Dataset Information

A large public dataset of annotated clinical MRIs and metadata of patients with acute stroke.

ABSTRACT: To extract meaningful and reproducible models of brain function from stroke images, for both clinical and research proposes, is a daunting task severely hindered by the great variability of lesion frequency and patterns. Large datasets are therefore imperative, as well as fully automated image post-processing tools to analyze them. The development of such tools, particularly with artificial intelligence, is highly dependent on the availability of large datasets to model training and testing. We present a public dataset of 2,888 multimodal clinical MRIs of patients with acute and early subacute stroke, with manual lesion segmentation, and metadata. The dataset provides high quality, large scale, human-supervised knowledge to feed artificial intelligence models and enable further development of tools to automate several tasks that currently rely on human labor, such as lesion segmentation, labeling, calculation of disease-relevant scores, and lesion-based studies relating function to frequency lesion maps.

SUBMITTER: Liu CF

PROVIDER: S-EPMC10444746 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A large public dataset of annotated clinical MRIs and metadata of patients with acute stroke.

Liu Chin-Fu CF Leigh Richard R Johnson Brenda B Urrutia Victor V Hsu Johnny J Xu Xin X Li Xin X Mori Susumu S Hillis Argye E AE Faria Andreia V AV

Scientific data 20230822 1

To extract meaningful and reproducible models of brain function from stroke images, for both clinical and research proposes, is a daunting task severely hindered by the great variability of lesion frequency and patterns. Large datasets are therefore imperative, as well as fully automated image post-processing tools to analyze them. The development of such tools, particularly with artificial intelligence, is highly dependent on the availability of large datasets to model training and testing. We ...[more]

PMID: 37607929

Similar Datasets

Project description:Our sense of hearing is mediated by cochlear hair cells, localized within the sensory epithelium called the organ of Corti. There are two types of hair cells in the cochlea, which are organized in one row of inner hair cells and three rows of outer hair cells. Each cochlea contains a few thousands of hair cells, and their survival is essential for our perception of sound because they are terminally differentiated and do not regenerate after insult. It is often desirable in hearing research to quantify the number of hair cells within cochlear samples, in both pathological conditions, and in response to treatment. However, the sheer number of cells along the cochlea makes manual quantification impractical. Machine learning can be used to overcome this challenge by automating the quantification process but requires a vast and diverse dataset for effective training. In this study, we present a large collection of annotated cochlear hair-cell datasets, labeled with commonly used hair-cell markers and imaged using various fluorescence microscopy techniques. The collection includes samples from mouse, human, pig and guinea pig cochlear tissue, from normal conditions and following in-vivo and in-vitro ototoxic drug application. The dataset includes over 90'000 hair cells, all of which have been manually identified and annotated as one of two cell types: inner hair cells and outer hair cells. This dataset is the result of a collaborative effort from multiple laboratories and has been carefully curated to represent a variety of imaging techniques. With suggested usage parameters and a well-described annotation procedure, this collection can facilitate the development of generalizable cochlear hair cell detection models or serve as a starting point for fine-tuning models for other analysis tasks. By providing this dataset, we aim to supply other groups within the hearing research community with the opportunity to develop their own tools with which to analyze cochlear imaging data more fully, accurately, and with greater ease.

Project description:We present Audiovisual Moments in Time (AVMIT), a large-scale dataset of audiovisual action events. In an extensive annotation task 11 participants labelled a subset of 3-second audiovisual videos from the Moments in Time dataset (MIT). For each trial, participants assessed whether the labelled audiovisual action event was present and whether it was the most prominent feature of the video. The dataset includes the annotation of 57,177 audiovisual videos, each independently evaluated by 3 of 11 trained participants. From this initial collection, we created a curated test set of 16 distinct action classes, with 60 videos each (960 videos). We also offer 2 sets of pre-computed audiovisual feature embeddings, using VGGish/YamNet for audio data and VGG16/EfficientNetB0 for visual data, thereby lowering the barrier to entry for audiovisual DNN research. We explored the advantages of AVMIT annotations and feature embeddings to improve performance on audiovisual event recognition. A series of 6 Recurrent Neural Networks (RNNs) were trained on either AVMIT-filtered audiovisual events or modality-agnostic events from MIT, and then tested on our audiovisual test set. In all RNNs, top 1 accuracy was increased by 2.71-5.94% by training exclusively on audiovisual events, even outweighing a three-fold increase in training data. Additionally, we introduce the Supervised Audiovisual Correspondence (SAVC) task whereby a classifier must discern whether audio and visual streams correspond to the same action label. We trained 6 RNNs on the SAVC task, with or without AVMIT-filtering, to explore whether AVMIT is helpful for cross-modal learning. In all RNNs, accuracy improved by 2.09-19.16% with AVMIT-filtered data. We anticipate that the newly annotated AVMIT dataset will serve as a valuable resource for research and comparative experiments involving computational models and human participants, specifically when addressing research questions where audiovisual correspondence is of critical importance.

Project description:BackgroundWhen patients with acute ischemic stroke present with suspected large vessel occlusion in the catchment area of a primary stroke center (PSC), the benefit of direct transport to a comprehensive stroke center (CSC) has been suggested. Equipoise remains between transport strategies and the best transport strategy is not well established.MethodsWe conducted a national investigator-driven, multicenter, randomized, assessor-blinded clinical trial. Patients eligible for intravenous thrombolysis (IVT) who were suspected for large vessel occlusion were randomized 1:1 to admission to the nearest PSC (prioritizing IVT) or direct CSC admission (prioritizing endovascular therapy). The primary outcome was functional improvement at day 90 for all patients with acute ischemic stroke, measured as shift towards a lower score on the modified Rankin Scale score.ResultsFrom September 2018 to May 2022, we enrolled 171 patients of whom 104 had acute ischemic stroke. The trial was halted before full recruitment. Baseline characteristics were well balanced. Primary analysis of shift in modified Rankin Scale (ordinal logistic regression) revealed an odds ratio for functional improvement at day 90 of 1.42 (95% CI, 0.72-2.82, P=0.31). Onset to groin time for patients with large vessel occlusion was 35 minutes (P=0.007) shorter when patients were transported to a CSC first, whereas onset to needle (IVT) was 30 minutes (P=0.012) shorter when patients were transported to PSC first. IVT was administered in 67% of patients in the PSC group versus 78% in the CSC group and EVT was performed in 53% versus 63% of the patients, respectively.ConclusionsThis trial investigated the benefit of bypassing PSC. We included only IVT-eligible patients presenting <4 hours from onset and with suspected large vessel occlusion. Lack of power prevented the results from showing effect on functional outcome for patients going directly to CSC.RegistrationURL: https://www.Clinicaltrialsgov; Unique identifier: NCT03542188.

Dataset Information

A large public dataset of annotated clinical MRIs and metadata of patients with acute stroke.

Publications

A large public dataset of annotated clinical MRIs and metadata of patients with acute stroke.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets