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Article ; Online: Rethinking causality-driven robot tool segmentation with temporal constraints.

Ding, Hao / Wu, Jie Ying / Li, Zhaoshuo / Unberath, Mathias

International journal of computer assisted radiology and surgery

2023 Volume 18, Issue 6, Page(s) 1009–1016

Abstract: Purpose: Vision-based robot tool segmentation plays a fundamental role in surgical robots perception and downstream tasks. CaRTS, based on a complementary causal model, has shown promising performance in unseen counterfactual surgical environments in ... ...

Abstract	Purpose: Vision-based robot tool segmentation plays a fundamental role in surgical robots perception 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 fewer iterations to achieve the same or better performance as CaRTS on different domains. 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.
MeSH term(s)	Humans ; Neural Networks, Computer ; Robotics ; Biomechanical Phenomena ; Image Processing, Computer-Assisted/methods
Language	English
Publishing date	2023-04-07
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2365628-1
ISSN	1861-6429 ; 1861-6410
ISSN (online)	1861-6429
ISSN	1861-6410
DOI	10.1007/s11548-023-02872-8
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Article ; Online: Eye gaze metrics for skill assessment and feedback in kidney stone surgery.

Li, Yizhou / Reed, Amy / Kavoussi, Nicholas / Wu, Jie Ying

International journal of computer assisted radiology and surgery

2023 Volume 18, Issue 6, Page(s) 1127–1134

Abstract: Purpose: Surgical skill assessment is essential for safe operations. In endoscopic kidney stone surgery, surgeons must perform a highly skill-dependent mental mapping from the pre-operative scan to the intraoperative endoscope image. Poor mental mapping ...

Abstract	Purpose: Surgical skill assessment is essential for safe operations. In endoscopic kidney stone surgery, surgeons must perform a highly skill-dependent mental mapping from the pre-operative scan to the intraoperative endoscope image. Poor mental mapping can lead to incomplete exploration of the kidney and high reoperation rates. Yet there are few objective ways to evaluate competency. We propose to use unobtrusive eye-gaze measurements in the task space to evaluate skill and provide feedback. Methods: We capture the surgeons' eye gaze on the surgical monitor with the Microsoft Hololens 2. To enable stable and accurate gaze detection, we develop a calibration algorithm to refine the eye tracking of the Hololens. In addition, we use a QR code to locate the eye gaze on the surgical monitor. We then run a user study with three expert and three novice surgeons. Each surgeon is tasked to locate three needles representing kidney stones in three different kidney phantoms. Results: We find that experts have more focused gaze patterns. They complete the task faster, have smaller total gaze area, and the gaze fewer times outside the area of interest. While fixation to non-fixation ratio did not show significant difference in our findings, tracking the ratio over time shows different patterns between novices and experts. Conclusion: We show that a non-negligible difference holds between novice and expert surgeons' gaze metrics in kidney stone identification in phantoms. Expert surgeons demonstrate more targeted gaze throughout a trial, indicating their higher level of proficiency. To improve the skill acquisition process for novice surgeons, we suggest providing sub-task specific feedback. This approach presents an objective and non-invasive method to assess surgical competence.
MeSH term(s)	Humans ; Fixation, Ocular ; Task Performance and Analysis ; Eye Movements ; Feedback ; Benchmarking ; Clinical Competence ; Kidney Calculi/diagnosis ; Kidney Calculi/surgery ; Kidney
Language	English
Publishing date	2023-05-18
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2365628-1
ISSN	1861-6429 ; 1861-6410
ISSN (online)	1861-6429
ISSN	1861-6410
DOI	10.1007/s11548-023-02901-6
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Book ; Online: Depth Anything in Medical Images

Han, John J. / Acar, Ayberk / Henry, Callahan / Wu, Jie Ying

A Comparative Study

2024

Abstract: Monocular depth estimation (MDE) is a critical component of many medical tracking and mapping algorithms, particularly from endoscopic or laparoscopic video. However, because ground truth depth maps cannot be acquired from real patient data, supervised ... ...

Abstract	Monocular depth estimation (MDE) is a critical component of many medical tracking and mapping algorithms, particularly from endoscopic or laparoscopic video. However, because ground truth depth maps cannot be acquired from real patient data, supervised learning is not a viable approach to predict depth maps for medical scenes. Although self-supervised learning for MDE has recently gained attention, the outputs are difficult to evaluate reliably and each MDE's generalizability to other patients and anatomies is limited. This work evaluates the zero-shot performance of the newly released Depth Anything Model on medical endoscopic and laparoscopic scenes. We compare the accuracy and inference speeds of Depth Anything with other MDE models trained on general scenes as well as in-domain models trained on endoscopic data. Our findings show that although the zero-shot capability of Depth Anything is quite impressive, it is not necessarily better than other models in both speed and performance. We hope that this study can spark further research in employing foundation models for MDE in medical scenes. Comment: 10 pages, 2 figures, 3 tables
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2024-01-29
Publishing country	us
Document type	Book ; Online
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Article: Towards navigation in endoscopic kidney surgery based on preoperative imaging.

