CN104582624A - 自动的外科手术和介入过程 - Google Patents

自动的外科手术和介入过程 Download PDF

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CN104582624A
CN104582624A CN201380040070.3A CN201380040070A CN104582624A CN 104582624 A CN104582624 A CN 104582624A CN 201380040070 A CN201380040070 A CN 201380040070A CN 104582624 A CN104582624 A CN 104582624A
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P·吉姆
A·克里埃格尔
Y·吉姆
A·沙德曼
S·莱昂纳德
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Childrens National Medical Center Inc
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Abstract

此处描述了用于在手术和介入医疗过程中自动化子任务的装置和方法。装置由机器人定位平台、具有自动化程序的操作系统和端部效应器工具组成，用以在监督性自主下进行任务。当端部效应器工具执行需要精度、准确度、机动性和重复的手术介入子任务时，操作系统基于一个成像形态或者两个或更多个成像形态的融合执行自动化程序、引导非结构化环境中的可移动和可变形目标的实时跟踪。装置和方法使得这些医疗过程更高效和有效，允许更宽的接入和更加标准化的结果以及提高的安全性。

Description

自动的外科手术和介入过程
相关申请的交叉引用
本申请根据35U.S.C.§119(e)要求2012年6月29日提交的U.S.Serial No.61/666,399的优先权的权益，其全部内容通过引用合并于此。
背景技术
技术领域
本发明涉及机器人手术的领域，即，手术任务的完全自动化或者部分自动化。
相关技术的描述
由于外科医生和机器人通过主/从范式进行交互，因此市场上可获得的所谓的“机器人手术”通常是机器人辅助手术。从机器人被约束来遵循来自外科医生主设备的直接命令，而几乎没有自主性。尽管该方法是可靠的，但是它将从机器人的速度和灵活度限制于外科医生的速度和灵活度。
如果过程是部分自动的，那么可以对机器人进行编程以使用从更有经验的外科医生收集的数据，以结合附加安全措施或者甚至优化过程以超越人类极限工作，从而提高效率、可靠性以及潜在地提高手术效果。
当前可获得的侵入性手术系统都没有在手术过程期间利用真正的自动化。Intuitive Surgical公司(Sunnyvale，CA)的da Vinci微创手术系统利用具有最小自动化的主-从控制系统。用于整形外科手术的Robodoc依靠外科医生输入和计划进行磨骨。然而，如由Moustris等人的文献综述中所详述的，存在对手术机器人进行闭环控制中所涉及的每个步骤的持续不断的研究。
在对用于生成自动例程的临床数据的分析中，Johns Hopkins大学的手术语言项目解析和创建了手术运动的模型，并且显示了生成手术运动的能力，尽管该工作仍在继续以使任务适应手术场景。尽管Bauernschmitt等人开发了可以在机器人中复现打结的系统，但是这只是模拟预先录制的移动，并且不是真正的自动化。
在设计用于手术自动化的控制系统中，多个组已设法使用超声波产生手术场景的3D重建，并且正在开发用3D超声波的视觉伺服。Majdani等人在尸体上对微创机器人耳蜗造口术进行了测试，使用术前CT图像计算钻探轨迹并且使用具有图像反馈的视觉标记以引导机器人。然而没有一种方法将两个可见光图像与其它模态结合以控制机器人。
有若干专利申请描述了自动化。“Surgical Guidance UtilizingTissue Feedback”描述了进行预编程的任务(例如轨迹移动)，同时将预期的组织反馈与当前组织反馈进行比较以帮助该过程(例如对于误差的校正，作为参考点使用)的系统。“Ultrasonic SurgicalSystem and Method”描述了利用超声波工具的手术系统，具有以在使用该工具中涉及的多个任务辅助外科医生，或者如由外科医生或者预加载程序所指引的自动地执行任务的能力。尽管这些专利包括作为用于它们的发明的可能控制方式的自动化，但是它们将自动化的使用限制为简单的任务或者辅助外科医生。尽管“Robotic SurgicalSystem”给出了体腔内自动手术系统的一般说明，但是几乎没有提供关于开发的计划或者其实现的细节。
