Pimms: Permutation invariant multi-modal segmentation

Thomas Varsavsky, Zach Eaton-Rosen, Carole H. Sudre, Parashkev Nachev, M. Jorge Cardoso

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

In a research context, image acquisition will often involve a pre-defined static protocol and the data will be of high quality. If we are to build applications that work in hospitals without significant operational changes in care delivery, algorithms should be designed to cope with the available data in the best possible way. In a clinical environment, imaging protocols are highly flexible, with MRI sequences commonly missing appropriate sequence labeling (e.g. T1, T2, FLAIR). To this end we introduce PIMMS, a Permutation Invariant Multi-Modal Segmentation technique that is able to perform inference over sets of MRI scans without using modality labels. We present results which show that our convolutional neural network can, in some settings, outperform a baseline model which utilizes modality labels, and achieve comparable performance otherwise.
Original languageEnglish
Title of host publicationDeep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 4th International Workshop, DLMIA 2018 and 8th International Workshop, ML-CDS 2018 Held in Conjunction with MICCAI 2018
EditorsLena Maier-Hein, Tanveer Syeda-Mahmood, Zeike Taylor, Zhi Lu, Danail Stoyanov, Anant Madabhushi, João Manuel R.S. Tavares, Jacinto C. Nascimento, Mehdi Moradi, Anne Martel, Joao Paulo Papa, Sailesh Conjeti, Vasileios Belagiannis, Hayit Greenspan, Gustavo Carneiro, Andrew Bradley
PublisherSpringer Verlag
Pages201-209
Volume11045 LNCS
ISBN (Print)9783030008888
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018 and 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018 Held in Conjunction with MICCAI 2018 - Granada, Spain
Duration: 20 Sep 201820 Sep 2018

Publication series

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

Conference

Conference4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018 and 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018 Held in Conjunction with MICCAI 2018
CountrySpain
CityGranada
Period20/09/201820/09/2018

Cite this

Varsavsky, T., Eaton-Rosen, Z., Sudre, C. H., Nachev, P., & Cardoso, M. J. (2018). Pimms: Permutation invariant multi-modal segmentation. In L. Maier-Hein, T. Syeda-Mahmood, Z. Taylor, Z. Lu, D. Stoyanov, A. Madabhushi, J. M. R. S. Tavares, J. C. Nascimento, M. Moradi, A. Martel, J. P. Papa, S. Conjeti, V. Belagiannis, H. Greenspan, G. Carneiro, ... A. Bradley (Eds.), Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 4th International Workshop, DLMIA 2018 and 8th International Workshop, ML-CDS 2018 Held in Conjunction with MICCAI 2018 (Vol. 11045 LNCS, pp. 201-209). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Springer Verlag. https://doi.org/10.1007/978-3-030-00889-5_23