Real-Time Pixel-Level Semantic Tracking in Retinal Microsurgery

视网膜显微外科手术中的实时像素级语义跟踪

• 批准号: 2904561
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2904561
• 关键词: Real; Time; Pixel; Level; Semantic

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.

博士项目的目标：该项目将通过以下创新来开发视网膜显微手术中的深度学习模型再生治疗交付：通过光流进行视网膜跟踪用于仪器分割的多目标学习用于注射指导的图像稳定。最终，博士的成果将用于指导视网膜下交付恢复视力的再生治疗。摘要：基因治疗和细胞治疗正在成为导致失明的严重视网膜疾病的变革性再生治疗。将这些治疗递送到特定和精细的视网膜组织层中，一些薄至10- 20 μ m，需要显微外科手术的精确度，该精确度处于或高于人类感知和灵巧的极限。因此，视网膜组织需要通过高分辨率成像进行可视化，并巧妙地操作以优化结果。此外，组织和工具应被识别和跟踪在真实的时间，以实现稳定的注射，尽管生理或外科医生诱导的患者motions.This博士项目将研究无监督学习方法的像素级跟踪和语义解释的玻璃体视网膜显微手术过程中获得的图像，结合时空的关键帧识别，闭环和不确定性估计的形式约束。学生将创建一个深度学习框架，在几分钟内稳健地跟踪注射点，工具和解剖标志，以确保以安全的流速长时间注射治疗。通过立体生物显微镜获取图像，该立体生物显微镜使视网膜正面可视化，从而也可以利用左/右一致性。