Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training

Huaqi Qiu, Chen Qin, Loic Le Folgoc, Benjamin Hou, Jo Schlemper, Daniel Rueckert

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Deep learning based registration methods have emerged as alternatives to traditional registration methods, with competitive accuracy and significantly less runtime. Two different strategies have been proposed to train such deep learning registration networks: supervised training strategy where the model is trained to regress to generated ground truth deformation; and unsupervised training strategy where the model directly optimises the similarity between the registered images. In this work, we directly compare the performance of these two training strategies for cardiac motion estimation on cardiac cine MR sequences. Testing on real cardiac MRI data shows that while the supervised training yields more regular deformation, the unsupervised more accurately captures the deformation of anatomical structures in cardiac motion.
Original languageEnglish
Title of host publicationInternational Workshop on Statistical Atlases and Computational Models of the Heart
Subtitle of host publicationStatistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges
PublisherSpringerLink
Pages186-194
Number of pages9
Volume12009
ISBN (Electronic)978-3-030-39074-7
ISBN (Print)978-3-030-39073-0
Publication statusE-pub ahead of print - 23 Jan 2020

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Link
Volume12009
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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