1.Moustris,GP,SC Hiridis,KM Deliparaschos和KMKonstantinidis的“Evolution of Autonomous and Semi-autonomousRobotic Surgical Systems:a Review of the Literature.”Int J MedRobot 7,no.4(2011):375-92。
2.Lin HC,Shafran I,Murphy TE等人的Automatic detectionand segmentation of robot-assisted surgical motions.In Medical ImageComputing and Computer-Assisted Intervention(MICCAI 2005.Springer:Berlin,2005；802810。
3.Reiley CE,Plaku E,Hager GD.Motion generation of roboticsurgical tasks:learning from expert demonstrations.In AnnualInternational Conference of the IEEE,Engineering in Medicine andBiology Society(EMBC),2010；967-970。
4.Bauernschmitt R,Schirmbeck EU,Knoll A等人的Towardsrobotic heart surgery:introduction of autonomous procedures into anexperimental surgical telemanipulator system.Int J MedRobot 2005；1(3):74-79。
5.Majdani O,Rau TS,Baron S等人的A robot-guidedminimally invasive approach for cochlear implant surgery:preliminary results of a temporal bone study.Int J Comput AssistRadiolSurg2009；4(5):475-486。
6.美国专利申请No.12/842,462“Surgical Guidance UtilizingTissue Feedback”，受让人：McMaster University。
7.美国专利申请No.11/392,040“Ultrasonic Surgical System andMethod”，受让人：Ethicon Endo-Surgery Inc。
8.美国专利申请No.12/045,230“Robotic Surgical System”，受让人：Fleit Gibbons Gutman Bongini&Bianco PL。
发明内容
当前可用的主-从操作模式将机器人限制于操作的外科医生的灵活度和技能，在与自主控制相比较时这对需要高的精度、灵活度和重复的某些子任务(例如缝合)可能是无效率的。另外，监督性自动化用建立在主外科医生的能力和来自辅助成像源的表面下组织信息上的基于证据的决策支持算法对基于每个个体外科医生的经验的自适应和视觉处理能力的限制进行改进。
将识别和选择需要高的灵活度、精度和重复性的手术子例程以用于自动化。将对来自专家外科医生进行的这些例程的手动和机器人辅助操作的临床数据(视觉记录、机器人移动、患者结果等等)进行分析以识别用于自动化的自动化关键信息(例如，关键参考点、视觉参考、器官/血管位置/移动等等)和最优移动模式。将对自动化程序进行设计，使得在给出前述关键信息的情况下自动化程序可以使手术任务适应不同患者。自动例程将不仅模拟临床数据，而且从专家外科医生的操作学习并且对外科医生的操作加以改进以利用机器人的效率和有效性。
自动化程序可以利用具有来自一个或者多个相机的实时视觉反馈的视觉伺服，该一个或者多个相机为内窥镜式的或者外部安装的，其能够提供可见光谱图像和/或非可见光谱图像。非可见光谱图像的示例是近红外荧光(NIR)图像。可以连同通过专用传感器的视觉信息或视觉信息的提取一起提供3D信息。例如，如果使用两个相机，则可以通过立体三角测量算法提取3D深度信息。还可以通过结构光3D扫描仪或者通过光场相机获得3D深度信息。
