1
|
Huang L, Kurz C, Freislederer P, Manapov F, Corradini S, Niyazi M, Belka C, Landry G, Riboldi M. Simultaneous object detection and segmentation for patient-specific markerless lung tumor tracking in simulated radiographs with deep learning. Med Phys 2024; 51:1957-1973. [PMID: 37683107 DOI: 10.1002/mp.16705] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 04/23/2023] [Accepted: 05/12/2023] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND Real-time tumor tracking is one motion management method to address motion-induced uncertainty. To date, fiducial markers are often required to reliably track lung tumors with X-ray imaging, which carries risks of complications and leads to prolonged treatment time. A markerless tracking approach is thus desirable. Deep learning-based approaches have shown promise for markerless tracking, but systematic evaluation and procedures to investigate applicability in individual cases are missing. Moreover, few efforts have been made to provide bounding box prediction and mask segmentation simultaneously, which could allow either rigid or deformable multi-leaf collimator tracking. PURPOSE The purpose of this study was to implement a deep learning-based markerless lung tumor tracking model exploiting patient-specific training which outputs both a bounding box and a mask segmentation simultaneously. We also aimed to compare the two kinds of predictions and to implement a specific procedure to understand the feasibility of markerless tracking on individual cases. METHODS We first trained a Retina U-Net baseline model on digitally reconstructed radiographs (DRRs) generated from a public dataset containing 875 CT scans and corresponding lung nodule annotations. Afterwards, we used an independent cohort of 97 lung patients to develop a patient-specific refinement procedure. In order to determine the optimal hyperparameters for automatic patient-specific training, we selected 13 patients for validation where the baseline model predicted a bounding box on planning CT (PCT)-DRR with intersection over union (IoU) with the ground-truth higher than 0.7. The final test set contained the remaining 84 patients with varying PCT-DRR IoU. For each testing patient, the baseline model was refined on the PCT-DRR to generate a patient-specific model, which was then tested on a separate 10-phase 4DCT-DRR to mimic the intrafraction motion during treatment. A template matching algorithm served as benchmark model. The testing results were evaluated by four metrics: the center of mass (COM) error and the Dice similarity coefficient (DSC) for segmentation masks, and the center of box (COB) error and the DSC for bounding box detections. Performance was compared to the benchmark model including statistical testing for significance. RESULTS A PCT-DRR IoU value of 0.2 was shown to be the threshold dividing inconsistent (68%) and consistent (100%) success (defined as mean bounding box DSC > 0.6) of PS models on 4DCT-DRRs. Thirty-seven out of the eighty-four testing cases had a PCT-DRR IoU above 0.2. For these 37 cases, the mean COM error was 2.6 mm, the mean segmentation DSC was 0.78, the mean COB error was 2.7 mm, and the mean box DSC was 0.83. Including the validation cases, the model was applicable to 50 out of 97 patients when using the PCT-DRR IoU threshold of 0.2. The inference time per frame was 170 ms. The model outperformed the benchmark model on all metrics, and the comparison was significant (p < 0.001) over the 37 PCT-DRR IoU > 0.2 cases, but not over the undifferentiated 84 testing cases. CONCLUSIONS The implemented patient-specific refinement approach based on a pre-trained baseline model was shown to be applicable to markerless tumor tracking in simulated radiographs for lung cases.
