Burden of intracranial artery calcification in white patients with ischemic stroke.

INTRODUCTION: The diagnostic workup of stroke doesn't identify an underlying cause in two-fifths of ischemic strokes. Intracranial arteriosclerosis is acknowledged as a cause of stroke in Asian and Black populations, but is underappreciated as such in whites. We explored the burden of Intracranial Artery Calcification (IAC), a marker of intracranial arteriosclerosis, as a potential cause of stroke among white patients with recent ischemic stroke or TIA. PATIENTS AND METHODS: Between December 2005 and October 2010, 943 patients (mean age 63.8 (SD ± 14.0) years, 47.9% female) were recruited, of whom 561 had ischemic stroke and 382 a TIA. CT-angiography was conducted according to stroke analysis protocols. The burden of IAC was quantified on these images, whereafter we assessed the presence of IAC per TOAST etiology underlying the stroke and assessed associations between IAC burden, symptom severity, and short-term functional outcome. RESULTS: IAC was present in 62.4% of patients. Furthermore, IAC was seen in 84.8% of atherosclerotic strokes, and also in the majority of strokes with an undetermined etiology (58.5%). Additionally, patients with larger IAC burden presented with heavier symptoms (adjusted OR 1.56 (95% CI [1.06-2.29]), but there was no difference in short-term functional outcome (1.14 [0.80-1.61]). CONCLUSION: IAC is seen in the majority of white ischemic stroke patients, aligning with findings from patient studies in other ethnicities. Furthermore, over half of patients with a stroke of undetermined etiology presented with IAC. Assessing IAC burden may help identify the cause in ischemic stroke of undetermined etiology, and could offer important prognostic information.

Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021.

Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1-EPVS, 9 for Task 2-Microbleeds and 6 for Task 3-Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1-EPVS and Task 2-Microbleeds and not practically useful results yet for Task 3-Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level.

Nested star-shaped objects segmentation using diameter annotations.

Most current deep learning based approaches for image segmentation require annotations of large datasets, which limits their application in clinical practice. We observe a mismatch between the voxelwise ground-truth that is required to optimize an objective at a voxel level and the commonly used, less time-consuming clinical annotations seeking to characterize the most important information about the patient (diameters, counts, etc.). In this study, we propose to bridge this gap for the case of multiple nested star-shaped objects (e.g., a blood vessel lumen and its outer wall) by optimizing a deep learning model based on diameter annotations. This is achieved by extracting in a differentiable manner the boundary points of the objects at training time, and by using this extraction during the backpropagation. We evaluate the proposed approach on segmentation of the carotid artery lumen and wall from multisequence MR images, thus reducing the annotation burden to only four annotated landmarks required to measure the diameters in the direction of the vessel's maximum narrowing. Our experiments show that training based on diameter annotations produces state-of-the-art weakly supervised segmentations and performs reasonably compared to full supervision. We made our code publicly available at https://gitlab.com/radiology/aim/carotid-artery-image-analysis/nested-star-shaped-objects.

Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge.

A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization therapy. Delayed enhancement-MRI or DE-MRI, which is performed 10 min after injection of the contrast agent, provides high contrast between viable and nonviable myocardium and is therefore a method of choice to evaluate the extent of MI. To automatically assess myocardial status, the results of the EMIDEC challenge that focused on this task are presented in this paper. The challenge's main objectives were twofold. First, to evaluate if deep learning methods can distinguish between non-infarct and pathological exams, i.e. exams with or without hyperenhanced area. Second, to automatically calculate the extent of myocardial infarction. The publicly available database consists of 150 exams divided into 50 cases without any hyperenhanced area after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department. Along with MRI, clinical characteristics are also provided. The obtained results issued from several works show that the automatic classification of an exam is a reachable task (the best method providing an accuracy of 0.92), and the automatic segmentation of the myocardium is possible. However, the segmentation of the diseased area needs to be improved, mainly due to the small size of these areas and the lack of contrast with the surrounding structures.