由于手术环境的常常动态和非结构化的性质，仅光学数据可能还不足够对可移动和可变形目标进行稳健的实时高保真度跟踪；因此，在需要时，视觉数据可以增扩、融合或者伴随有其它传感器(例如红外线相机、触觉传感器等等)。视觉图像连同其它感觉数据和关键数据一起将馈送到自动化程序的控制系统中，该自动化程序的控制系统将移动机器人和工具以执行期望的手术任务。对于每个手术过程可以生成超过一个自动化程序，其中每个程序接受传感器和关键数据的不同组合以适应不同手术情况，但完成相同的手术目标。也可以使单个程序接受传感器和数据的多个组合。
在手术期间，机器人将能够在三个操作模式中的一个下进行操作：主-从、半自主和监督性自主。在主-从模式中，外科医生直接控制机器人的运动以执行用另外两个模式不能进行的任务，例如为自主程序准备手术场景(例如放置组织标记)。在半自主模式中，外科医生将为机器人提供动作命令(例如，在特定位置中放置缝合线、以直线切割组织、打结)，机器人将使用自主计算的轨迹和工具致动来执行动作命令。也就是说，在准备手术部位之后，外科医生仍然交互地参与决策制定和命令规范。该交互可以通过图形用户界面实现，其中外科医生标出缝合线位置的轮廓以使得程序可以在视觉上跟踪目标位置并且生成机器人轨迹以到达目标。外科医生与程序交互的部分限定半自主性。在监督性自主模式中，外科医生仅向机器人提供总体目标(例如执行吻合术)并且自主程序在没有来自外科医生的任何输入的情况下，确定完成目标必需的动作(例如，要放置的缝合线的位置、数量、张力和顺序)。也就是说，在准备手术部位之后，程序自动地选出目标位置并且继续进行。外科医生的角色主要是安全性监督。在整个手术期间，外科医生可以视情况采用这三个模式中的任何一个，并且在操作中的任何时候，外科医生可以中断机器人运动并且采取机器人的主/从控制。
本发明实施例可以显著有利于其的一个手术子任务的示例是吻合术。通常手动地或者最近使用机器人通过主-从控制执行吻合术，但由于该过程需要大量灵活度、精度和重复，两个技术都是耗时的并且繁琐的。由于这些特征，因此从将该任务自动化将得到很大的潜在改进。
本技术有对需要精度、重复、可操纵性和再现性的其它手术过程加以改进的潜能，包括但不限于骨骼植入物的放置(旋拧/固定)、组织解剖、活组织检查、玻璃体视网膜手术、显微和/或血管吻合术、近距离治疗(brachytherapy)以及皮肤闭合。
此处公开的实施例提供了用于执行自动手术过程的系统。系统包括：提供关于手术区域的信息的传感器，配置为接收由外科医生发布的命令的用户界面，配置为将信息中继至外科医生的反馈设备，具有用于执行手术任务的端部部分的手术工具，耦接至手术工具并且定位和定向手术工具的手术机器人，通过处理硬件实现的跟踪处理模块，以及通过处理硬件实现的控制模块，并且所述跟踪处理模块被配置为从传感器接收传感器数据，基于传感器数据识别目标组织、周围组织和手术工具端部部分中的至少一个的位置，跟踪目标组织、周围组织和工具端部效应器中的至少一个中识别的位置的，并且所述控制模块配置为处理通过自动化程序从传感器、跟踪处理模块和用户界面接收的数据以生成和发送命令至手术机器人。
根据系统的另一个实施例，系统还包括提供关于手术区域信息的多个传感器。
根据系统的另一个实施例，传感器是相机、近红外荧光(NIR)相机、深度相机、结构光3D扫描仪中的一个。
根据系统的另一个实施例，反馈设备是配置为显示视觉提示或者图像的显示器，或者听觉设备。
根据系统的另一个实施例，跟踪处理模块还配置为使用近红外荧光(NIR)标记跟踪目标组织、周围组织和工具端部效应器中的至少一个的所识别的位置。
根据系统的另一个实施例，手术机器人可拆卸地耦接至手术工具。
根据系统的另一个实施例，手术机器人耦接至将手术机器人移进并且移出手术区域的移动机构。
根据系统的另一个实施例，自动化程序是半自主的。
根据系统的另一个实施例，自动化程序是监督性自主的。
根据系统的另一个实施例，控制模块配置为禁用自动化程序并且实现主-从模式。
根据系统的另一个实施例，控制模块配置为基于外科医生的输入中断自动化程序。
根据系统的另一个实施例，控制模块还配置为实现视觉伺服校正。
根据系统的另一个实施例，自动化程序配置为实现吻合术。
根据系统的另一个实施例，控制模块还配置为还基于禁飞区、运动远程中心和速度/力极限中的至少一个生成命令。
根据系统的另一个实施例，自动程序配置为通过生成和发送命令至所述手术机器人接合组织。
根据系统的另一个实施例，通过缝合线、夹子、卡钉或者粘合剂执行组织的接合。
根据系统的另一个实施例，控制模块还配置为实现视觉伺服校正以将工具端部部分带至目标。
根据系统的另一个实施例，由跟踪处理模块识别的位置是三维位置。
