[1C4] Deep literacy models for ischemic stroke lesion segmentation in medical images
R Vure
Vellore Institute of Technology, India
Ischemic stroke is a severe neurological complaint and a leading cause of death and disability worldwide. Accurate segmentation of stroke lesions in medical images such as magnetic resonance imaging (MRI) and computed tomography (CT) reviews is pivotal for opinion, treatment planning and prognostics. Common imaging modalities used for stroke opinion include MRI and CT reviews, able to image lesions at different complaint stages from acute to habitual. Three major public standard datasets for assessing stroke segmentation algorithms are ATLAS, ISLES and AISD, each with unique characteristics. Foundational deep-literacy infrastructures for medical image segmentation, including convolutional neural network (CNN)-grounded and motor-grounded models, are essential for stroke lesion segmentation. Recent inventions in conforming these infrastructures for stroke lesion segmentation across standard datasets have shown promising results. Loss functions and data accruals play a significant part in perfecting stroke lesion segmentation delicacy, colourful aspects related to stroke segmentation tasks, similar to previous knowledge, small lesions, and multi-modal emulsion, are discussed in the paper. The review outlines promising future exploration directions to support uninterrupted progress in automated stroke lesion segmentation.