Collapse
Affiliation(s)
- Lili Huang
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, München, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Philipp Freislederer
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Farkhad Manapov
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Maximilian Niyazi
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Claus Belka
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and LMU University Hospital Munich, Germany
- Bavarian Cancer Research Center (BZKF), Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Marco Riboldi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, München, Germany
| |
Collapse
|
2
|
Fan Q, Pham H, Li X, Zhang P, Zhang L, Fu Y, Huang B, Li C, Cuaron J, Cerviño L, Moran JM, Li T. Toward quantitative intrafractional monitoring in paraspinal SBRT using a proprietary software application: clinical implementation and patient results. Phys Med Biol 2024; 69:045015. [PMID: 38241714 DOI: 10.1088/1361-6560/ad2099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 01/19/2024] [Indexed: 01/21/2024]
Abstract
Objective.We report on paraspinal motion and the clinical implementation of our proprietary software that leverages Varian's intrafraction motion review (IMR) capability for quantitative tracking of the spine during paraspinal SBRT. The work is based on our prior development and analysis on phantoms.Approach.To address complexities in patient anatomy, digitally reconstructed radiographs (DRR's) that highlight only the spine or hardware were constructed as tracking reference. Moreover, a high-pass filter and first-pass coarse search were implemented to enhance registration accuracy and stability. For evaluation, 84 paraspinal SBRT patients with sites spanning across the entire vertebral column were enrolled with prescriptions ranging from 24 to 40 Gy in one to five fractions. Treatments were planned and delivered with 9 IMRT beams roughly equally distributed posteriorly. IMR was triggered every 200 or 500 MU for each beam. During treatment, the software grabbed the IMR image, registered it with the corresponding DRR, and displayed the motion result in near real-time on auto-pilot mode. Four independent experts completed offline manual registrations as ground truth for tracking accuracy evaluation.Main results.Our software detected ≥1.5 mm and ≥2 mm motions among 17.1% and 6.6% of 1371 patient images, respectively, in either lateral or longitudinal direction. In the validation set of 637 patient images, 91.9% of the tracking errors compared to manual registration fell within ±0.5 mm in either direction. Given a motion threshold of 2 mm, the software accomplished a 98.7% specificity and a 93.9% sensitivity in deciding whether to interrupt treatment for patient re-setup.Significance.Significant intrafractional motion exists in certain paraspinal SBRT patients, supporting the need for quantitative motion monitoring during treatment. Our improved software achieves high motion tracking accuracy clinically and provides reliable guidance for treatment intervention. It offers a practical solution to ensure accurate delivery of paraspinal SBRT on a conventional Linac platform.
Collapse
Affiliation(s)
- Qiyong Fan
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 1275 York Avenue, NY 10065, United States of America
| | - Hai Pham
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 1275 York Avenue, NY 10065, United States of America
| | - Xiang Li
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 1275 York Avenue, NY 10065, United States of America
| | - Pengpeng Zhang
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 1275 York Avenue, NY 10065, United States of America
| | - Lei Zhang
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 1275 York Avenue, NY 10065, United States of America
| | - Yabo Fu
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 1275 York Avenue, NY 10065, United States of America
| | - Bohong Huang
- Stony Brook University, Department of Applied Mathematics and Statistics, 100 Nicolls Rd, Stony Brook, NY 11794, United States of America
| | - Cindy Li
- Carnegie Mellon University, Mellon College of Science, 5000 Forbes Ave, Pittsburgh, PA 15213, United States of America
| | - John Cuaron
- Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, 1275 York Avenue, NY 10065, United States of America
| | - Laura Cerviño
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 1275 York Avenue, NY 10065, United States of America
| | - Jean M Moran
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 1275 York Avenue, NY 10065, United States of America
| | - Tianfang Li
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 1275 York Avenue, NY 10065, United States of America
| |
Collapse
|
3
|
Ramasubramanian AK, Kazasidis M, Fay B, Papakostas N. On the Evaluation of Diverse Vision Systems towards Detecting Human Pose in Collaborative Robot Applications. Sensors (Basel) 2024; 24:578. [PMID: 38257671 PMCID: PMC10818797 DOI: 10.3390/s24020578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 12/26/2023] [Accepted: 01/05/2024] [Indexed: 01/24/2024]
Abstract
Tracking human operators working in the vicinity of collaborative robots can improve the design of safety architecture, ergonomics, and the execution of assembly tasks in a human-robot collaboration scenario. Three commercial spatial computation kits were used along with their Software Development Kits that provide various real-time functionalities to track human poses. The paper explored the possibility of combining the capabilities of different hardware systems and software frameworks that may lead to better performance and accuracy in detecting the human pose in collaborative robotic applications. This study assessed their performance in two different human poses at six depth levels, comparing the raw data and noise-reducing filtered data. In addition, a laser measurement device was employed as a ground truth indicator, together with the average Root Mean Square Error as an error metric. The obtained results were analysed and compared in terms of positional accuracy and repeatability, indicating the dependence of the sensors' performance on the tracking distance. A Kalman-based filter was applied to fuse the human skeleton data and then to reconstruct the operator's poses considering their performance in different distance zones. The results indicated that at a distance less than 3 m, Microsoft Azure Kinect demonstrated better tracking performance, followed by Intel RealSense D455 and Stereolabs ZED2, while at ranges higher than 3 m, ZED2 had superior tracking performance.
Collapse
Affiliation(s)
| | | | | | - Nikolaos Papakostas
- Laboratory for Advanced Manufacturing Simulation and Robotics, School of Mechanical and Materials Engineering, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland; (A.K.R.); (M.K.); (B.F.)
| |
Collapse
|
4
|
Cronin NJ, Walker J, Tucker CB, Nicholson G, Cooke M, Merlino S, Bissas A. Feasibility of OpenPose markerless motion analysis in a real athletics competition. Front Sports Act Living 2024; 5:1298003. [PMID: 38250008 PMCID: PMC10796501 DOI: 10.3389/fspor.2023.1298003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024] Open
Abstract
This study tested the performance of OpenPose on footage collected by two cameras at 200 Hz from a real-life competitive setting by comparing it with manually analyzed data in SIMI motion. The same take-off recording from the men's Long Jump finals at the 2017 World Athletics Championships was used for both approaches (markerless and manual) to reconstruct the 3D coordinates from each of the camera's 2D coordinates. Joint angle and Centre of Mass (COM) variables during the final step and take-off phase of the jump were determined. Coefficients of Multiple Determinations (CMD) for joint angle waveforms showed large variation between athletes with the knee angle values typically being higher (take-off leg: 0.727 ± 0.242; swing leg: 0.729 ± 0.190) than those for hip (take-off leg: 0.388 ± 0.193; swing leg: 0.370 ± 0.227) and ankle angle (take-off leg: 0.247 ± 0.172; swing leg: 0.155 ± 0.228). COM data also showed considerable variation between athletes and parameters, with position (0.600 ± 0.322) and projection angle (0.658 ± 0.273) waveforms generally showing better agreement than COM velocity (0.217 ± 0.241). Agreement for discrete data was generally poor with high random error for joint kinematics and COM parameters at take-off and an average ICC across variables of 0.17. The poor agreement statistics and a range of unrealistic values returned by the pose estimation underline that OpenPose is not suitable for in-competition performance analysis in events such as the long jump, something that manual analysis still achieves with high levels of accuracy and reliability.
Collapse
Affiliation(s)
- Neil J. Cronin
- Neuromuscular Research Centre, Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
- School of Education and Sciences, University of Gloucestershire, Gloucester, United Kingdom
| | - Josh Walker
- Carnegie School of Sport, Leeds Beckett University, Leeds, United Kingdom
| | | | - Gareth Nicholson
- Carnegie School of Sport, Leeds Beckett University, Leeds, United Kingdom
| | - Mark Cooke
- Carnegie School of Sport, Leeds Beckett University, Leeds, United Kingdom
| | - Stéphane Merlino
- International Relations and Development Department, World Athletics, Monaco, Monaco
| | - Athanassios Bissas
- School of Education and Sciences, University of Gloucestershire, Gloucester, United Kingdom
- Carnegie School of Sport, Leeds Beckett University, Leeds, United Kingdom
| |
Collapse
|
5
|
Pfau T, Landsbergen K, Davis BL, Kenny O, Kernot N, Rochard N, Porte-Proust M, Sparks H, Takahashi Y, Toth K, Scott WM. Comparing Inertial Measurement Units to Markerless Video Analysis for Movement Symmetry in Quarter Horses. Sensors (Basel) 2023; 23:8414. [PMID: 37896509 PMCID: PMC10610735 DOI: 10.3390/s23208414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/07/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023]
Abstract
BACKGROUND With an increasing number of systems for quantifying lameness-related movement asymmetry, between-system comparisons under non-laboratory conditions are important for multi-centre or referral-level studies. This study compares an artificial intelligence video app to a validated inertial measurement unit (IMU) gait analysis system in a specific group of horses. METHODS Twenty-two reining Quarter horses were equipped with nine body-mounted IMUs while being videoed with a smartphone app. Both systems quantified head and pelvic movement symmetry during in-hand trot (hard/soft ground) and on the lunge (left/right rein, soft ground). Proportional limits of agreement (pLoA) were established. RESULTS Widths of pLoA were larger for head movement (29% to 50% in-hand; 22% to 38% on lunge) than for pelvic movement (13% to 24% in-hand; 14% to 24% on lunge). CONCLUSION The between-system pLoAs exceed current "lameness thresholds" aimed at identifying the affected limb(s) in lame horses. They also exceed published limits of agreement for stride-matched data but are similar to repeatability values and "lameness thresholds" from "non-lame" horses. This is encouraging for multi-centre studies and referral-level veterinary practice. The narrower pLoA values for pelvic movement asymmetry are particularly encouraging, given the difficulty of grading hind limb lameness "by eye".
