Self-guided Microrobotics for Automated Brain Dissection

用于自动脑解剖的自引导微型机器人

• 批准号: ES/T011866/1
• 负责人: Danail Stoyanov
• 金额: $ 64.74万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FT011866%2F1
• 关键词: Self; guided; Microrobotics; Automated; Brain

Neural precursor cells (NPCs) are neural stem cells and their progeny, which are multipotent, self-renewing cells that are responsible for building the fundamental components of the vertebrate central nervous system during embryonic development. Notably, NPCs persist into the mature brain as very rare cells that continue to generate new neurons throughout the lifetime of the organism. Intensive study of these precious cells could lead to a watershed moment in the field of neurobiology and the development of regenerative therapies to treat Alzheimer's disease, dementia, and traumatic brain injury. However, our ability to harness the therapeutic potential of NPCs is limited by the practical difficulty of identifying and collecting them for analysis. Current methods require painstaking manual microdissection of neural tissue followed by cell sorting methods which, in addition to requiring large samples sizes (and thus large quantities of tissue) also often fail to distinguish between NPCs and other cells expressing the same biomarkers.This highly interdisciplinary project brings together researchers with expertise in artificial intelligence (AI), robotics, engineering and neuroscience to create a new automated microsurgical platform which will be applied to identify and collect NPCs from brain tissues. By exploiting the capacity of deep learning approaches to detect features in complex data sets we will develop two new image-guided microsurgery tools: a microrobotic resector capable of carefully excising the small region of brain tissue (the SVZ) within which NPCs are found and microrobotic cell collector, capable of efficiently harvesting individual cells for further study and analysis using a coordinated array of optoelectronic microrobots. When then aim to integrate these two systems into a single, unique robotic microsurgery platform.The new system will be applied to collect and analyse NPCs from different brain tissues. In particular, we propose to analyse the sex-dependent differences in NPCs phenotypes, which have been reported in previous research. As well as transforming our capacity for collection of NPCs and likewise supporting the development of new regenerative therapies, we believe this approach will provide a powerful new method for a wide range of microsurgery applications. Altogether, we will use these results to simulate debate among the related engineering, scientific and wider public communities to shape a new international multi-disciplinary network focusing on challenges with regenerative medicine i.e. providing new case studies for govt. policy and further investment for AI-driven healthcare applications.

神经前体细胞（neural precursor cells，NPC）是神经干细胞及其后代，其是在胚胎发育期间负责构建脊椎动物中枢神经系统的基本组成部分的多能、自我更新的细胞。值得注意的是，NPC作为非常罕见的细胞持续存在于成熟的大脑中，这些细胞在生物体的整个生命周期中继续产生新的神经元。对这些珍贵细胞的深入研究可能会导致神经生物学领域的一个分水岭，以及治疗阿尔茨海默病、痴呆和创伤性脑损伤的再生疗法的发展。然而，我们利用NPC的治疗潜力的能力受到识别和收集它们进行分析的实际困难的限制。目前的方法需要对神经组织进行艰苦的手动显微切割，然后进行细胞分选方法，除了需要大的样本量之外，（以及因此大量的组织）也常常无法区分NPC和表达相同生物标志物的其他细胞。这个高度跨学科的项目汇集了在人工智能（AI）、机器人、工程学和神经科学共同创建一个新的自动化显微外科平台，该平台将用于识别和收集脑组织中的NPC。通过利用深度学习方法在复杂数据集中检测特征的能力，我们将开发两种新的图像引导显微手术工具：一种微型机器人切除器，能够仔细切除发现NPC的脑组织小区域（SVZ），以及微型机器人细胞收集器，能够有效地收集单个细胞，以便使用光电微型机器人的协调阵列进行进一步研究和分析。然后，我们的目标是将这两个系统集成到一个独特的机器人显微手术平台中。新系统将用于收集和分析来自不同脑组织的NPC。特别是，我们建议分析NPC表型的性别依赖性差异，这在以前的研究中已有报道。除了改变我们收集NPC的能力，同样也支持新再生疗法的开发，我们相信这种方法将为广泛的显微外科应用提供一种强大的新方法。总而言之，我们将利用这些结果来模拟相关工程、科学和更广泛的公众社区之间的辩论，以形成一个新的国际多学科网络，重点关注再生医学的挑战，即为政府政策提供新的案例研究，并为人工智能驱动的医疗保健应用提供进一步投资。

项目成果

Autonomous pick-and-place using the dVRK.

使用DVRK的自主选择。

• DOI: 10.1007/s11548-021-02397-y
• 发表时间: 2021-07
• 期刊: International journal of computer assisted radiology and surgery
• 影响因子: 3
• 作者: D'Ettorre C;Stilli A;Dwyer G;Tran M;Stoyanov D
• 通讯作者: Stoyanov D

Adaptive Autonomous Navigation of Multiple Optoelectronic Microrobots in Dynamic Environments

• DOI: 10.1109/lra.2022.3194308
• 发表时间: 2022-10
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Laurent Mennillo;Christopher Bendkowski;M. Elsayed;Harrison Edwards;Shuailong Zhang;Vijay M. Pawar;A. R. Wheeler;D. Stoyanov;M. Shaw

Stain-free identification of tissue pathology using a generative adversarial network to infer nanomechanical signatures.

• DOI: 10.1039/d1na00527h
• 发表时间: 2021-11-09
• 期刊: Nanoscale advances
• 影响因子: 4.7
• 作者: Neary-Zajiczek L;Essmann C;Rau A;Bano S;Clancy N;Jansen M;Heptinstall L;Miranda E;Gander A;Pawar V;Fernandez-Reyes D;Shaw M;Davidson B;Stoyanov D
• 通讯作者: Stoyanov D

Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit

• DOI: 10.1109/mra.2021.3101646
• 发表时间: 2021-12-01
• 期刊: IEEE ROBOTICS & AUTOMATION MAGAZINE
• 影响因子: 5.7
• 作者: D'Ettorre, Claudia;Mariani, Andrea;Stoyanov, Danail
• 通讯作者: Stoyanov, Danail

Autonomous object harvesting using synchronized optoelectronic microrobots

使用同步光电微型机器人进行自主物体采集

• DOI: 10.1109/iros51168.2021.9636475
• 发表时间: 2021
• 作者: Bendkowski C