Acar, Ayberk / Lu, Daiwei / Wu, Yifan / Oguz, Ipek / Kavoussi, Nicholas / Wu, Jie Ying

Healthcare technology letters

2023 Volume 11, Issue 2-3, Page(s) 67–75

Abstract: Endoscopic renal surgeries have high re-operation rates, particularly for lower volume surgeons. Due to the limited field and depth of view of current endoscopes, mentally mapping preoperative computed tomography (CT) images of patient anatomy to the ... ...

Abstract	Endoscopic renal surgeries have high re-operation rates, particularly for lower volume surgeons. Due to the limited field and depth of view of current endoscopes, mentally mapping preoperative computed tomography (CT) images of patient anatomy to the surgical field is challenging. The inability to completely navigate the intrarenal collecting system leads to missed kidney stones and tumors, subsequently raising recurrence rates. A guidance system is proposed to estimate the endoscope positions within the CT to reduce re-operation rates. A Structure from Motion algorithm is used to reconstruct the kidney collecting system from the endoscope videos. In addition, the kidney collecting system is segmented from CT scans using 3D U-Net to create a 3D model. The two collecting system representations can then be registered to provide information on the relative endoscope position. Correct reconstruction and localization of intrarenal anatomy and endoscope position is demonstrated. Furthermore, a 3D map is created supported by the RGB endoscope images to reduce the burden of mental mapping during surgery. The proposed reconstruction pipeline has been validated for guidance. It can reduce the mental burden for surgeons and is a step towards the long-term goal of reducing re-operation rates in kidney stone surgery.
Language	English
Publishing date	2023-12-13
Publishing country	England
Document type	Journal Article
ZDB-ID	2782924-8
ISSN	2053-3713
ISSN	2053-3713
DOI	10.1049/htl2.12059
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Article: Intraoperative gaze guidance with mixed reality.

Acar, Ayberk / Atoum, Jumanh / Reed, Amy / Li, Yizhou / Kavoussi, Nicholas / Wu, Jie Ying

Healthcare technology letters

2023 Volume 11, Issue 2-3, Page(s) 85–92

Abstract: Efficient communication and collaboration are essential in the operating room for successful and safe surgery. While many technologies are improving various aspects of surgery, communication between attending surgeons, residents, and surgical teams is ... ...

Abstract	Efficient communication and collaboration are essential in the operating room for successful and safe surgery. While many technologies are improving various aspects of surgery, communication between attending surgeons, residents, and surgical teams is still limited to verbal interactions that are prone to misunderstandings. Novel modes of communication can increase speed and accuracy, and transform operating rooms. A mixed reality (MR) based gaze sharing application on Microsoft HoloLens 2 headset that can help expert surgeons indicate specific regions, communicate with decreased verbal effort, and guide novices throughout an operation is presented. The utility of the application is tested with a user study of endoscopic kidney stone localization completed by urology experts and novice surgeons. Improvement is observed in the NASA task load index surveys (up to 25.23%), in the success rate of the task (6.98% increase in localized stone percentage), and in gaze analyses (up to 31.99%). The proposed application shows promise in both operating room applications and surgical training tasks.
Language	English
Publishing date	2023-12-13
Publishing country	England
Document type	Journal Article
ZDB-ID	2782924-8
ISSN	2053-3713
ISSN	2053-3713
DOI	10.1049/htl2.12061
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Article ; Online: ASO Author Reflections: Augmented Reality in Head and Neck Oncology.

Prasad, Kavita / Lewis, James S / Wu, Jie Ying / Rosenthal, Eben / Topf, Michael C

Annals of surgical oncology

2023 Volume 30, Issue 8, Page(s) 5001–5002

MeSH term(s)	Humans ; Augmented Reality ; Neck ; Medical Oncology ; Surgical Oncology
Language	English
Publishing date	2023-05-05
Publishing country	United States
Document type	Journal Article ; Comment
ZDB-ID	1200469-8
ISSN	1534-4681 ; 1068-9265
ISSN (online)	1534-4681
ISSN	1068-9265
DOI	10.1245/s10434-023-13582-5
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Article ; Online: Cognitive effort detection for tele-robotic surgery via personalized pupil response modeling.

