Purpose: Tracking the 3D motion of the surgical tool and the patient anatomy is a fundamental requirement for computer-assisted skull-base surgery. The estimated motion can be used both for intra-operative guidance and for downstream skill analysis. Recovering such motion solely from surgical videos is desirable, as it is compliant with current clinical workflows and instrumentation. Methods: We present Tracker of Anatomy and Tool (TAToo). TAToo jointly tracks the rigid 3D motion of patient skull and surgical drill from stereo microscopic videos. TAToo estimates motion via an iterative optimization process in an end-to-end differentiable form. For robust tracking performance, TAToo adopts a probabilistic formulation and enforces geometric constraints on the object level. Results: We validate TAToo on both simulation data, where ground truth motion is available, as well as on anthropomorphic phantom data, where optical tracking provides a strong baseline. We report sub-millimeter and millimeter inter-frame tracking accuracy for skull and drill, respectively, with rotation errors below 1{\deg}. We further illustrate how TAToo may be used in a surgical navigation setting. Conclusion: We present TAToo, which simultaneously tracks the surgical tool and the patient anatomy in skull-base surgery. TAToo directly predicts the motion from surgical videos, without the need of any markers. Our results show that the performance of TAToo compares favorably to competing approaches. Future work will include fine-tuning of our depth network to reach a 1 mm clinical accuracy goal desired for surgical applications in the skull base.
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Purpose: Vision-based robot tool segmentation plays a fundamental role in surgical robots and downstream tasks. CaRTS, based on a complementary causal model, has shown promising performance in unseen counterfactual surgical environments in the presence of smoke, blood, etc. However, CaRTS requires over 30 iterations of optimization to converge for a single image due to limited observability. Method: To address the above limitations, we take temporal relation into consideration and propose a temporal causal model for robot tool segmentation on video sequences. We design an architecture named Temporally Constrained CaRTS (TC-CaRTS). TC-CaRTS has three novel modules to complement CaRTS - temporal optimization pipeline, kinematics correction network, and spatial-temporal regularization. Results: Experiment results show that TC-CaRTS requires much fewer iterations to achieve the same or better performance as CaRTS. TC- CaRTS also has the same or better performance in different domains compared to CaRTS. All three modules are proven to be effective. Conclusion: We propose TC-CaRTS, which takes advantage of temporal constraints as additional observability. We show that TC-CaRTS outperforms prior work in the robot tool segmentation task with improved convergence speed on test datasets from different domains.
