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Proceedings Paper

Observer variation-aware medical image segmentation by combining deep learning and surrogate-assisted genetic algorithms
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Paper Abstract

There has recently been great progress in automatic segmentation of medical images with deep learning algorithms. In most works observer variation is acknowledged to be a problem as it makes training data heterogeneous but so far no attempts have been made to explicitly capture this variation. Here, we propose an approach capable of mimicking different styles of segmentation, which potentially can improve quality and clinical acceptance of automatic segmentation methods. In this work, instead of training one neural network on all available data, we train several neural networks on subgroups of data belonging to different segmentation variations separately. Because a priori it may be unclear what styles of segmentation exist in the data and because different styles do not necessarily map one-on-one to different observers, the subgroups should be automatically determined. We achieve this by searching for the best data partition with a genetic algorithm. Therefore, each network can learn a specific style of segmentation from grouped training data. We provide proof of principle results for open-sourced prostate segmentation MRI data with simulated observer variations. Our approach provides an improvement of up to 23% (depending on simulated variations) in terms of Dice and surface Dice coefficients compared to one network trained on all data.

Paper Details

Date Published: 10 March 2020
PDF: 11 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113131B (10 March 2020); doi: 10.1117/12.2547459
Show Author Affiliations
Arkadiy Dushatskiy, Ctr. Wiskunde & Informatica (Netherlands)
Adriënne M. Mendrik, Netherlands eScience Ctr. (Netherlands)
Peter A. N. Bosman, Ctr. Wiskunde & Informatica (Netherlands)
Delft Univ. of Technology (Netherlands)
Tanja Alderliesten, Amsterdam Univ. Medical Ctr., Univ. of Amsterdam (Netherlands)

Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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