Multi-modal registration, especially CT/MR to ultrasound (US), is still a challenge, as conventional similarity metrics such as mutual information do not match the imaging characteristics of ultrasound. The main motivation for this work is to investigate whether a deep learning network can be used to directly estimate the displacement between a pair of multi-modal image patches, without explicitly performing similarity metric and optimizer, the two main components in a registration framework. The proposed DVNet is a fully convolutional neural network and is trained using a large set of artificially generated displacement vectors (DVs). The DVNet was evaluated on mono- and simulated multi-modal data, as well as real CT and US liver slices (selected from 3D volumes). The results show that the DVNet is quite robust on the single- and multi-modal (simulated) data, but does not work yet on the real CT and US images.

Original languageEnglish
Title of host publicationUnderstanding and Interpreting Machine Learning in Medical Image Computing Applications - First International Workshops MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Proceedings
PublisherSpringer Verlag
Pages43-51
Volume11038 LNCS
ISBN (Print)978-3-030-02627-1
DOIs
Publication statusPublished - 2018
Event1st International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, 1st International Workshop on Deep Learning Fails, DLF 2018, and 1st International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: 16 Sep 201820 Sep 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11038 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, 1st International Workshop on Deep Learning Fails, DLF 2018, and 1st International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018
CountrySpain
CityGranada
Period16/09/1820/09/18

    Research areas

  • CNN, CT, Liver, Registration, Ultrasound

ID: 47534196