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现在,人工智能(AI)可以自动解释医学图像以供临床使用。但是,AI在介入图像中的潜在用途(相对于参与分类或诊断的图像),例如在手术期间的指导,在很大程度上尚未开发。这是因为目前,使用现场分析对现场手术收集的数据进行了事后分析,这是因为手术AI系统具有基本和实际限制,包括道德考虑,费用,可扩展性,数据完整性以及缺乏地面真相。在这里,我们证明从人类模型中创建逼真的模拟图像是可行的替代方法,并与大规模的原位数据收集进行了补充。我们表明,对现实合成数据的训练AI图像分析模型,结合当代域的概括或适应技术,导致在实际数据上的模型与在精确匹配的真实数据训练集中训练的模型相当地执行的模型。由于从基于人类的模型尺度的合成生成培训数据,因此我们发现我们称为X射线图像分析的模型传输范式(我们称为Syntheex)甚至可以超越实际数据训练的模型,因为训练的有效性较大的数据集。我们证明了合成在三个临床任务上的潜力:髋关节图像分析,手术机器人工具检测和COVID-19肺病变分割。 Synthex提供了一个机会,可以极大地加速基于X射线药物的智能系统的概念,设计和评估。此外,模拟图像环境还提供了测试新颖仪器,设计互补手术方法的机会,并设想了改善结果,节省时间或减轻人为错误的新技术,从实时人类数据收集的道德和实际考虑方面摆脱了人为错误。
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机器人辅助手术期间机器人工具的基于视觉的分割可以使下游应用,例如增强现实反馈,同时允许机器人运动学的不准确性。随着深度学习的引入,提出了许多直接和仅从图像中求解仪器分割的方法。尽管这些方法在基准数据集上取得了显着的进展,但与其鲁棒性有关的基本挑战仍然存在。我们提出了CARTS,这是一种因果关系驱动的机器人工具分割算法,它是基于机器人工具分割任务的互补因果模型而设计的。 CART没有直接从观察到的图像中直接推断分段掩码,而是通过向前的运动学和可区分渲染来更新最初错误的机器人运动学参数,将工具模型与图像观测值对齐,以优化图像特征特征相似性端到端。我们基准在精确控制场景中生成的DVRK的合成和真实数据基准了竞争技术,以允许反事实合成。在训练域测试数据上,卡车在对反事实更改的测试数据上进行测试时,骰子得分为93.4(骰子得分为91.8),表现出低亮度,烟雾,血液和背景模式改变。这比基于SOTA图像的方法的骰子得分分别与95.0和86.7的骰子分数进行了比较。未来的工作将涉及加速推车以实现视频帧速率,并估计闭塞在实践中的影响。尽管存在这些局限性,但我们的结果还是很有希望的:除了达到高分割精度外,购物车还提供了真正的机器人运动学的估计,这可能会受益于诸如力估计等应用。代码可在以下网址找到:https://github.com/hding2455/carts
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机器学习透明度(ML),试图揭示复杂模型的工作机制。透明ML承诺推进人为因素在目标用户中以人为本的人体目标的工程目标。从以人为本的设计视角,透明度不是ML模型的属性,而是一种能力,即算法与用户之间的关系;因此,与用户的迭代原型和评估对于获得提供透明度的充足解决方案至关重要。然而,由于有限的可用性和最终用户,遵循了医疗保健和医学图像分析的人以人为本的设计原则是具有挑战性的。为了调查医学图像分析中透明ML的状态,我们对文献进行了系统审查。我们的评论在医学图像分析应用程序的透明ML的设计和验证方面揭示了多种严重的缺点。我们发现,大多数研究到达迄今为止透明度作为模型本身的属性,类似于任务性能,而不考虑既未开发也不考虑最终用户也不考虑评估。此外,缺乏用户研究以及透明度声明的偶发验证将当代研究透明ML的医学图像分析有可能对用户难以理解的风险,因此临床无关紧要。为了缓解即将到来的研究中的这些缺点,同时承认人以人为中心设计在医疗保健中的挑战,我们介绍了用于医学图像分析中的透明ML系统的系统设计指令。 Intrult指南建议形成的用户研究作为透明模型设计的第一步,以了解用户需求和域要求。在此过程之后,会产生支持设计选择的证据,最终增加了算法提供透明度的可能性。
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用于计算机视觉任务的深度神经网络在越来越安全 - 严重和社会影响的应用中部署,激励需要在各种,天然存在的成像条件下关闭模型性能的差距。在包括对抗机器学习的多种上下文中尤为色难地使用的鲁棒性,然后指在自然诱导的图像损坏或改变下保持模型性能。我们进行系统审查,以识别,分析和总结当前定义以及对计算机愿景深度学习中的非对抗鲁棒性的进展。我们发现,该研究领域已经收到了相对于对抗机器学习的不成比例地注意力,但存在显着的稳健性差距,这些差距通常表现在性能下降中与对抗条件相似。为了在上下文中提供更透明的稳健性定义,我们引入了数据生成过程的结构因果模型,并将非对抗性鲁棒性解释为模型在损坏的图像上的行为,其对应于来自未纳入数据分布的低概率样本。然后,我们确定提高神经网络鲁棒性的关键架构,数据增强和优化策略。这种稳健性的这种因果观察表明,目前文献中的常见做法,关于鲁棒性策略和评估,对应于因果概念,例如软干预导致成像条件的决定性分布。通过我们的调查结果和分析,我们提供了对未来研究如何可能介意这种明显和显着的非对抗的鲁棒性差距的观点。
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时间一致的深度估计对于诸如增强现实之类的实时应用至关重要。虽然立体声深度估计已经接受了显着的注意,导致逐帧的改进,虽然相对较少的工作集中在跨越帧的时间一致性。实际上,基于我们的分析,当前立体声深度估计技术仍然遭受不良时间一致性。由于并发对象和摄像机运动,在动态场景中稳定深度是挑战。在在线设置中,此过程进一步加剧,因为只有过去的帧可用。在本文中,我们介绍了一种技术,在线设置中的动态场景中产生时间一致的深度估计。我们的网络增强了具有新颖运动和融合网络的当前每帧立体声网络。通过预测每个像素SE3变换,运动网络占对象和相机运动。融合网络通过用回归权重聚合当前和先前预测来提高预测的一致性。我们在各种数据集中进行广泛的实验(合成,户外,室内和医疗)。在零射泛化和域微调中,我们证明我们所提出的方法在数量和定性的时间稳定和每个帧精度方面优于竞争方法。我们的代码将在线提供。
