Mr. Ahmer Asif Profile

Ahmer Asif

at Univ of Oklahoma Health Sciences Ctr

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Proceedings Article | 15 February 2021 Poster + Paper

Applying quantitative image markers to predict clinical measures after aneurysmal subarachnoid hemorrhage

Gopichandh Danala, Masoom Desai, Maryum Shoukat, Ahmer Asif, Morteza Heidari, Bin Zheng

Proceedings Volume 11597, 115972Z (2021) https://doi.org/10.1117/12.2580976

KEYWORDS: Computed tomography, Image segmentation, Computer aided diagnosis and therapy, Neuroimaging, Machine learning, Cognitive modeling, Brain, Blood, Solid modeling, Performance modeling

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Brain computed tomography (CT) images have been routinely used by neuroradiologists in diagnosis of aneurysmal subarachnoid hemorrhage (aSAH). The purpose of this study is to develop a computer-aided detection (CAD) scheme to generate quantitative image markers computed from CT images to predict various clinical measures after aSAH. A CT image dataset involving 59 aSAH patients was retrospectively collected and used. For each patient, non-contrast CT acquired during admission into hospital is used for this study. From each CT image set, CAD scheme segments intracranial brain region, and labels each CT voxel into one of four regions namely, cerebrospinal fluid, white matter, grey matter, and leaked blood. For image slices above the level of the lateral ventricles, cerebrospinal fluid regions are also defined as sulci regions. Nest, CAD scheme computes 9 image features related to the volumes of the segmented sulci, blood, white and gray matter, as well as their ratios. We then built machine learning (ML) models by fusion of these features to predict 5 clinical measures including Delayed Cerebral Ischemia, Clinical Vasospasm, Ventriculoperitoneal Shunting, Modified Rankin Scale and Montreal Cognitive Assessment to assess prognosis of aSAH patients. Based on a leave-one-case-out cross-validation method, ML models yield performance of predicting the 5 selected clinical measures with the areas under ROC curves (AUC) ranging from 0.658 to 0.825. Study results demonstrate the promising feasibility of applying CAD-based image processing and machine learning method to generate valuably quantitative image markers and potential to assist clinicians optimally diagnosing and treating aSAH patients.

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