Magnetic resonance imaging (MRI) is an important imaging modality in stroke. Computer based automated medical image processing is increasingly finding its way into clinical routine. The Ischemic Stroke Lesion Segmentation (ISLES) challenge is a continuous effort to develop and identify benchmark methods for acute and sub-acute ischemic stroke lesion segmentation. Here we introduce an expert-annotated, multicenter MRI dataset for segmentation of acute to subacute stroke lesions (https://doi.org/10.5281/zenodo.7153326). This dataset comprises 400 multi-vendor MRI cases with high variability in stroke lesion size, quantity and location. It is split into a training dataset of n = 250 and a test dataset of n = 150. All training data is publicly available. The test dataset will be used for model validation only and will not be released to the public. This dataset serves as the foundation of the ISLES 2022 challenge (https://www.isles-challenge.org/) with the goal of finding algorithmic methods to enable the development and benchmarking of automatic, robust and accurate segmentation methods for ischemic stroke.

Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research.

This dataset provides vertebral segmentation masks for spine CT images and annotations of vertebral fractures or abnormalities per vertebral level; it is available from https://osf.io/nqjyw/ and is... Published under a CC BY 4.0 license. Supplemental material is available for this article.

With the advent of deep learning algorithms, fully automated radiological image analysis is within reach. In spine imaging, several atlas- and shape-based as well as deep learning segmentation algorithms have been proposed, allowing for subsequent automated analysis of morphology and pathology. The first “Large Scale Vertebrae Segmentation Challenge” (VerSe 2019) showed that these perform well on normal anatomy, but fail in variants not frequently present in the training dataset. Building on that experience, we report on the largely increased VerSe 2020 dataset and results from the second iteration of the VerSe challenge (MICCAI 2020, Lima, Peru). VerSe 2020 comprises annotated spine computed tomography (CT) images from 300 subjects with 4142 fully visualized and annotated vertebrae, collected across multiple centres from four different scanner manufacturers, enriched with cases that exhibit anatomical variants such as enumeration abnormalities (n = 77) and transitional vertebrae (n = 161). Metadata includes vertebral labelling information, voxel-level segmentation masks obtained with a human-machine hybrid algorithm and anatomical ratings, to enable the development and benchmarking of robust and accurate segmentation algorithms.

Objective: In multiple sclerosis, neuropathological studies have shown widespread changes in the cerebral cortex. In vivo imaging is critical, because the histopathological substrate of most measurements is unknown.Methods: Using a novel magnetic resonance imaging analysis technique, based on the ratio of T1‐ and T2‐weighted signal intensities, we studied the cerebral cortex of a large cohort of patients in early stages of multiple sclerosis. A total of 168 patients with clinically isolated syndrome or relapsing–remitting multiple sclerosis (Expanded Disability Status Scale: median = 1, range = 0–3.5) and 80 age‐ and sex‐matched healthy controls were investigated. We also searched for the histopathological substrate of the T1/T2‐weighted ratio by combining postmortem imaging and histopathology in 9 multiple sclerosis brain donors.Results: Patients showed lower T1/T2‐weighted ratio values in parietal and occipital areas. The 4 most significant clusters appeared in the medial occipital and posterior cingulate cortex (each left and right). The decrease of the T1/T2‐weighted ratio in the posterior cingulate was related to performance in attention. Analysis of the T1/T2‐weighted ratio values of postmortem imaging yielded a strong correlation with dendrite density but none of the other parameters including myelin.Interpretation: The T1/T2‐weighted ratio decreases in early stages of multiple sclerosis in a widespread manner, with a preponderance of posterior areas and with a contribution to attentional performance; it seems to reflect dendrite pathology. As the method is broadly available and applicable to available clinical scans, we believe that it is a promising candidate for studying and monitoring cortical pathology or therapeutic effects in multiple sclerosis. Ann Neurol 2017;82:519–529