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外科模拟器不仅允许规划和培训复杂的程序,而且还提供了为算法开发产生结构化数据的能力,这可以应用于图像引导的计算机辅助干预措施。虽然在外科医生或数据生成引擎的发展培训平台上,但我们知识的这两个功能尚未一起提供。我们展示了我们的开发成本效益和协同框架,命名为异步多体框架加(AMBF +),它与练习其外科技能的用户同时生成下游算法开发的数据。 AMBF +在虚拟现实(VR)设备上提供立体显示器,并触觉外科仿真的触觉反馈。它还可以生成不同的数据,例如对象姿势和分段图。 AMBF +采用柔性插件设置设计,可允许仿真仿真不同外科手术。我们将AMBF +的一个用例显示为虚拟钻探模拟器,用于横向颅底手术,用户可以使用虚拟手术钻积极地修改患者解剖结构。我们进一步演示如何生成的数据可用于验证和培训下游计算机视觉算法
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We present a novel hybrid learning method, HyLEAR, for solving the collision-free navigation problem for self-driving cars in POMDPs. HyLEAR leverages interposed learning to embed knowledge of a hybrid planner into a deep reinforcement learner to faster determine safe and comfortable driving policies. In particular, the hybrid planner combines pedestrian path prediction and risk-aware path planning with driving-behavior rule-based reasoning such that the driving policies also take into account, whenever possible, the ride comfort and a given set of driving-behavior rules. Our experimental performance analysis over the CARLA-CTS1 benchmark of critical traffic scenarios revealed that HyLEAR can significantly outperform the selected baselines in terms of safety and ride comfort.
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Machine learning (ML) models can leak information about users, and differential privacy (DP) provides a rigorous way to bound that leakage under a given budget. This DP budget can be regarded as a new type of compute resource in workloads of multiple ML models training on user data. Once it is used, the DP budget is forever consumed. Therefore, it is crucial to allocate it most efficiently to train as many models as possible. This paper presents the scheduler for privacy that optimizes for efficiency. We formulate privacy scheduling as a new type of multidimensional knapsack problem, called privacy knapsack, which maximizes DP budget efficiency. We show that privacy knapsack is NP-hard, hence practical algorithms are necessarily approximate. We develop an approximation algorithm for privacy knapsack, DPK, and evaluate it on microbenchmarks and on a new, synthetic private-ML workload we developed from the Alibaba ML cluster trace. We show that DPK: (1) often approaches the efficiency-optimal schedule, (2) consistently schedules more tasks compared to a state-of-the-art privacy scheduling algorithm that focused on fairness (1.3-1.7x in Alibaba, 1.0-2.6x in microbenchmarks), but (3) sacrifices some level of fairness for efficiency. Therefore, using DPK, DP ML operators should be able to train more models on the same amount of user data while offering the same privacy guarantee to their users.
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