Uncertainty-Based Segmentation of Myocardial Infarction Areas on Cardiac MR Images
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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.
Original language | English |
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Title of host publication | 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 |
Editors | Esther Puyol Anton, Mihaela Pop, Maxime Sermesant, Victor Campello, Alain Lalande, Karim Lekadir, Avan Suinesiaputra, Oscar Camara, Alistair Young |
Number of pages | 7 |
Publisher | Springer |
Publication date | 2021 |
Pages | 385-391 |
ISBN (Print) | 9783030681067 |
DOIs | |
Publication status | Published - 2021 |
Event | 11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020 - Lima, Peru Duration: 4 Oct 2020 → 4 Oct 2020 |
Conference
Conference | 11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020 |
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Land | Peru |
By | Lima |
Periode | 04/10/2020 → 04/10/2020 |
Series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 12592 LNCS |
ISSN | 0302-9743 |
ID: 258186465