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.
@article{arxiv.2109.01887,
title = {Weakly supervised semantic segmentation of tomographic images in the diagnosis of stroke},
author = {Anna Dobshik and Andrey Tulupov and Vladimir Berikov},
journal= {arXiv preprint arXiv:2109.01887},
year = {2021}
}