Büter, Regine / Soberanis-Mukul, Roger D / Shankar, Rohit / Ruiz Puentes, Paola / Ghazi, Ahmed / Wu, Jie Ying / Unberath, Mathias

International journal of computer assisted radiology and surgery

2024

Abstract: Purpose: Gaze tracking and pupillometry are established proxies for cognitive load, giving insights into a user's mental effort. In tele-robotic surgery, knowing a user's cognitive load can inspire novel human-machine interaction designs, fostering ... ...

Abstract	Purpose: Gaze tracking and pupillometry are established proxies for cognitive load, giving insights into a user's mental effort. In tele-robotic surgery, knowing a user's cognitive load can inspire novel human-machine interaction designs, fostering contextual surgical assistance systems and personalized training programs. While pupillometry-based methods for estimating cognitive effort have been proposed, their application in surgery is limited by the pupil's sensitivity to brightness changes, which can mask pupil's response to cognitive load. Thus, methods considering pupil and brightness conditions are essential for detecting cognitive effort in unconstrained scenarios. Methods: To contend with this challenge, we introduce a personalized pupil response model integrating pupil and brightness-based features. Discrepancies between predicted and measured pupil diameter indicate dilations due to non-brightness-related sources, i.e., cognitive effort. Combined with gaze entropy, it can detect cognitive load using a random forest classifier. To test our model, we perform a user study with the da Vinci Research Kit, where 17 users perform pick-and-place tasks in addition to auditory tasks known to generate cognitive effort responses. Results: We compare our method to two baselines (BCPD and CPD), demonstrating favorable performance in varying brightness conditions. Our method achieves an average true positive rate of 0.78, outperforming the baselines (0.57 and 0.64). Conclusion: We present a personalized brightness-aware model for cognitive effort detection able to operate under unconstrained brightness conditions, comparing favorably to competing approaches, contributing to the advancement of cognitive effort detection in tele-robotic surgery. Future work will consider alternative learning strategies, handling the difficult positive-unlabeled scenario in user studies, where only some positive and no negative events are reliably known.
Language	English
Publishing date	2024-04-08
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2365628-1
ISSN	1861-6429 ; 1861-6410
ISSN (online)	1861-6429
ISSN	1861-6410
DOI	10.1007/s11548-024-03108-z
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Article ; Online: Leveraging vision and kinematics data to improve realism of biomechanic soft tissue simulation for robotic surgery.

Wu, Jie Ying / Kazanzides, Peter / Unberath, Mathias

International journal of computer assisted radiology and surgery

2020 Volume 15, Issue 5, Page(s) 811–818

Abstract: Purpose: Surgical simulations play an increasingly important role in surgeon education and developing algorithms that enable robots to perform surgical subtasks. To model anatomy, finite element method (FEM) simulations have been held as the gold ... ...

Abstract	Purpose: Surgical simulations play an increasingly important role in surgeon education and developing algorithms that enable robots to perform surgical subtasks. To model anatomy, finite element method (FEM) simulations have been held as the gold standard for calculating accurate soft tissue deformation. Unfortunately, their accuracy is highly dependent on the simulation parameters, which can be difficult to obtain. Methods: In this work, we investigate how live data acquired during any robotic endoscopic surgical procedure may be used to correct for inaccurate FEM simulation results. Since FEMs are calculated from initial parameters and cannot directly incorporate observations, we propose to add a correction factor that accounts for the discrepancy between simulation and observations. We train a network to predict this correction factor. Results: To evaluate our method, we use an open-source da Vinci Surgical System to probe a soft tissue phantom and replay the interaction in simulation. We train the network to correct for the difference between the predicted mesh position and the measured point cloud. This results in 15-30% improvement in the mean distance, demonstrating the effectiveness of our approach across a large range of simulation parameters. Conclusion: We show a first step towards a framework that synergistically combines the benefits of model-based simulation and real-time observations. It corrects discrepancies between simulation and the scene that results from inaccurate modeling parameters. This can provide a more accurate simulation environment for surgeons and better data with which to train algorithms.
MeSH term(s)	Algorithms ; Biomechanical Phenomena/physiology ; Computer Simulation ; Deep Learning ; Humans ; Models, Anatomic ; Neural Networks, Computer ; Phantoms, Imaging ; Robotic Surgical Procedures/education
Language	English
Publishing date	2020-04-22
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2365628-1
ISSN	1861-6429 ; 1861-6410
ISSN (online)	1861-6429
ISSN	1861-6410
DOI	10.1007/s11548-020-02139-6
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Article ; Online: Cross-modal self-supervised representation learning for gesture and skill recognition in robotic surgery.