此处公开的实施例还提供了生成自动手术程序的计算机实现方法。方法包括处理临床数据以产生手术的3D空间和时间数据的步骤，获得手术机器人规格和临床参数的步骤，基于3D空间和时间数据、手术机器人规格和临床参数生成自动手术程序的步骤。
根据方法的另一个实施例，临床数据包括从相机或者内窥镜中获得的视觉数据、运动学数据或者触觉数据中的至少一个。
根据方法的另一个实施例，临床数据包括患者情况、生命特征和手术结果中的至少一个。
根据方法的另一个实施例，临床数据包括外科医生经验。
根据方法的另一个实施例，临床数据的处理基于考虑工具运动与手术结果之间的相关性产生手术的3D空间和时间数据。
根据方法的另一个实施例，临床数据的处理基于考虑外科医生经验水平之间的差异产生手术的3D空间和时间数据以产生更有效的移动。
根据方法的另一个实施例，3D空间和时间数据包括工具运动、工具定位、生命器官或者结构的位置和移动、可行的参考点以及组织变形中的至少一个。
根据方法的另一个实施例，手术机器人规格包括手术机器人的速度和精度。
根据方法的另一个实施例，生成的自动手术程序包括在临床数据中缺少但可由手术机器人实现的手术运动。
根据方法的另一个实施例，临床参数包括组织特征。
根据方法的另一个实施例，组织特征包括组织的预期移动和刚度或者组织的染剂的可吸收性。
根据方法的另一个实施例，自动手术程序包括：优选的移动模式、关键操作信息、配置为基于传感器信息和关键操作信息的组合指示机器人运动的控制模块。
根据方法的另一个实施例，关键操作信息包括生命器官位置、参考点或者标记、传感器数据和外科医生输入中的至少一个。
根据方法的另一个实施例，控制模块还配置为还基于包括运动远程中心、禁飞区和速度极限中的至少一个的运动限制来生成命令。
根据方法的另一个实施例，控制模块还配置为还基于组织变形或者移动模型生成命令。
根据方法的另一个实施例，其中自动手术程序能够用附加临床数据更新。
附图说明
图1示出了在机器人手术中利用本发明的整个工作流程；
图2示出了在部分自动化手术任务的半自主模式中的本发明实施例的整体结构；
图3示出了主-从机器人-辅助模式中的系统的实施例；
图4示出了具有监督性自主性的系统的实施例；
图5A和5B示出了示例的具有荧光标记的红外图像，其中图5A图示了具有荧光标记的红外图像以及图5B图示了具有强度阈值的二值图像；
图6A和6B示出了示例的当前图像和目标图像，用于在实现图像坐标误差校正的视觉伺服中使用，其中图6A图示了当前图像以及图6B图示了目标图像；
图7示出了利用双模式内窥镜用于自动吻合术的实施例；
图8以用NIR和生物可降解夹子进行吻合术的吻合术工具和器官的角度示出了可以如何用荧光标记物标出组织和工具；
图9以将夹子附接至器官的吻合术工具的角度示出了用于自动吻合术的专用工具可以如何进行工作；
图10A、10B和10C示出了双模式内窥镜可以接收的两个图像，以及可以如何叠加它们，其中图10A图示了可见光谱图像，图10B图示了NIR光谱图像，以及图10C图示了叠加的可见图像和NIR图像；
图11A和11B示出了制作用于吻合术的专用夹子的实施例，其中图11A图示了生物可降解夹子、卡环和组织以及图11B图示了夹子如何刺穿两个组织，以及卡环如何在夹子的一个端部上绷紧以将组织固定在一起；
图12示出了用于由临床数据开发自动手术程序的整个过程；以及
图13图示了根据一个实施例的计算设备的框图。
具体实施方式
本发明的实施例描述了用于自动化手术任务的系统。实施例由基于临床数据生成自动手术程序，然后在手术期间对患者应用自动手术程序组成。
图1示出了在外科手术操作中利用所提出的系统的整个工作流程。外科医生通过利用手动工具101开始100手术，执行可以高效地手动进行的操作部分或者为机器人准备手术部位102。一旦已经准备好患者，外科医生于是便将机器人引入到患者中103，并且开始操作的机器人模式104。当部署时，可以将机器人设置为以三个操作模式中的一个进行工作：主/从105，其中外科医生直接通过控制器控制机器人的运动；半自主106，其中机器人在外科医生指导下执行任务；或者监督性自主107，其中机器人在外科医生监督下自主地执行任务。在激活半自主或者监督性自主操作模式之前，外科医生将按需要准备手术部位和手术系统108。外科医生还可以在不同操作模式之间自由地切换109，直到不再需要机器人或者必须移除机器人以继续手术111。在移除机器人之后，外科医生可以使用手动工具，如果需要使机器人返回103，以继续手术101，或者通过执行任何最终任务使操作结束112。
