Deep Active Learning for Left Ventricle Segmentation in Echocardiography

Alajrami, Eman, Naidoo, Preshen, Jevsikov, Jevgeni, Lane, Elisabeth, Pordoy, Jamie, Serej, Nasim Dadashi, Azarmehr, Neda, Dinmohammadi, Fateme, Shun-shin, Matthew J., Francis, Darrel P. and Zolgharni, Massoud (2023) Deep Active Learning for Left Ventricle Segmentation in Echocardiography. In: Functional Imaging and Modeling of the Heart, 19-22 June 2023, Lyon, France.

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The training of advanced deep learning algorithms for medical image interpretation requires precisely annotated datasets, which is laborious and expensive. Therefore, this research investigates state-of-the-art active learning methods for utilising limited annotations when performing automated left ventricle segmentation in echocardiography. Our experiments reveal that the performance of different sampling strategies varies between datasets from the same domain. Further, an optimised method for representativeness sampling is introduced, combining images from feature-based outliers to the most representative samples for label acquisition. The proposed method significantly outperforms the current literature and demonstrates convergence with minimal annotations. We demonstrate that careful selection of images can reduce the number of images needed to be annotated by up to 70%. This research can therefore present a cost-effective approach to handling datasets with limited expert annotations in echocardiography.

Item Type: Conference or Workshop Item (Poster)
Identifier: 10.1007/978-3-031-35302-4_29
Page Range: pp. 283-291
Identifier: 10.1007/978-3-031-35302-4_29
Depositing User: Massoud Zolgharni
Date Deposited: 17 Jun 2023 10:06
Last Modified: 17 Jun 2023 10:06

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