"Deep learning for MRI-based acute and subacute ischaemic stroke lesion segmentation—a systematic review, meta-analysis, and pilot evaluation of key results" has reached the 5000 views milestone. Medical Imaging Analyst and lecturer, Dr Maria Valdes Hernandez, based within the Row Fogo Centre into Research into Ageing and the Brain, co-authored a paper on MRI segmentation for stroke lesions. The paper, "Deep learning for MRI-based acute and subacute ischaemic stroke lesion segmentation—a systematic review, meta-analysis, and pilot evaluation of key results", published on 10 June 2025 has attracted 5000 views since its release. And the number accessing the article continues to grow. Segmentation of ischaemic stroke lesions from magnetic resonance images (MRI) remains a challenging task mainly due to the confounding appearance of these lesions with other pathologies, and variations in their presentation depending on the lesion stage (i.e., hyper-acute, acute, subacute and chronic). Works on the theme have been reviewed, but none of the reviews have addressed the seminal question on what would be the optimal architecture to address this challenge. Dr Maria Valdes Hernandez and co-author, Makram Baaklini systematically reviewed the literature (2015–2023) for deep learning algorithms that segment acute and/or subacute stroke lesions on brain MRI seeking to address this question, meta-analysed the data extracted, and evaluated the results.Well done to Maria and Makram on their success!You can access the full publication at the link below:Frontiers | Deep learning for MRI-based acute and subacute ischaemic stroke lesion segmentation—a systematic review, meta-analysis, and pilot evaluation of key results This article was published on 2026-01-21