Collapse
Affiliation(s)
- Thilo Pfau
- Faculty of Kinesiology, University of Calgary, Calgary, AB T2N 1N4, Canada
- Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada (W.M.S.)
| | - Kiki Landsbergen
- Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada (W.M.S.)
| | - Brittany L. Davis
- Faculty of Kinesiology, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Olivia Kenny
- Faculty of Biomedical Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Nicole Kernot
- School of Agricultural, Environmental and Veterinary Sciences, Charles Sturt University, North Wagga, NSW 2650, Australia
| | - Nina Rochard
- Ecole Nationale Vétérinaire de Toulouse, 31300 Toulouse, France
| | | | - Holly Sparks
- Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada (W.M.S.)
| | - Yuji Takahashi
- Faculty of Kinesiology, University of Calgary, Calgary, AB T2N 1N4, Canada
- Japan Racing Association, Tokyo 105-0003, Japan
| | - Kasara Toth
- Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada (W.M.S.)
| | - W. Michael Scott
- Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada (W.M.S.)
| |
Collapse
|
6
|
Bumgarner JR, Becker-Krail DD, White RC, Nelson RJ. Machine learning and deep learning frameworks for the automated analysis of pain and opioid withdrawal behaviors. Front Neurosci 2022; 16:953182. [PMID: 36225736 PMCID: PMC9549170 DOI: 10.3389/fnins.2022.953182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 09/08/2022] [Indexed: 11/23/2022] Open
Abstract
The automation of behavioral tracking and analysis in preclinical research can serve to advance the rate of research outcomes, increase experimental scalability, and challenge the scientific reproducibility crisis. Recent advances in the efficiency, accuracy, and accessibility of deep learning (DL) and machine learning (ML) frameworks are enabling this automation. As the ongoing opioid epidemic continues to worsen alongside increasing rates of chronic pain, there are ever-growing needs to understand opioid use disorders (OUDs) and identify non-opioid therapeutic options for pain. In this review, we examine how these related needs can be advanced by the development and validation of DL and ML resources for automated pain and withdrawal behavioral tracking. We aim to emphasize the utility of these tools for automated behavioral analysis, and we argue that currently developed models should be deployed to address novel questions in the fields of pain and OUD research.
Collapse
|
7
|
Abe T, Kinsella I, Saxena S, Buchanan EK, Couto J, Briggs J, Kitt SL, Glassman R, Zhou J, Paninski L, Cunningham JP. Neuroscience Cloud Analysis As a Service: An open-source platform for scalable, reproducible data analysis. Neuron 2022; 110:2771-2789.e7. [PMID: 35870448 PMCID: PMC9464703 DOI: 10.1016/j.neuron.2022.06.018] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 05/06/2022] [Accepted: 06/22/2022] [Indexed: 10/17/2022]
Abstract
A key aspect of neuroscience research is the development of powerful, general-purpose data analyses that process large datasets. Unfortunately, modern data analyses have a hidden dependence upon complex computing infrastructure (e.g., software and hardware), which acts as an unaddressed deterrent to analysis users. Although existing analyses are increasingly shared as open-source software, the infrastructure and knowledge needed to deploy these analyses efficiently still pose significant barriers to use. In this work, we develop Neuroscience Cloud Analysis As a Service (NeuroCAAS): a fully automated open-source analysis platform offering automatic infrastructure reproducibility for any data analysis. We show how NeuroCAAS supports the design of simpler, more powerful data analyses and that many popular data analysis tools offered through NeuroCAAS outperform counterparts on typical infrastructure. Pairing rigorous infrastructure management with cloud resources, NeuroCAAS dramatically accelerates the dissemination and use of new data analyses for neuroscientific discovery.
