Medical image analysis methods in MR/CT-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. A critical appraisal

Abstract

Over the last 15 years, basic thresholding techniques in combination with standard statistical correlation-based data analysis tools have been widely used to investigate different aspects of evolution of acute or subacute to late stage ischemic stroke in both human and animal data. Yet, a wave of biology-dependent and imaging-dependent issues is still untackled pointing towards the key question: “how does an ischemic stroke evolve?” Paving the way for potential answers to this question, both magnetic resonance (MRI) and CT (computed tomography) images have been used to visualize the lesion extent, either with or without spatial distinction between dead and salvageable tissue. Combining diffusion and perfusion imaging modalities may provide the possibility of predicting further tissue recovery or eventual necrosis. Going beyond these basic thresholding techniques, in this critical appraisal, we explore different semi-automatic or fully automatic 2D/3D medical image analysis methods and mathematical models applied to human, animal (rats/rodents) and/or synthetic ischemic stroke to tackle one of the following three problems: (1) segmentation of infarcted and/or salvageable (also called penumbral) tissue, (2) prediction of final ischemic tissue fate (death or recovery) and (3) dynamic simulation of the lesion core and/or penumbra evolution. To highlight the key features in the reviewed segmentation and prediction methods, we propose a common categorization pattern. We also emphasize some key aspects of the methods such as the imaging modalities required to build and test the presented approach, the number of patients/animals or synthetic samples, the use of external user interaction and the methods of assessment (clinical or imaging-based). Furthermore, we investigate how any key difficulties, posed by the evolution of stroke such as swelling or reperfusion, were detected (or not) by each method. In the absence of any imaging-based macroscopic dynamic model applied to ischemic stroke, we have insights into relevant microscopic dynamic models simulating the evolution of brain ischemia in the hope to further promising and challenging 4D imaging-based dynamic models. By depicting the major pitfalls and the advanced aspects of the different reviewed methods, we present an overall critique of their performances and concluded our discussion by suggesting some recommendations for future research work focusing on one or more of the three addressed problems.

Multivariate Dynamic Prediction of Ischemic Infarction and Tissue Salvage as a Function of Time and Degree of Recanalization

“…Multivariate imaging approaches to predict infarct size have focused primarily on perfusion parameters only without adjusting for varying time intervals to recanalization. 20 We expanded a GLMbased method to derive and validate the first multivariate brain For any degree of recanalization and time to treatment, the trained and cross-validated GLM calculates a brain map of infarct probability based on the individual constellation of multiple CT perfusion parameters adjusting for tissue type, spatial clustering of ischemia, anatomy of vessel territory, severity of symptoms, age, sex, and time to imaging. Total infarct volume was calculated from cumulative voxelwise infarct probability based on the concept of the expected value in probabilistic statistical analysis.…”

“…5,9,19 To combine the independent contributing effect of multiple variables on voxelwise infarct probability, multivariate image analysis has been explored including logistic regression or generalized linear model (GLM). 14,20,21 A multivariate imaging model that contains variables of timing and recanalization status opens the opportunity for dynamic estimation of infarction and salvageable tissue in the context of elapsing time and recanalization status after endovascular treatment. This is particularly important in light of evidence that growth of infarct lesions is heterogeneous and likely occurs beyond generally applied time windows for treatment in a large minority of patients.…”

“…Few studies employed mathematical dynamic models to simulate stroke evolution but none used medical data or have been assessed using imaging or clinical outcome. 20 The purpose of this study was (1) to construct and validate a multivariate CTP-based imaging model for voxelwise infarct probability by incorporating multiple contributors of infarct risk including recanalization status and time to treatment based on a large empirical multicenter data set, (2) to examine how patientspecific multivariate voxelwise infarct risk is related to degree of recanalization in the context of variable time intervals to treatment. We hypothesize that there are patient-specific multivariate constellations of brain perfusion and clinical status where a benefit of successful recanalization on tissue outcome exists at extended time intervals to treatment.…”

A Survey on Medical Image Segmentation Methods with Different Modalitites

“…Tumor segmentation methods frequently got ideas from other brain tissue and lesion segmentation methods that have attained a exact accuracy [22]. Brain lesions ensuing from traumatic brain problems [23], [24] and stroke [25], [26] are same to glioma lesions in terms of size and multimodal power patterns, but have attracted little attention so far. Different probabilistic models exactly get the changes between the appearance of the lesion and other tissues from the data.…”

Validity of Shape as a Predictive Biomarker of Final Infarct Volume in Acute Ischemic Stroke

“…2,3 Different approaches of MRI-based models of prediction of the final infarct volume have been proposed. 4,5 From an image analysis point of view, 2 main approaches have been reported to date to assess the evolution of acute ischemic lesions using objective measurements from MRI: volumetric region of interest [6][7][8][9] or pixel-by-pixel analyses. [10][11][12][13][14] Recent studies have demonstrated that another type of analysis based on shape of the acute ischemic lesion may also contain important predictive information.…”