Brain Stroke Lesion Segmentation Using Computed Tomography Images based on Modified U-Net Model with ResNet Blocks
DOI:
https://doi.org/10.3991/ijoe.v18i13.32881Keywords:
brain stroke, deep learning, U-Net, computed tomography, segmentationAbstract
Segmentation of brain regions affected by ischemic stroke helps to overcome the main obstacles in modern studies of stroke visualization. Unfortunately, contemporary methods of solving this problem using artificial intelligence methods are not optimal. Therefore, in the study we consider how to increase the efficiency of segmentation of the stroke focus using computer perfusion imaging using modifications based on UNet. The network was trained and tested using the ISLES 2018 dataset. The publication includes an analysis of the results obtained, as well as recommendations for future research. By choosing the appropriate model parameters, our approach can be easily applied to detect ischemic stroke. We present modified U-Net models with two ResNet blocks as U-Net+ ResNetblock 1 and U-Net +ResNetblock 2, as well as a modified UNet model. Due to the small number of images for training the model, the best results were obtained by applying data preprocessing and object representation approaches, as well as data normalization methods to avoid overfitting. The results show that the modified UNet model is superior to other models in terms of average distance and recall, that are significant parameters for segmentation of the stroke.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Azhar Tursynova, Batyrkhan Omarov, Aivar Sakhipov, Natalya Tukenova
This work is licensed under a Creative Commons Attribution 4.0 International License.
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution Licence (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
By submitting an article the author grants to this journal the non-exclusive right to publish it. The author retains the copyright and the publishing rights for his article without any restrictions.