Collapse
Affiliation(s)
- Taiga Abe
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University Medical Center, Columbia University, New York, NY 10027, USA
| | - Ian Kinsella
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Department of Statistics, Columbia University, New York, NY 10027, USA
| | - Shreya Saxena
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Department of Statistics, Columbia University, New York, NY 10027, USA; Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32607, USA
| | - E Kelly Buchanan
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University Medical Center, Columbia University, New York, NY 10027, USA
| | - Joao Couto
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - John Briggs
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA
| | - Sian Lee Kitt
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Ryan Glassman
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - John Zhou
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Liam Paninski
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University Medical Center, Columbia University, New York, NY 10027, USA; Department of Statistics, Columbia University, New York, NY 10027, USA
| | - John P Cunningham
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Department of Statistics, Columbia University, New York, NY 10027, USA.
| |
Collapse
|
8
|
Karashchuk P, Rupp KL, Dickinson ES, Walling-Bell S, Sanders E, Azim E, Brunton BW, Tuthill JC. Anipose: A toolkit for robust markerless 3D pose estimation. Cell Rep 2021; 36:109730. [PMID: 34592148 PMCID: PMC8498918 DOI: 10.1016/j.celrep.2021.109730] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 06/15/2021] [Accepted: 08/27/2021] [Indexed: 01/12/2023] Open
Abstract
Quantifying movement is critical for understanding animal behavior. Advances in computer vision now enable markerless tracking from 2D video, but most animals move in 3D. Here, we introduce Anipose, an open-source toolkit for robust markerless 3D pose estimation. Anipose is built on the 2D tracking method DeepLabCut, so users can expand their existing experimental setups to obtain accurate 3D tracking. It consists of four components: (1) a 3D calibration module, (2) filters to resolve 2D tracking errors, (3) a triangulation module that integrates temporal and spatial regularization, and (4) a pipeline to structure processing of large numbers of videos. We evaluate Anipose on a calibration board as well as mice, flies, and humans. By analyzing 3D leg kinematics tracked with Anipose, we identify a key role for joint rotation in motor control of fly walking. To help users get started with 3D tracking, we provide tutorials and documentation at http://anipose.org/.
Collapse
Affiliation(s)
- Pierre Karashchuk
- Neuroscience Graduate Program, University of Washington, Seattle, WA, USA
| | - Katie L. Rupp
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Evyn S. Dickinson
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Sarah Walling-Bell
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Elischa Sanders
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Eiman Azim
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Bingni W. Brunton
- Department of Biology, University of Washington, Seattle, WA, USA,Senior author,Correspondence: (B.W.B.), (J.C.T.)
| | - John C. Tuthill
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA,Senior author,Lead contact,Correspondence: (B.W.B.), (J.C.T.)
| |
Collapse
|
9
|
Liu X, Yu SY, Flierman NA, Loyola S, Kamermans M, Hoogland TM, De Zeeuw CI. OptiFlex: Multi-Frame Animal Pose Estimation Combining Deep Learning With Optical Flow. Front Cell Neurosci 2021; 15:621252. [PMID: 34122011 PMCID: PMC8194069 DOI: 10.3389/fncel.2021.621252] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 04/26/2021] [Indexed: 11/29/2022] Open
Abstract
Animal pose estimation tools based on deep learning have greatly improved animal behaviour quantification. These tools perform pose estimation on individual video frames, but do not account for variability of animal body shape in their prediction and evaluation. Here, we introduce a novel multi-frame animal pose estimation framework, referred to as OptiFlex. This framework integrates a flexible base model (i.e., FlexibleBaseline), which accounts for variability in animal body shape, with an OpticalFlow model that incorporates temporal context from nearby video frames. Pose estimation can be optimised using multi-view information to leverage all four dimensions (3D space and time). We evaluate FlexibleBaseline using datasets of four different lab animal species (mouse, fruit fly, zebrafish, and monkey) and introduce an intuitive evaluation metric-adjusted percentage of correct key points (aPCK). Our analyses show that OptiFlex provides prediction accuracy that outperforms current deep learning based tools, highlighting its potential for studying a wide range of behaviours across different animal species.
