2D-CNN Based Segmentation of Ischemic Stroke Lesions in MRI Scans

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Shah, Pir Masoom, Khan, Hikmat, Shafi, Uferah, Islam, Saif ul, Raza, Mohsin, Son, Tran The and Le-Minh, Hoa (2020) 2D-CNN Based Segmentation of Ischemic Stroke Lesions in MRI Scans. In: Advances in Computational Collective Intelligence - 12th International Conference, ICCCI 2020, Proceedings. Communications in Computer and Information Science . Springer, VNM, pp. 276-286. ISBN 9783030631185

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Abstract

Stroke is the second overall driving reason for human death and disability. Strokes are categorized into Ischemic and Hemorrhagic strokes. Ischemic stroke is 85% of strokes while hemorrhagic is 15%. An exact automatic lesion segmentation of ischemic stroke remains a test to date. A few machine learning techniques are applied previously to beat manual human observers yet slacks to survive. In this paper, we propose a completely automatic lesion segmentation of ischemic stroke in view of the Convolutional Neural Network (CNN). The dataset used as a part of this study is obtained from ISLES 2015 challenge, included four MRI modalities DWI, T1, T1c, and FLAIR of 28 patients. The CNN model is trained on 25 patient’s data while tested on the remaining 3 patients. As CNN is only used for classification, we convert segmentation to the pixel-by-pixel classification tasks. Dice Coefficient (DC) is used as a performance evaluation metric for assessing the performance of the model. The experimental results show that the proposed model achieves a comparatively higher DC rate from 4–5% than the considered state-of-the-art machine learning techniques.

Item Type:	Book Section
Additional Information:	Publisher Copyright: © 2020, Springer Nature Switzerland AG.
Uncontrolled Keywords:	convolutional neural network,deep learning,mri,stroke,computer science(all),mathematics(all) ,/dk/atira/pure/subjectarea/asjc/1700
Faculty \ School:	Faculty of Science > School of Computing Sciences
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	16 Jun 2025 10:31
Last Modified:	17 Jun 2025 06:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/99547
DOI:	10.1007/978-3-030-63119-2_23

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