Alquhayz, Hani Tufail, Hafiz Zahid Raza, Basit 2024-01-19T10:32:16Z 2024-01-19T10:32:16Z 2023-02-03 2022-12 0010-4825 https://pubs.kist.re.kr/handle/201004/114204 Ischemic and hemorrhagic strokes are two major types of internal brain injury. 3D brain MRI is suggested by neurologists to examine the brain. Manual examination of brain MRI is very sensitive and time-consuming task. However, automatic diagnosis can assist doctors in this regard. Anatomical Tracings of Lesions After Stroke (ATLAS) is publicly available dataset for research experiments. One of the major issues in medical imaging is class imbalance. Similarly, pixel representation of stroke lesion is less than 1% in ATLAS. Second major challenge in this dataset is inter-class similarity. A multi-level classification network (MCN) is proposed for segmentation of ischemic stroke lesions. MCN consists of three cascaded discrete networks. The first network designed to reduce the slice level class imbalance, where a classifier model is trained to extract the slices of stroke lesions from a whole brain MRI volume. The interclass similarity cause to produce false positives in segmented output. Therefore, all extracted stroke slices were divided into overlapping patches (64 x 64) and carried to the second network. The task associated with second network is to classify the patches comprises of stroke lesion. The third network is a 2D modified residual U-Net that segments out the stroke lesions from the patches extracted by the second network. MCN achieved 0.754 mean dice score on test dataset which is higher than the other state-of-the-art methods on the same dataset. English Pergamon Press Ltd. The multi-level classification network (MCN) with modified residual U-Net for ischemic stroke lesions segmentation from ATLAS Article 10.1016/j.compbiomed.2022.106332 1 Computers in Biology and Medicine, v.151 Computers in Biology and Medicine 151 N scie scopus 000900924100001 2-s2.0-85142429309 Biology Computer Science, Interdisciplinary Applications Engineering, Biomedical Mathematical & Computational Biology Life Sciences & Biomedicine - Other Topics Computer Science Engineering Mathematical & Computational Biology Article GLOBAL BURDEN BRAIN DISEASE MRI Brain stroke segmentation ATLAS Residual U -Net Multi -level classification (MCN)