TAToo: Vision-based Joint Tracking of Anatomy and Tool for Skull-base Surgery

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.

Twin-S: A Digital Twin for Skull-base Surgery

Purpose: Digital twins are virtual interactive models of the real world, exhibiting identical behavior and properties. In surgical applications, computational analysis from digital twins can be used, for example, to enhance situational awareness. Methods: We present a digital twin framework for skull-base surgeries, named Twin-S, which can be integrated within various image-guided interventions seamlessly. Twin-S combines high-precision optical tracking and real-time simulation. We rely on rigorous calibration routines to ensure that the digital twin representation precisely mimics all real-world processes. Twin-S models and tracks the critical components of skull-base surgery, including the surgical tool, patient anatomy, and surgical camera. Significantly, Twin-S updates and reflects real-world drilling of the anatomical model in frame rate. Results: We extensively evaluate the accuracy of Twin-S, which achieves an average 1.39 mm error during the drilling process. We further illustrate how segmentation masks derived from the continuously updated digital twin can augment the surgical microscope view in a mixed reality setting, where bone requiring ablation is highlighted to provide surgeons additional situational awareness. Conclusion: We present Twin-S, a digital twin environment for skull-base surgery. Twin-S tracks and updates the virtual model in real-time given measurements from modern tracking technologies. Future research on complementing optical tracking with higher-precision vision-based approaches may further increase the accuracy of Twin-S.

Q-LSTM Language Model -- Decentralized Quantum Multilingual Pre-Trained Language Model for Privacy Protection

Large-scale language models are trained on a massive amount of natural language data that might encode or reflect our private information. With careful manipulation, malicious agents can reverse engineer the training data even if data sanitation and differential privacy algorithms were involved in the pre-training process. In this work, we propose a decentralized training framework to address privacy concerns in training large-scale language models. The framework consists of a cloud quantum language model built with Variational Quantum Classifiers (VQC) for sentence embedding and a local Long-Short Term Memory (LSTM) model. We use both intrinsic evaluation (loss, perplexity) and extrinsic evaluation (downstream sentiment analysis task) to evaluate the performance of our quantum language model. Our quantum model was comparable to its classical counterpart on all the above metrics. We also perform ablation studies to look into the effect of the size of VQC and the size of training data on the performance of the model. Our approach solves privacy concerns without sacrificing downstream task performance. The intractability of quantum operations on classical hardware ensures the confidentiality of the training data and makes it impossible to be recovered by any adversary.