@inproceedings{2e58bd3faf244c7cbad5d5e8848401f8,
title = "Uncertainty-Based Segmentation of Myocardial Infarction Areas on Cardiac MR Images",
abstract = "Every segmentation task is uncertain due to image resolution, artefacts, annotation protocol etc. Propagating those uncertainties in a segmentation pipeline can improve the segmentation. This article aims to assess if segmentation can benefit from uncertainty of an auxiliary unsupervised task - the reconstruction of the input image. This auxillary task could help the network focus on rare examples that are otherwise poorly segmented. The method was applied to segmentation of myocardial infarction areas on cardiac magnetic resonance images.",
author = "Robin Camarasa and Alexis Faure and Thomas Crozier and Daniel Bos and {de Bruijne}, Marleen",
year = "2021",
doi = "10.1007/978-3-030-68107-4_40",
language = "English",
isbn = "9783030681067",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "385--391",
editor = "{Puyol Anton}, Esther and Mihaela Pop and Maxime Sermesant and Victor Campello and Alain Lalande and Karim Lekadir and Avan Suinesiaputra and Oscar Camara and Alistair Young",
booktitle = "Statistical Atlases and Computational Models of the Heart. MandMs and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers",
address = "Switzerland",
note = "11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020 ; Conference date: 04-10-2020 Through 04-10-2020",
}