This paper presents an automatic algorithm for the segmentation of areas affected by an acute stroke in 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.
|Journal||Journal of Physics: Conference Series|
|Publication status||Published - 13 Dec 2021|
|Event||International Conference on Marchuk Scientific Readings 2021, MSR 2021 - Novosibirsk, Virtual, Russian Federation|
Duration: 4 Oct 2021 → 8 Oct 2021
- 1.03 PHYSICAL SCIENCES AND ASTRONOMY