Automated infaction core delineation

Tato práce se zabývá návrhem metody, která by mohla lékařům poskytnout dodatečné informace při rozhodování o nejvhodnější léčbě pro pacienty s ischemickou cévní mozkovou příhodou. Navrhovaná metoda je automatická a dokáže označit nekrotickou tkáň na snímcích, které vznikly kombinací angiografického a nekontrastního vyšetření počítačovou tomografií. Tato kombinace dokáže pokrýt oblast celého mozku. Objem nekrotické tkáně je kritickým faktorem pro aplikaci trombolytické léčby. Takový parametr nemají lékaři v současné době k dispozici. Lidem, kteří přesáhnou určitou hodnotu objemu nekrotické tkáně, by neměla být aplikována trombolytická léčba, neboť je vysoké riziko dalšího krvácení.Katedra informatiky a výpočetní technikyObhájenoThis thesis deals with methods that could provide additional information for physicians when deciding about the most appropriate treatment for patients with ischemic strokes. The thesis proposes a method for automated infarction core delineation based on combination of angiography and non contract computed tomography examinations. Such perfused blood volume mapping has advantage in whole brain coverage. The volume of the infarction core area is the crucial factor for thrombolytic treatment indication. Such information is not yet available for physicians. Patients who exceed certain level of the infarction core volume should not be indicated for thrombolytic treatment because of high risk of further bleeding

Cerebral attenuation on single-phase CT angiography source images: Automated ischemia detection and morphologic outcome prediction after thrombectomy in patients with ischemic stroke

Objectives: Stroke triage using CT perfusion (CTP) or MRI gained importance after successful application in recent trials on late-window thrombectomy but is often unavailable and time-consuming. We tested the clinical value of software-based analysis of cerebral attenuation on Single-phase CT angiography source images (CTASI) as CTP surrogate in stroke patients. Methods: Software-based automated segmentation and Hounsfield unit (HU) measurements for all regions of the Alberta Stroke Program Early CT Score (ASPECTS) on CTASI were performed in patients with large vessel occlusion stroke who underwent thrombectomy. To normalize values, we calculated relative HU (rHU) as ratio of affected to unaffected hemisphere. Ischemic regions, regional ischemic core and final infarction were determined on simultaneously acquired CTP and follow-up imaging as ground truth. Receiver operating characteristics analysis was performed to calculate the area-under-the-curve (AUC). Resulting cut-off values were used for comparison with visual analysis and to calculate an 11-point automated CTASI ASPECTS. Results: Seventy-nine patients were included. rHU values enabled significant classification of ischemic involvement on CTP in all ten regions of the ASPECTS (each p<0.001, except M4-cortex p = 0.002). Classification of ischemic core and prediction of final infarction had best results in subcortical regions but produced lower AUC values with significant classification for all regions except M1, M3 and M5. Relative total hemispheric attenuation provided strong linear correlation with CTP total ischemic volume. Automated classification of regional ischemia on CTASI was significantly more accurate in most regions and provided better agreement with CTP cerebral blood flow ASPECTS than visual assessment. Conclusions: Automated attenuation measurements on CTASI provide excellent performance in detecting acute ischemia as identified on CTP with improved accuracy compared to visual analysis. However, value for the approximation of ischemic core and morphologic outcome in large vessel occlusion stroke after thrombectomy was regionally dependent and limited. This technique has the potential to facilitate stroke imaging as sensitive surrogate for CTP-based ischemia

Automated measurement of net water uptake from baseline and follow-up CTs in patients with large vessel occlusion stroke

Quantifying the extent and evolution of cerebral edema developing after stroke is an important but challenging goal. Lesional net water uptake (NWU) is a promising CT-based biomarker of edema, but its measurement requires manually delineating infarcted tissue and mirrored regions in the contralateral hemisphere. We implement an imaging pipeline capable of automatically segmenting the infarct region and calculating NWU from both baseline and follow-up CTs of large-vessel occlusion (LVO) patients. Infarct core is extracted from CT perfusion images using a deconvolution algorithm while infarcts on follow-up CTs were segmented from non-contrast CT (NCCT) using a deep-learning algorithm. These infarct masks were flipped along the brain midline to generate mirrored regions in the contralateral hemisphere of NCCT; NWU was calculated as one minus the ratio of densities between regions, removing voxels segmented as CSF and with HU outside thresholds of 20-80 (normal hemisphere and baseline CT) and 0-40 (infarct region on follow-up). Automated results were compared with those obtained using manually-drawn infarcts and an ASPECTS region-of-interest based method that samples densities within the infarct and normal hemisphere, using intraclass correlation coefficient (ρ). This was tested on serial CTs from 55 patients with anterior circulation LVO (including 66 follow-up CTs). Baseline NWU using automated core was 4.3% (IQR 2.6-7.3) and correlated with manual measurement (ρ = 0.80

A Review on Computer Aided Diagnosis of Acute Brain Stroke.

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas

3D Convolutional Neural Network Applied to CT Angiography in the Detection of an Acute Ischemic Stroke

Peer reviewe

Computed tomography in acute ischemic stroke

Stroke remains the third most important cause of mortality in industrialized countries; this has prompted research for improvements in both diagnostic and therapeutic strategies for patients with signs of acute cerebral ischemia. Over the last decade, there has been a parallel in progress in techniques in both diagnostic and therapeutic options. While previously only used for excluding hemorrhage, imaging now has the possibility to detect ischemia, vascular occlusion, as well as detect tissue at risk in one setting. It should also allow to monitor treatment and predict/exclude therapeutic complications. Parallel to advances in magnetic resonance imaging of stroke, computed tomography has improved immensely over the last decade due to the development of CT scanners that are faster and that allow to acquire studies such as CT perfusion or CT angiography in a reliable way. CT can detect many signs that might help us detect impending signs of massive infarction, but we still lack the experience to use these alone to prevent a patient from benefitting from possible therap

Automatic segmentation of cerebral infarcts in follow-up computed tomography images with convolutional neural networks

Background and purpose: Infarct volume is a valuable outcome measure in treatment trials of acute ischemic stroke and is strongly associated with functional outcome. Its manual volumetric assessment is, however, too demanding to be implemented in clinical practice. Objective: To assess the value of convolutional neural networks (CNNs) in the automatic segmentation of infarct volume in follow-up CT images in a large population of patients with acute ischemic stroke. Materials and methods: We included CT images of 1026 patients from a large pooling of patients with acute ischemic stroke. A reference standard for the infarct segmentation was generated by manual delineation. We introduce three CNN models for the segmentati