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Cerebral and retinal ischemia share similar pathogenesis and epidemiology, each carrying both acute and prolonged risk of the other and often co-occurring. The most used preclinical stroke models, the Koizumi and Longa middle cerebral artery occlusion (MCAO) methods, have reported retinal damage with great variability, leaving the disruption of retinal blood supply via MCAO poorly investigated, even providing conflicting assumptions on the origin of the ophthalmic artery in rodents. The aim of our study was to use longitudinal in vivo magnetic resonance assessment of cerebral and retinal vascular perfusion after the ischemic injury to clarify whether and how the Koizumi and Longa methods induce retinal ischemia and how they differ in terms of cerebral and retinal lesion evolution. We provided anatomical evidence of the origin of the ophthalmic artery in mice from the pterygopalatine artery. Following the Koizumi surgery, retinal responses to ischemia overlapped with those in the brain, resulting in permanent damage. In contrast, the Longa method produced only extensive cerebral lesions, with greater tissue loss than in the Koizumi method. Additionally, our data suggests the Koizumi method should be redefined as a model of ischemia with chronic hypoperfusion rather than of ischemia and reperfusion.

Optical coherence tomography (OCT) images of the retina provide a structural representation and give an insight into the pathological changes present in age-related macular degeneration (AMD). Due to the three-dimensionality and complexity of the images, manual analysis of pathological features is difficult, time-consuming, and prone to subjectivity. Computer analysis of 3D OCT images is necessary to enable automated quantitative measuring of the features, objectively and repeatedly. As supervised and semi-supervised learning-based automatic segmentation depends on the training data and quality of annotations, we have created a new database of annotated retinal OCT images -the AROI database. It consists of 1136 images with annotations for pathological changes (fluid accumulation and related findings) and basic structures (layers) in patients with AMD. Inter-and intra-observer errors have been calculated in order to enable the validation of developed algorithms in relation to human variability. Also, we have performed the automatic segmentation with standard U-net architecture and two state-of-the-art architectures for medical image segmentation to set a baseline for further algorithm development and to get insight into challenges for automatic segmentation. To facilitate and encourage further research in the field, we have made the AROI database openly available.

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