图2、3和4表示所提出系统的不同操作模式。在半自主模式(图2)中，外科医生200在操作期间对自动化程序208提供命令。自动化程序208从基于图像的跟踪模块207接收跟踪信息，将跟踪信息与来自外科医生200的术中命令和患者特定参数210结合并且实时向机器人发送适当命令，以控制手术机器人201和一个或者多个手术工具211(其可以或者可以不专用于当前过程)，以获得预定目标(例如吻合术)。当外科医生200注视视觉显示器209，以及作为监督员213与手术机器人进行交互时，可以给予他/她视觉、声音或者触觉反馈212，不论何时需要时，外科医生200通过主控制台接管控制。
在主从模式(图3)中，外科医生300通过机器人301的主-从控制314控制手术工具。外科医生通过视觉显示器309接收视觉反馈，并且还可以向外科医生提供其它视觉、声音或者触觉反馈312，但控制循环是单独(solely)通过外科医生闭合的。
在监督性自主模式(图4)中，除了当外科医生400停止自主控制并且接管控制413以阻止并发症、校正错误动作或者其它原因时，控制循环是单独通过利用基于图像的跟踪和患者特定参数410的自主程序408而闭合的。
在手术中，外科医生需使用手动工具或者机器人准备手术场景以使自动化程序能够接管。这可以包括但不限于：在目标器官202附近放置工具、移动相机203以提供目标器官202的视野、标记关键参考点、安装额外传感器模块205、标记工作区、以及标记重要组织/器官。一旦建立手术场景，便可以在适当时使用半自主操作模式和监督性自主操作模式。
可见光源204照亮手术场景，允许相机203记录过程的现场图像。图像采集和控制模块206将来自内窥镜/相机203的图像捕获并且数字化，并且将它们提供至基于图像的跟踪模块207和视觉显示器209。视觉显示器209提供获得的视觉图像的图像反馈；视觉显示器209还可以通过对视频叠加以来自额外传感器205或者来自基于图像的跟踪模块207的信息来显示扩大的现实图像。基于图像的跟踪模块207应用图像处理算法以跟踪工具和参考点。将通过连接到传感器并且包括用于图像采集206、自动化程序208、基于图像的跟踪模块207和处理反馈212的软件的计算机来执行这些任务。
根据需要使用额外传感器模块205以使自动化程序更稳健，该额外传感器模块可以根据传感器的性质将来自额外传感器模块205的信息发送至图像采集模块206或者直接发送至自动化程序208。额外传感器模块还可以将来自额外传感器模块205的信息发送至视觉显示器209，用于与视频叠加或者发送给外科医生控制台以提供视觉、声音或者触觉反馈212。
在本发明的一个实施例中，外科医生从可用自动化程序库中选择一系列自动化程序。自动化程序的示例是执行缝合任务的一个自动化程序，其中必须将不同组织上的一个或者多个点缝合或者缝到一起，即，必须相对于组织定位手术工具以执行缝合。
在本发明的一个实施例中，自动化程序利用基于图像的视觉伺服系统，其中使用基于图像的控制规则闭环地控制机器人工具。在视觉伺服中，描绘目标位置处工具的预期图像与描绘当前工具和目标位置的当前图像之间的差异被用于计算图像坐标中的误差。图像坐标中的该误差被用于生成机器人工具朝目标位置的运动。随着机器人工具越来越靠近手术区域中的目标位置，图像空间中的误差越来越小。在最终控制循环迭代处，误差接近零，在该点处，工具已到达图像坐标和笛卡尔机器人坐标两者中的目标位置。这是基于图像的视觉伺服控制循环的核心。如果使用立体相机系统，则可以增大左边和右边图像的坐标以控制更多的机器人工具自由度(DOF)。
本发明的一个实施例使用包括手术区域的可见光谱和/或诸如近红外光谱(NIR，700～1100nm)的其它非可见光内容的图像。例如，在激活自主程序之前，外科医生可以在将使用NIR相机跟踪的目标位置处放置NIR标记。NIR标记与视觉光谱图像的可区别性连同近红外光谱通过血液和组织的能力一起允许在动态手术环境(例如变形软组织)中对目标组织的更稳健的实时跟踪。多光谱光学成像还可以用于检测帮助最优靶向的表面下组织信息。
在本发明的实施例中描述了吻合术的自动化，其中使用缝合线、夹子、胶或者卡钉接合诸如肠子的管状器官。在使用缝合线的半自主吻合术中，外科医生首先在预定方位上准备管状器官并且为程序标记管的缝合线位置或者外周以在视觉上进行跟踪。然后自动化程序自主地将机器人工具移动至所选择的缝合线位置并且执行缝合。在使用缝合线的自主吻合术中，程序首先基于组织的特性(例如机械的、光学的、几何学的)以及机器人工具的最优灵活度的运动学和动态特征来检测最优缝合线位置。一旦检测到最优缝合线位置，自主程序便将工具带至缝合线位置并且执行缝合。
