Real-Time Pixel-Level Semantic Tracking in Retinal Microsurgery

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Aim of the PhD Project:This project will develop deep learning models regenerative therapy delivery in retinal microsurgery by innovating on:Retinal tracking via optical flowMulti-objective learning for instrument segmentationImage stabilisation for injection guidance.Ultimately, the outputs of the PhD will be used to guide the subretinal delivery of sight-restoring regenerative therapies.Summary:Gene therapy and cellular therapy are emerging as transformative regenerative treatments for severe retinal diseases leading to blindness. The delivery of these treatments into specific and delicate retinal tissue layers, some as thin as 10-20um, requires microsurgical precision that lies at and above the limit of human perception and dexterity. As such, retinal tissues need to be visualised via high-resolution imaging and dexterously manipulated to optimise outcomes. In addition, tissue and tools should be recognised and tracked in real time to achieve stable injections despite physiological or surgeon induced patient motions.This PhD project will research unsupervised learning methods for pixel-level tracking and semantic interpretation of images acquired during vitreoretinal microsurgery, incorporating spatiotemporal constraints in the form of keyframe identification, loop-closure, and uncertainty estimation. The student will create a deep learning framework that robustly tracks injection point, tools, and anatomical landmarks over several minutes, to ensure prolonged injection of therapies with safe flow rates. Images are acquired via stereo biomicroscopy that visualises the retina en face, allowing for left/right consistency to also be leveraged.