Edinburgh Research Explorer

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

Research output: Contribution to journalArticle

Related Edinburgh Organisations

Open Access permissions



  • Download as Adobe PDF

    Rights statement: Available under Open Access

    Final published version, 771 KB, PDF document

    Licence: Creative Commons: Attribution Non-Commercial (CC-BY-NC)

Original languageEnglish
Pages (from-to)164-178
Number of pages15
JournalNeuroImage: Clinical
Issue number1
Publication statusPublished - 1 Jan 2012


Over the last 15 years, basic thresholding techniques in combination with standard statistical correlation-based data analysis tools have beenwidely used to investigate different aspects of evolution of acute or subacute to late stage ischemic stroke in both human and animal data. Yet, awave 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, bothmagnetic 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 eachmethod. In the absence of any imaging-based macroscopic dynamicmodel 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.

Download statistics

No data available

ID: 8468333