图5和6示出了利用视觉伺服和NIR标记执行吻合术的本发明的一个实施例。在该实施例中，外科医生将荧光NIR标记500放置在目标缝合线位置处以为自主程序准备手术部位。视觉系统获得可见光谱图像和近红外光谱图像(图5A)两者，允许视觉伺服通过NIR标记500可靠地实时跟踪NIR标记的工具501和缝合线位置502。可以执行以辅助跟踪的图像处理的一个示例是红外线阈值二值图像(图5B)，其清楚地将标记区与非标记区区别开。然后视觉伺服将机器人工具602朝向缝合线部位601移动603(图6A)，使得图像和笛卡儿坐标空间中的误差接近零(图6B)。一旦到达缝合线部位，自主程序便在移动到下一个缝合线部位上之前放置缝合线。
为了进一步地辅助对在动态和变形手术环境中的组织的跟踪，本发明的某些实施例可以具有获得关于手术工作空间的3D信息的装置。该装置的一个实施例使用两个相机，其允许通过立体三角测量算法提取3D深度信息。另一个实施例涉及使用结构光3D扫描仪获得3D信息。另一个实施例涉及通过光场相机获得3D信息。
图7示出了利用具有NIR标记的视觉伺服用于吻合术的实施例的系统图。以手动模式将荧光标记部署在器官702(例如要吻合术的胆管两侧)上并且两个光源704和715照射场景。一个光源704是可见光源，能够获得器官的正常图像。另一个光源715是窄带光源(例如在近红外范围中)，是根据荧光材料的激发波长选择的。可见光和荧光图像两者由双模式内窥镜703捕获并且被发送给图像采集和控制模块706，该图像采集和控制模块706接着会将图像发送至视觉显示器709用于叠加并且发送至基于图像的跟踪模块707用于处理。自动化程序的视觉伺服控制系统708利用荧光标记以变得更稳健，允许自动化程序适当地移动机器人701和专用吻合术工具711以进行预期过程(吻合术)。
图8、9和10示出了用NIR标记和生物可降解夹子执行吻合术的本发明的实施例。为了准备操作的监督性自主模式，围绕吻合术部位802和工具811(可选的)传送荧光标记816。工具部署可以用于执行吻合术的生物可降解夹子817(在图11中提供了更多细节)。在图9中，为自主程序提供荧光标记916的图像，该荧光标记的图像连同其它传感器数据一起用于将工具911引导至将部署夹子917以执行吻合术的吻合术部位902。图10表明了在手术区域中有视觉梗阻1018的情况下使用荧光标记的潜在益处。尽管梗阻会削弱可见光谱中吻合术部位的视野(图10A)，但是某些荧光染剂发射可以通过梗阻的红外光(图10B)。通过结合来自不同光谱的信息(图10C)，使得视觉跟踪系统更稳健。
图11示出了用于执行吻合术的生物可降解夹子1117的一个实施例。夹子刺穿要接合的两个组织1102，并且通过围绕生物可降解夹子1117的尾部绷紧生物可降解卡环1119而固定就位。
图12表示用于由临床数据开发自动手术程序的一般工作流程。处理临床数据120以获得原始数据集121。这由来自相机或者内窥镜的视觉数据、(如果机器人执行手术)运动学和触觉信息以及其它有关数据(诸如整个手术期间患者的情况、结果、生命特征等等)组成。手动地或者通过使用计算机算法(例如模式识别)分析这些数据以产生关于手术的时空信息集122。该集合包括工具运动、工具定位、生命器官/结构的位置和移动、可行的参考点、组织变形以及诸如某些运动与患者结果之间的相关性的其它信息。在此阶段还可以通过比较不同经验的外科医生的移动来识别无效移动，其可以在编程期间删除并且被识别用于将来培训外科医生。这可以由来自机器人模仿学习的技术实现，其中当执行相似策略时收集来自专业操作者的感觉数据。感觉数据(例如输入设备的轨迹)首先进行缩放并且标准化，然后进行参数化。使用例如线性子空间方法(例如主成分分析(PCA))从相同任务的策略重复中学习参数。然后每个专家策略能够由不同参数曲线的线性组合表示。可以通过结合外科医生由于他们手的灵活度约束通常不会做出的移动进一步地优化移动。然后该过程的时空数据与手术机器人的性能123(例如速度和灵活度)和组织特征124(例如预期的移动、组织刚度)相结合以产生特定于手术过程的自动化程序125。程序包括过程中的移动模式、结合不同感觉信息以产生移动模式的控制系统、必须提供的关键信息集(例如参考点、生命器官/血管)、限制因素集(例如速度极限和空间限制)以及牵涉的组织的变形/移动模型。还可以实现更新方法以结合更专业的外科医生的临床数据以随着时间的过去帮助提高该自动过程。可以通过硬件(例如图13的描述中找到的硬件)实现上面记录的描述的基于程序或算法的元件中的每一个。在图12中，计算机1299包括执行上面描述的过程的CPU 1200。可以在存储器1202中存储过程数据和指令。这些过程和指令还可以存储在存储介质磁盘1204(例如硬盘驱动器(HDD)或者便携式存储介质)上或者可以远程存储。此外，要求保护的进步并不由存储发明过程的指令的计算机可读介质的形式所限制。例如，指令可以存储在CD、DVD上，可以存储在FLASH存储器、RAM、ROM、PROM、EPROM、EEPROM、硬盘或者系统与其进行通信的任何其它信息处理设备(例如服务器或者计算机)中。
