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Comparison of Semi- and Un-Supervised Domain Adaptation Methods for Whole-Heart Segmentation

Muffoletto M, Xu H, Barbaroux H, Kunze KP, Neji R, Botnar R, Prieto C, Rueckert D, Young A (2022)

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Publication Type: Conference contribution

Publication year: 2022

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 13593 LNCS

Pages Range: 91-100

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Singapore, SGP

ISBN: 9783031234422

DOI: 10.1007/978-3-031-23443-9_9

Abstract

Quantification of heart geometry is important in the clinical diagnosis of cardiovascular diseases. Changes in geometry are indicative of remodelling processes as the heart tissue adapts to disease. Coronary Computed Tomography Angiography (CCTA) is considered a first line tool for patients at low or intermediate risk of coronary artery disease, while Coronary Magnetic Resonance Angiography (CMRA) is a promising alternative due to the absence of radiation-induced risks and high performance in the evaluation of cardiac geometry. Yet, the accuracy of an image-based diagnosis is susceptible to the quality of volume segmentations. Deep Learning (DL) techniques are gradually being adopted to perform such segmentations and substitute the tedious and manual work performed by physicians. However, practical applications of DL techniques on a large scale are still limited due to their poor adaptability across modalities and patients. Hence, the aim of this work was to develop a pipeline to perform automatic heart segmentation of multiple cardiac imaging scans, addressing the domain shift between MRs (target) and CTs (source). We trained two Domain Adaptation (DA) methods, using Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs), following different training routines, which we refer to as un- and semi- supervised approaches. We also trained a baseline supervised model following state-of-the-art choice of parameters and augmentation. The results showed that DA methods can be significantly boosted by the addition of a few supervised cases, increasing Dice and Hausdorff distance metrics across the main cardiac structures.

Involved external institutions

How to cite

APA:

Muffoletto, M., Xu, H., Barbaroux, H., Kunze, K.P., Neji, R., Botnar, R.,... Young, A. (2022). Comparison of Semi- and Un-Supervised Domain Adaptation Methods for Whole-Heart Segmentation. In Oscar Camara, Esther Puyol-Antón, Avan Suinesiaputra, Alistair Young, Chen Qin, Maxime Sermesant, Shuo Wang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 91-100). Singapore, SGP: Springer Science and Business Media Deutschland GmbH.

MLA:

Muffoletto, Marica, et al. "Comparison of Semi- and Un-Supervised Domain Adaptation Methods for Whole-Heart Segmentation." Proceedings of the 13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Singapore, SGP Ed. Oscar Camara, Esther Puyol-Antón, Avan Suinesiaputra, Alistair Young, Chen Qin, Maxime Sermesant, Shuo Wang, Springer Science and Business Media Deutschland GmbH, 2022. 91-100.

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