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.
@article{arxiv.1807.06537,
title = {PIMMS: Permutation Invariant Multi-Modal Segmentation},
author = {Thomas Varsavsky and Zach Eaton-Rosen and Carole H. Sudre and Parashkev Nachev and M. Jorge Cardoso},
journal= {arXiv preprint arXiv:1807.06537},
year = {2018}
}
Comments
Accepted at the 4th Workshop on Deep Learning in Medical Image Analysis held at MICCAI2018