此外，要求保护的改进可以被提供为结合CPU 1200和操作系统(例如Microsoft Windows 7、UNIX、Solaris、LINUX、AppleMAC-OS以及本领域技术人员已知的其它系统)一起执行的操作系统的实用应用、后台守护进程或者组件或者它们的组合。
CPU 1200可以是来自美国Intel的Xenon或者Core处理器，或者是来自美国AMD的Opteron处理器，或者可以是将由本领域普通技术人员辨识的其它处理器类型。可选地，如本领域普通技术人员将辨识的，可以在FPGA、ASIC、PLD上或者使用离散逻辑电路实现CPU 1200。此外，CPU 1200可以实现为并行合作工作的多个处理器，以执行上面描述的发明过程的指令。
图13中的计算机1299还包括用于与网络1250接口的网络控制器1206，例如来自美国Intel公司的Intel Ethernet PRO网络接口卡。如可以理解的，网络1250可以是公共网络(例如因特网)或者专用网络(例如LAN或者WAN网络)或它们的任意组合并且还可以包括PSTN或者ISDN子网络。网络1250还可以是有线的(例如以太网)或者可以是无线的(例如包括EDGE、3G和4G无线蜂窝系统的蜂窝网络)。无线网络还可以是WiFi、Bluetooth或者已知的任何其它无线通信形式。
计算机1299还包括显示器控制器1208(例如来自美国NVIDIA公司的NVIDIA GeForce GTX或者Quadro图形适配器)，用于与显示器1210(例如Hewlett Packard HPL2445w的LCD监视器)接口。通用I/O接口1212与键盘和/或鼠标1214以及触摸屏面板1216接口，触摸屏面板1216在显示器1210上或者与显示器1210分离。通用I/O接口还连接至各种外围设备1218，该外围设备1218包括打印机和扫描仪，例如来自Hewlett Packard的OfficeJet或者DeskJet。先前在上面的示例性实施例中描述的外围设备元件可以由图13的示例性实施例中的外围设备1218体现。
还可以在计算机1299中提供声音控制器1220(例如来自Creative的Sound Blaster X-Fi Titanium)以与扬声器/传声器1222接口，从而提供声音和/或音乐。扬声器/传声器1222还可以用于接受口述词作为用于控制机器人引导的医疗过程系统或者用于提供相对于目标特性的位置和/或特性信息的命令。
通用存储控制器1224将存储介质磁盘1204与通信总线1226连接，该通信总线可以是ISA、EISA、VESA、PCI或者类似总线，用于互连机器人引导的医疗过程系统的所有组件。由于这些特性是公知的，为了简便起见，此处省略了对显示器1210、键盘和/或鼠标1214以及显示器控制器1208、存储控制器1224、网络控制器1206、声音控制器1220和通用I/O接口1212的一般特性和功能性的描述。
显然，根据上面的教导，本公开可以有许多的修改和变化。因此，将理解，在所附的权利要求范围内，可以以上述具体描述以外的方式实施本发明。例如，如果以不同的顺序执行所公开的技术的步骤，如果所公开系统中的组件以不同方式组合，或者如果以其它组件替换或补充组件，则可以实现有利结果。可以在硬件或者由硬件执行的软件中执行此处描述的功能、过程和算法，该硬件包括计算机处理器和/或可编程处理电路，该计算机处理器和/或可编程处理电路配置为执行程序代码和/或计算机指令以执行此处描述的功能、过程和算法。处理电路包括编程的处理器，作为包括电路的处理器。处理电路也包括例如布置以实现所述功能的常规电路组件和专用集成电路(ASIC)的设备。
此处描述的功能和特征也可以由各种分布式的系统组件执行。例如，一个或多个处理器可以执行这些系统功能，其中处理器跨越在网络中通信的多个组件分布。除各种人机接口和/或通信设备(例如，显示器监视器、智能电话、平板电脑、个人数字助理(PDA))以外，分布式组件可以包括一个或多个客户端和/或服务器机器。网络可以是诸如LAN或WAN的专用网络，或者诸如因特网的公共网络。可以通过直接用户输入接收对系统的输入和/或可以或者实时地或者作为批处理来远程接收对系统的输入。另外，可以在与描述的那些模块或硬件不同的模块或硬件上执行一些实施方式。因此，其它实施方式在可以要求保护的范围内。
应当注意，如在说明书和所附权利要求中所使用的，除非上下文另外明确指出，单数形式“一”(“a”、“an”)和“所述”包括复数形式。

Claims (30)

1.一种用于执行自动手术过程的系统，包括：
传感器，提供关于手术区域的信息；
