Machine Learning Algorithms Versus Thresholding to Segment Ischemic Regions in Patients With Acute Ischemic Stroke

Abstract

Objective: Computed tomography (CT) scan is a fast and widely used modality for early assessment in patients with symptoms of a cerebral ischemic stroke. CT perfusion (CTP) is often added to the protocol and is used by radiologists for assessing the severity of the stroke. Standard parametric maps are calculated from the CTP datasets. Based on parametric value combinations, ischemic regions are separated into presumed infarct core (irreversibly damaged tissue) and penumbra (tissue-at-risk). Different thresholding approaches have been suggested to segment the parametric maps into these areas. The purpose of this study is to compare fully-automated methods based on machine learning and thresholding approaches to segment the hypoperfused regions in patients with ischemic stroke. Methods: We test two different architectures with three mainstream machine learning algorithms. We use parametric maps as input features, and manual annotations made by two expert neuroradiologists as ground truth. Results: The best results are produced with random forest (RF) and Single-Step approach; we achieve an average Dice coefficient of 0.68 and 0.26, respectively for penumbra and core, for the three groups analysed. We also achieve an average in volume difference of 25.1ml for penumbra and 7.8ml for core. Conclusions: Our best RFbased method outperforms the classical thresholding approaches, to segment both the ischemic regions in a group of patients regardless of the severity of vessel occlusion. Significance: A correct visualization of the ischemic regions will guide treatment decisions better.

Exploiting 4D CT Perfusion for segmenting infarcted areas in patients with suspected acute ischemic stroke

“…MRI performed during the first days after hospital admission was also utilized. Details for the manual annotations have been described [36] previously.…”

“…However, an appropriate method is still required to simultaneously handle the spatio-temporal information for segmenting both the ischemic core and penumbra regions. An understanding of the extension of the penumbra during the first stages of the ischemic stroke is crucial for treatment decision [35,36]. To the best of our knowledge, Tomasetti et al was the first research group to segment both core and penumbra regions using machine learning [36] or DNN approaches [32,37].…”

“…An understanding of the extension of the penumbra during the first stages of the ischemic stroke is crucial for treatment decision [35,36]. To the best of our knowledge, Tomasetti et al was the first research group to segment both core and penumbra regions using machine learning [36] or DNN approaches [32,37].…”

Clinical investigation of sensing ailments in brain movement through physiological activity using ML technique

“…According to L. Tomasetti [3], radiologists frequently add CT perfusion to the technique to determine the complexity of strokes. This CTP data is used to construct conventional parameterized mappings.…”

“…Brain connections related with cognitive skills, spontaneously activity, including neurological conditions have indeed been studied using brain functional connectedness. Aside from ambulatory EEG, that is primarily used it for seizures identification and quantitation, mainly a few wearables for brain appropriate steps to prevent on relatively close spectroscopic are available [1][2][3]. ML is the way to teach a machine using its prior expertise to fix a problem.…”