Data variation-aware medical image segmentation

Arkadiy Dushatskiy; Gerry Lowe; Peter A. N. Bosman; Tanja Alderliesten

doi:10.1117/12.2608611

4 April 2022 Data variation-aware medical image segmentation

Arkadiy Dushatskiy, Gerry Lowe, Peter A. N. Bosman, Tanja Alderliesten

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120322V (2022) https://doi.org/10.1117/12.2608611
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Deep learning algorithms have become the golden standard for segmentation of medical imaging data. In most works, the variability and heterogeneity of real clinical data is acknowledged to still be a problem. One way to automatically overcome this is to capture and exploit this variation explicitly. Here, we propose an approach that improves on our previous work in this area and explain how it potentially can improve clinical acceptance of (semi-)automatic segmentation methods. In contrast to a standard neural network that produces one segmentation, we propose to use a multi-path Unet network that produces multiple segmentation variants, presumably corresponding to the variations that reside in the dataset. Different paths of the network are trained on disjoint data subsets. Because a priori it may be unclear what variations exist in the data, the subsets should be automatically determined. This is achieved by searching for the best data partitioning with an evolutionary optimization algorithm. Because each network path can become more specialized when trained on a more homogeneous data subset, better segmentation quality can be achieved. In practical usage, various automatically produced segmentations can be presented to a medical expert, from which the preferred segmentation can be selected. In experiments with a real clinical dataset of CT scans with prostate segmentations, our approach provides an improvement of several percentage points in terms of Dice and surface Dice coefficients compared to when all network paths are trained on all training data. Noticeably, the largest improvement occurs in the upper part of the prostate that is known to be most prone to inter-observer segmentation variation.

Conference Presentation

Citation Download Citation

Arkadiy Dushatskiy, Gerry Lowe, Peter A. N. Bosman, and Tanja Alderliesten "Data variation-aware medical image segmentation", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120322V (4 April 2022); https://doi.org/10.1117/12.2608611

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