Pipeline for automatic segmentation of multiparametric MRI data in a rat model of ischemic stroke

Stroke is a global health concern, positioning itself as one of the chief culprits behind both disability and mortality worldwide. There is currently no treatment for ischemic stroke beside re-perfusion therapy. To evaluate new treatment options, researchers conduct studies on rat models of ischemic stroke using the same MRI approach as in patients. MR images and parametric maps are used to define the ischemic core, the area-at-risk of infarction and the final lesion. Historically, this process has relied heavily on manual segmentation conducted by experts, which not only consumes a significant amount of time but also often lacks the desired level of reliability. This drawback establishes an urgent requirement for robust, dependable, and automated tools to stimulate progress in stroke research. Addressing this pressing need, we introduce a novel pipeline that automates lesion segmentation in a rat stroke model of ischemic stroke. This innovative solution ingeniously amalgamates steps of pre-processing, optimal thresholding, and the state-of-the-art UNet deep learning method. From our knowledge, we are the first to propose an automatic regions segmentation from T2, DWI and PWI MRI imaging. The integrated approach of optimal thresholding and UNet employed in this pipeline delivers high-quality results. We evaluated performance on 58 rats using four measures of segmentation quality and also correlation curves between lesion size and manual versus automatic segmentation. With this robust tool, the segmentation of abnormalities in rat model is made both efficient and precise, saving valuable time and resources. Therefore, our results hold potential to propel advancements in stroke research and stimulate the development of pioneering treatment strategies. Our code and data (with manual annotations) are available online.