用户界面，配置为接收由外科医生发布的命令；
反馈设备，配置为将信息中继至所述外科医生；
具有端部部分的手术工具，用于执行手术任务；
手术机器人，耦接至所述手术工具并且定位和定向所述手术工具；
跟踪处理模块，通过处理硬件实现并且配置为
从所述传感器接收传感器数据，
基于所述传感器数据识别目标组织、周围组织和所述手术工具端部部分中的至少一个中的位置，
跟踪所述目标组织、所述周围组织和所述工具端部效应器中的至少一个中的所述识别的位置；以及
控制模块，通过所述处理硬件实现并且配置为通过自动化程序处理从所述传感器、所述跟踪处理模块和所述用户界面接收的数据以生成并且发送命令至所述手术机器人。
2.根据权利要求1所述的系统，其中所述系统包括多个传感器，所述多个传感器提供关于所述手术区域的信息。
3.根据权利要求1所述的系统，其中所述传感器是相机、近红外荧光(NIR)相机、深度相机、结构光3D扫描仪中的一个。
4.根据权利要求1所述的系统，其中所述反馈设备是配置为显示视觉提示或者图像的显示器或者听觉设备。
5.根据权利要求1所述的系统，其中所述跟踪处理模块还配置为使用近红外荧光(NIR)标记跟踪所述目标组织、所述周围组织和所述工具端部效应器中的至少一个中的所识别的位置。
6.根据权利要求1所述的系统，其中所述手术机器人可拆卸地耦接至所述手术工具。
7.根据权利要求1所述的系统，其中所述自动程序是半自主的。
8.根据权利要求1所述的系统，其中所述自动程序是监督性自主的。
9.根据权利要求1所述的系统，其中所述控制模块配置为基于外科医生输入而禁用所述自动化程序并且实现主-从模式。
10.根据权利要求1所述的系统，其中所述控制模块还配置为实现视觉伺服校正。
11.根据权利要求1所述的系统，其中所述自动化程序配置为实现吻合术。
12.根据权利要求1所述的系统，其中所述控制模块还配置为还基于禁飞区、运动远程中心和速度/力极限中的至少一个生成命令。
13.根据权利要求1所述的系统，其中所述自动化程序配置为通过生成和发送命令至所述手术机器人来接合组织。
14.根据权利要求13所述的系统，其中通过缝合线、夹子、卡钉或者粘合剂执行所述组织的所述接合。
15.根据权利要求1所述的系统，其中所述控制模块还配置为实现视觉伺服校正以将所述工具端部部分带至目标。
16.根据权利要求1所述的系统，其中由所述跟踪处理模块识别的所述位置是三维位置。
17.一种生成自动手术程序的计算机实现的方法，包括：
处理临床数据以产生手术的3D空间和时间数据；
获得手术机器人规格和临床参数；
基于所述3D空间和时间数据、所述手术机器人规格和所述临床参数，生成所述自动手术程序。
18.根据权利要求17所述的方法，其中所述临床数据包括从相机或者内窥镜获得的视觉数据、运动学数据或者触觉数据中的至少一个，或者所述临床数据包括患者情况、生命特征和所述手术的结果中的至少一个。
19.根据权利要求17所述的方法，其中所述临床数据包括外科医生经验。
20.根据权利要求17所述的方法，其中所述临床数据的所述处理基于考虑工具运动与手术结果之间的相关性或者外科医生经验水平之间的差异中的至少一个来产生所述手术的所述3D空间和时间数据，以产生更有效的移动。
21.根据权利要求19所述的方法，其中所述3D空间和时间数据包括工具运动、工具定位、生命器官或者结构的位置和移动、可行的参考点以及组织变形中的至少一个。
22.根据权利要求17所述的方法，其中所述手术机器人规格包括手术机器人的速度和精度。
23.根据权利要求17所述的方法，其中所述生成的自动手术程序包括在临床数据中缺少但能够由所述手术机器人实现的手术运动。
24.根据权利要求17所述的方法，其中所述临床参数包括组织特征。
25.根据权利要求24所述的方法,其中所述组织特征包括所述组织的预期移动和刚度或者所述组织的染剂的可吸收性。
26.根据权利要求17所述的方法，其中所述自动手术程序包括：
优选的移动模式，
关键操作信息，
控制模块，配置为基于传感器信息和所述关键操作信息的组合指示机器人运动。
27.根据权利要求26所述的方法，其中所述关键操作信息包括生命器官位置、参考点或者标记、传感器数据和外科医生输入中的至少一个。
28.根据权利要求26所述的方法，其中所述控制模块还配置为还基于包括运动远程中心、禁飞区和速度极限中的至少一个的运动约束来生成所述命令。
29.根据权利要求26所述的方法，其中所述控制模块还配置为还基于组织变形或者移动模型来生成所述命令。
30.根据权利要求17所述的方法，其中所述自动手术程序可用附加临床数据更新。
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