Wu, Jie Ying / Tamhane, Aniruddha / Kazanzides, Peter / Unberath, Mathias

International journal of computer assisted radiology and surgery

2021 Volume 16, Issue 5, Page(s) 779–787

Abstract: Purpose: Multi- and cross-modal learning consolidates information from multiple data sources which may offer a holistic representation of complex scenarios. Cross-modal learning is particularly interesting, because synchronized data streams are ... ...

Abstract	Purpose: Multi- and cross-modal learning consolidates information from multiple data sources which may offer a holistic representation of complex scenarios. Cross-modal learning is particularly interesting, because synchronized data streams are immediately useful as self-supervisory signals. The prospect of achieving self-supervised continual learning in surgical robotics is exciting as it may enable lifelong learning that adapts to different surgeons and cases, ultimately leading to a more general machine understanding of surgical processes. Methods: We present a learning paradigm using synchronous video and kinematics from robot-mediated surgery. Our approach relies on an encoder-decoder network that maps optical flow to the corresponding kinematics sequence. Clustering on the latent representations reveals meaningful groupings for surgeon gesture and skill level. We demonstrate the generalizability of the representations on the JIGSAWS dataset by classifying skill and gestures on tasks not used for training. Results: For tasks seen in training, we report a 59 to 70% accuracy in surgical gestures classification. On tasks beyond the training setup, we note a 45 to 65% accuracy. Qualitatively, we find that unseen gestures form clusters in the latent space of novice actions, which may enable the automatic identification of novel interactions in a lifelong learning scenario. Conclusion: From predicting the synchronous kinematics sequence, optical flow representations of surgical scenes emerge that separate well even for new tasks that the model had not seen before. While the representations are useful immediately for a variety of tasks, the self-supervised learning paradigm may enable research in lifelong and user-specific learning.
MeSH term(s)	Algorithms ; Biomechanical Phenomena ; Gestures ; Humans ; Learning ; Machine Learning ; Reproducibility of Results ; Robotic Surgical Procedures ; Robotics ; Surgeons ; Video Recording
Language	English
Publishing date	2021-03-24
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2365628-1
ISSN	1861-6429 ; 1861-6410
ISSN (online)	1861-6429
ISSN	1861-6410
DOI	10.1007/s11548-021-02343-y
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Article: Development of an augmented reality guidance system for head and neck cancer resection.

Tong, Guansen / Xu, Jiayi / Pfister, Michael / Atoum, Jumanh / Prasad, Kavita / Miller, Alexis / Topf, Michael / Wu, Jie Ying

Healthcare technology letters

2023 Volume 11, Issue 2-3, Page(s) 93–100

Abstract: The use of head-mounted augmented reality (AR) for surgeries has grown rapidly in recent years. AR aids in intraoperative surgical navigation through overlaying three-dimensional (3D) holographic reconstructions of medical data. However, performing AR ... ...

Abstract	The use of head-mounted augmented reality (AR) for surgeries has grown rapidly in recent years. AR aids in intraoperative surgical navigation through overlaying three-dimensional (3D) holographic reconstructions of medical data. However, performing AR surgeries on complex areas such as the head and neck region poses challenges in terms of accuracy and speed. This study explores the feasibility of an AR guidance system for resections of positive tumour margins in a cadaveric specimen. The authors present an intraoperative solution that enables surgeons to upload and visualize holographic reconstructions of resected cadaver tissues. The solution involves using a 3D scanner to capture detailed scans of the resected tissue, which are subsequently uploaded into our software. The software converts the scans of resected tissues into specimen holograms that are viewable through a head-mounted AR display. By re-aligning these holograms with cadavers with gestures or voice commands, surgeons can navigate the head and neck tumour site. This workflow can run concurrently with frozen section analysis. On average, the authors achieve an uploading time of 2.98 min, visualization time of 1.05 min, and re-alignment time of 4.39 min, compared to the 20 to 30 min typical for frozen section analysis. The authors achieve a mean re-alignment error of 3.1 mm. The authors' software provides a foundation for new research and product development for using AR to navigate complex 3D anatomy in surgery.
Language	English
Publishing date	2023-12-07
Publishing country	England
Document type	Journal Article
ZDB-ID	2782924-8
ISSN	2053-3713
ISSN	2053-3713
DOI	10.1049/htl2.12062
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