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arXiv · 2109.01887

Weakly supervised semantic segmentation of tomographic images in the diagnosis of stroke

Anna Dobshik·Andrey Tulupov·Vladimir Berikov
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Abstract

This paper presents an automatic algorithm for the segmentation of areas affected by an acute stroke on the non-contrast computed tomography brain images. The proposed algorithm is designed for learning in a weakly supervised scenario when some images are labeled accurately, and some images are labeled inaccurately. Wrong labels appear as a result of inaccuracy made by a radiologist in the process of manual annotation of computed tomography images. We propose methods for solving the segmentation problem in the case of inaccurately labeled training data. We use the U-Net neural network architecture with several modifications. Experiments on real computed tomography scans show that the proposed methods increase the segmentation accuracy.

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Anna Dobshik, Andrey Tulupov, Vladimir Berikov. 2021-09-04. Weakly supervised semantic segmentation of tomographic images in the diagnosis of stroke. https://doi.org/10.1088/1742-6596/2099/1/012021

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