Closing the Gap Between Deep and Conventional Image Registration Using Probabilistic Dense Displacement Networks

Mattias P. Heinrich*

*Corresponding author for this work


Nonlinear image registration continues to be a fundamentally important tool in medical image analysis. Diagnostic tasks, image-guided surgery and radiotherapy as well as motion analysis all rely heavily on accurate intra-patient alignment. Furthermore, inter-patient registration enables atlas-based segmentation or landmark localisation and shape analysis. When labelled scans are scarce and anatomical differences large, conventional registration has often remained superior to deep learning methods that have so far mainly dealt with relatively small or low-complexity deformations. We address this shortcoming by leveraging ideas from probabilistic dense displacement optimisation that has excelled in many registration tasks with large deformations. We propose to design a network with approximate min-convolutions and mean field inference for differentiable displacement regularisation within a discrete weakly-supervised registration setting. By employing these meaningful and theoretically proven constraints, our learnable registration algorithm contains very few trainable weights (primarily for feature extraction) and is easier to train with few labelled scans. It is very fast in training and inference and achieves state-of-the-art accuracies for the challenging inter-patient registration of abdominal CT outperforming previous deep learning approaches by 15% Dice overlap.

Original languageEnglish
Title of host publicationMICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
EditorsDinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan
Number of pages8
Volume11769 LNCS
PublisherSpringer, Cham
Publication date10.10.2019
ISBN (Print)978-3-030-32225-0
ISBN (Electronic)978-3-030-32226-7
Publication statusPublished - 10.10.2019
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention - Shenzhen, China
Duration: 13.10.201917.10.2019
Conference number: 232939


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