Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data

Abstract

Background and Purpose-We evaluated deep learning algorithms' segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted (DWI) datasets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods-Ischemic stroke data sets from the MRI-GENetics Interface Exploration (MRI-GENIE) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3D convolutional neural networks (CNNs). Three ensembles were trained using data from: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms' performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes and vascular risk factors were performed to identify phenotypes associated with large acute DWI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results-The ensemble consisting of a mixture of MRI-GENIE and single-center CNNs performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92, p<0.0001). Median [IQR] DWI lesion volumes from 2770 patients were 3.7 [0.9-16.6] cm 3. Patients with small artery occlusion (SAO) stroke subtype had smaller lesion volumes (p<0.0001) and different topography compared to other stroke subtypes. Conclusions-Automated accurate clinical DWI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke etiology with sufficient sample size from "big" heterogeneous multi-center clinical imaging phenotype datasets. Wu et al.

Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks

“…Third, the training of deep learning models is stochastic in nature. Future work may investigate ensembling multiple models trained on subsets of the training data, as this may allow averaging out deficiencies in single models [5], [10], [24]. Addressing these limitations may further improve CNN-based lesion segmentations in NCCT datasets.…”

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke

“…Furthermore, these collaborations are bound to extend beyond the datasets with pre-specified MRI characteristics, as additional methodologies emerge in the future to reconcile the variability in sequences and technical approaches used to extract the data relevant to stroke recovery. For instance, clinical MRI scans obtained during emergency hospitalization for acute ischemic stroke provide abundance of data related to post-stroke outcomes when analyzed using novel machinelearning methods for stroke lesion segmentation on diffusion-weighted imaging (Wu et al, 2019). Future artificial intelligence-powered methodology will allow to optimize analysis of these clinical scans and reconcile different types of data and approaches to lesion segmentation.…”

Diffusion-Weighted Imaging, MR Angiography, and Baseline Data in a Systematic Multicenter Analysis of 3,301 MRI Scans of Ischemic Stroke Patients—Neuroradiological Review Within the MRI-GENIE Study

“…MRI-GENIE offers a novel approach to developing such type of data, and systematic review and radiological analysis of the MRI data are the first steps toward detailed characterization of clinical MRI phenotypes. We will continue our work by combining this work with recently developed automated volume segmentation algorithms (14) to assess the combined effect of risk factors and vascular anatomy with lesion characteristics. We will also assess the effect of genetic traits that may alter the MRI stroke phenotype in general, or in specific conditions such as in the presence of certain neurovascular conditions or certain risk factors.…”

“…A single subcortical, supratentorial lesion smaller than 1.5 cm was further defined as lacunar. Volumetric analysis of the DWIs was recently published (14).…”

“…A detailed description of the design and rationale for both these studies has been published previously (11,12). The MRI-GENIE initiative has already resulted in a number of pioneering studies in automated volumetric MRI analysis (13)(14)(15), posterior-circulation stroke (16), and several ongoing projects using the imaging data to link genetic traits, such as the recently discovered RABEP2-gene (17) with phenotypic outcome.…”