Medical Image Analysis Methods for Accurate Segmentation and Quantification of Acute-Subacute Ischemic Stroke Lesion — A Systematic Review

A medical emergency, acute ischemic stroke necessitates immediate treatment as the longer the neural is without blood flow, the greater the risk of irreversible neural damage and disability. Depending on the extent, the lesion may vary in size and location and location of the blood flow blockage, and can have significant effects on a person’s cognitive, motor, and sensory function. The process of identifying and classifying regions of affected neural tissue during a ischemic stroke is referred to as “ischemic stroke lesion segmentation.” Medical imaging techniques like computed tomography (CT) and magnetic resonance imaging (MRI) are typically used to accomplish this specialized software tools. The goal of segmentation is to provide a detailed and accurate map of the extent and location of the stroke damage, which can be used to guide treatment decisions and monitor the patient’s recovery. Although manual segmentation is regarded as the most accurate method, it takes time and is subject to variability between observers, which leads to inconsistent results. There are a few unique ways to deal with dividing ischemic stroke sores, ranging from manual delineation by trained clinician to fully automated algorithms that use machine learning and other advanced techniques. Automated segmentation methods are faster and more objective, but they may require large training sets and can be sensitive to imaging artifacts and other sources of noise. Although fully automatic methods hold promise, semi-automatic methods remain the preferred approach in clinical research. We performed a systematic review of the literature to explore the latest advancements and trends in analyzing ischemic stroke lesions using automated methods developed in the past five years. Our search of IEEE explores, Springer, Science Direct, Taylor & Francis, etc. yielded 1580 papers, from which we selected 50 for detailed analysis. Of these studies, 12 employed supervised segmentation, 12 used unsupervised segmentation and 27 employed deep learning segmentation methods. Only a limited number of studies have validated their fully automatic methods using longitudinal samples, and a mere eight studies included validation using clinical parameters. Furthermore, only 23 of the 50 studies made their methods publicly available. To advance the field, there is a need for fully automatic methods that are validated with longitudinal samples and clinical parameters. Moreover, making methods publicly available is essential for promoting reproducibility and facilitating comparison of results.