Abstract

Deformable medical image registration serves as a vital prerequisite for multimodal image fusion, disease diagnosis, and surgical navigation. Although existing learning-based registration methods are very popular, they mainly rely on the pyramid and cascaded structure-based networks trained in an end-to-end manner, which may lead to suboptimal parameter optimization and limited accuracy. In this work, we have proposed a novel self-distilled progressive learning network (SPLNet) utilizing progressive learning and knowledge distillation (KD). In the proposed SPLNet, the optimization order of each component is reasonably arranged based on the idea of evolution and the corresponding learning gaps are filled by using the self-distillation (SD) scheme. The layers of different levels in SPLNet are progressively updated based on the depth-wise pyramid weight-inherited evolution, thereby enhancing the sensitivity of SPLNet to multiscale deformations. Meanwhile, the feature maps of trained layers are treated as teachers and shifted to distill the learning process of newly generated layers. Besides, more subnetworks in SPLNet are progressively appended based on the width-wise cascaded evolution, enabling SPLNet to learn the decomposed deformations more easily. Distinctively, a distribution-consistent distillation is proposed to fill the learning gaps between the newly appended subnetworks and old ones. Indeed, the introduction of two types of evolution and distillation enables SPLNet to handle complex deformations more effectively. Experiments on both mono-modal datasets (LPBA40, OASIS, SLIVER, and LSPIG) and a multimodal dataset (MMWHS) demonstrate the superior registration performance of SPLNet to several baseline methods in terms of Dice score, 95% maximum Hausdorff distance, folds, and inference speed. The code is available at https://github.com/YiboWang3813/SEN.

PMID:
42606970
Bibliographic data and abstract were imported from PubMed on 18 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.