Although volumetric assessment of intracerebral hemorrhage (ICH) plays a key role for clinicians to make optimal treatment decisions and predict prognosis of ICH patients, qualitative assessment of neuroradiologists in reading brain CT images is not accurate and has large interreader variability. To overcome this clinical challenge, this study develops and tests a new interactive computer-aided detection (ICAD) tool to quantitatively assess hemorrhage volumes. A retrospectively assembled dataset including 200 patients with ICH was collected for this study. After loading each case, the ICAD tool first segments intracranial brain volume, performs CT labelling of each voxel, then contour-guided image-thresholding techniques based on CT Hounsfield Unit is used to estimate and segment hemorrhage-associated voxels (ICH). Next, two experienced neurology residents examine and corrects the markings of ICH categorized into either intraparenchymal hemorrhage (IPH) or intraventricular hemorrhage (IVH) to obtain the true markings. Additionally, volumes and maximum two-dimensional diameter of each sub-type of hemorrhage were also computed for understanding ICH prognosis. The performance to segment hemorrhage regions between semi-automated ICAD and the verified neurology residents’ true markings was evaluated using dice similarity coefficient (DSC). Data analysis results show that median and [interquartile range] of DSC are 0.96 [0.91, 0.98], 0.97 [0.93, 0.99], 0.92 [0.83, 0.97] for ICH, IPH and IVH, respectively. Thus, this study demonstrates that the new ICAD tool enables to segment and quantify ICH and other hemorrhage volume with higher DSC, which has potential to quantify ICH in future clinical practice.
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.
Advent of advanced imaging technology and better neuro-interventional equipment have resulted in timely diagnosis and effective treatment for acute ischemic stroke (AIS) due to large vessel occlusion (LVO). However, objective clinicoradiologic correlate to identify appropriate candidates and their respective clinical outcome is largely unknown. The purpose of the study is to develop and test a new interactive decision-making support tool to predict severity of AIS prior to thrombectomy using CT perfusion imaging protocol. CT image data of 30 AIS patients with LVO assessed radiologically for their eligibility to undergo mechanical thrombectomy were retrospectively collected and analyzed in this study. First, a computer-aided scheme automatically categorizes images into multiple sequences followed by indexing each slice to specified brain location. Next, consecutive mapping is used for accurate brain region segmentation from skull. The brain is then split into left and right hemispheres, followed by detecting blood in each hemisphere. Additionally, visual tools including segmentation, blood correction, select sequence and index analyzer are implemented for deeper analysis. Last, comparison between blood-volume in each hemisphere over the sequences is made to observe wash-in and wash-out rate of blood flow to assess the extent of damaged and “at risk” brain tissue. By integrating computer-aided scheme into a user graphic interface, the study builds a unique image feature analysis and visualization tool to observe and quantify the delayed or reduced blood flow (brain “at-risk” to develop AIS) in the corresponding hemisphere, which has potential to assist radiologists to quickly visualize and more accurately assess extent of AIS.
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