The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images
Ugurlu D, Puyol-Antón E, Ruijsink B, Young A, Machado I, Hammernik K, King AP, Schnabel JA (2022)
Publication Type: Conference contribution
Publication year: 2022
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 13131 LNCS
Pages Range: 57-65
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Strasbourg, FRA
ISBN: 9783030937218
DOI: 10.1007/978-3-030-93722-5_7
Abstract
Domain shift refers to the difference in the data distribution of two datasets, normally between the training set and the test set for machine learning algorithms. Domain shift is a serious problem for generalization of machine learning models and it is well-established that a domain shift between the training and test sets may cause a drastic drop in the model’s performance. In medical imaging, there can be many sources of domain shift such as different scanners or scan protocols, different pathologies in the patient population, anatomical differences in the patient population (e.g. men vs women) etc. Therefore, in order to train models that have good generalization performance, it is important to be aware of the domain shift problem, its potential causes and to devise ways to address it. In this paper, we study the effect of domain shift on left and right ventricle blood pool segmentation in short axis cardiac MR images. Our dataset contains short axis images from 4 different MR scanners and 3 different pathology groups. The training is performed with nnUNet. The results show that scanner differences cause a greater drop in performance compared to changing the pathology group, and that the impact of domain shift is greater on right ventricle segmentation compared to left ventricle segmentation. Increasing the number of training subjects increased cross-scanner performance more than in-scanner performance at small training set sizes, but this difference in improvement decreased with larger training set sizes. Training models using data from multiple scanners improved cross-domain performance.
Involved external institutions
United Kingdom (GB)
Germany (DE)How to cite
APA:
Ugurlu, D., Puyol-Antón, E., Ruijsink, B., Young, A., Machado, I., Hammernik, K.,... Schnabel, J.A. (2022). The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images. In Esther Puyol Antón, Alistair Young, Avan Suinesiaputra, Mihaela Pop, Carlos Martín-Isla, Maxime Sermesant, Oscar Camara, Karim Lekadir (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 57-65). Strasbourg, FRA: Springer Science and Business Media Deutschland GmbH.
MLA:
Ugurlu, Devran, et al. "The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images." Proceedings of the 12th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2021 held in conjunction with MICCAI 2021, Strasbourg, FRA Ed. Esther Puyol Antón, Alistair Young, Avan Suinesiaputra, Mihaela Pop, Carlos Martín-Isla, Maxime Sermesant, Oscar Camara, Karim Lekadir, Springer Science and Business Media Deutschland GmbH, 2022. 57-65.
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