Collapse
Affiliation(s)
- XiaoLe Liu
- Faculty of Mathematics, University of Waterloo, Waterloo, ON, Canada
| | - Si-yang Yu
- Department of Neuroscience, Erasmus MC, Rotterdam, Netherlands
| | - Nico A. Flierman
- Department of Neuroscience, Erasmus MC, Rotterdam, Netherlands
- Netherlands Institute for Neuroscience, Royal Academy of Arts and Sciences, Amsterdam, Netherlands
| | - Sebastián Loyola
- Netherlands Institute for Neuroscience, Royal Academy of Arts and Sciences, Amsterdam, Netherlands
| | - Maarten Kamermans
- Netherlands Institute for Neuroscience, Royal Academy of Arts and Sciences, Amsterdam, Netherlands
- Department of Biomedical Physics and Biomedical Photonics, Amsterdam UMC location AMC, University of Amsterdam, Amsterdam, Netherlands
| | - Tycho M. Hoogland
- Department of Neuroscience, Erasmus MC, Rotterdam, Netherlands
- Netherlands Institute for Neuroscience, Royal Academy of Arts and Sciences, Amsterdam, Netherlands
| | - Chris I. De Zeeuw
- Department of Neuroscience, Erasmus MC, Rotterdam, Netherlands
- Netherlands Institute for Neuroscience, Royal Academy of Arts and Sciences, Amsterdam, Netherlands
| |
Collapse
|
10
|
Schlüter M, Glandorf L, Gromniak M, Saathoff T, Schlaefer A. Concept for Markerless 6D Tracking Employing Volumetric Optical Coherence Tomography. Sensors (Basel) 2020; 20:s20092678. [PMID: 32397153 PMCID: PMC7248981 DOI: 10.3390/s20092678] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 04/21/2020] [Accepted: 05/05/2020] [Indexed: 11/16/2022]
Abstract
Optical tracking systems are widely used, for example, to navigate medical interventions. Typically, they require the presence of known geometrical structures, the placement of artificial markers, or a prominent texture on the target’s surface. In this work, we propose a 6D tracking approach employing volumetric optical coherence tomography (OCT) images. OCT has a micrometer-scale resolution and employs near-infrared light to penetrate few millimeters into, for example, tissue. Thereby, it provides sub-surface information which we use to track arbitrary targets, even with poorly structured surfaces, without requiring markers. Our proposed system can shift the OCT’s field-of-view in space and uses an adaptive correlation filter to estimate the motion at multiple locations on the target. This allows one to estimate the target’s position and orientation. We show that our approach is able to track translational motion with root-mean-squared errors below 0.25 mm and in-plane rotations with errors below 0.3°. For out-of-plane rotations, our prototypical system can achieve errors around 0.6°.
Collapse
|
11
|
Ostrek M, Rhodin H, Fua P, Müller E, Spörri J. Are Existing Monocular Computer Vision-Based 3D Motion Capture Approaches Ready for Deployment? A Methodological Study on the Example of Alpine Skiing. Sensors (Basel) 2019; 19:E4323. [PMID: 31590465 DOI: 10.3390/s19194323] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 10/01/2019] [Accepted: 10/02/2019] [Indexed: 11/17/2022]
Abstract
In this study, we compared a monocular computer vision (MCV)-based approach with the golden standard for collecting kinematic data on ski tracks (i.e., video-based stereophotogrammetry) and assessed its deployment readiness for answering applied research questions in the context of alpine skiing. The investigated MCV-based approach predicted the three-dimensional human pose and ski orientation based on the image data from a single camera. The data set used for training and testing the underlying deep nets originated from a field experiment with six competitive alpine skiers. The normalized mean per joint position error of the MVC-based approach was found to be 0.08 ± 0.01m. Knee flexion showed an accuracy and precision (in parenthesis) of 0.4 ± 7.1° (7.2 ± 1.5°) for the outside leg, and -0.2 ± 5.0° (6.7 ± 1.1°) for the inside leg. For hip flexion, the corresponding values were -0.4 ± 6.1° (4.4° ± 1.5°) and -0.7 ± 4.7° (3.7 ± 1.0°), respectively. The accuracy and precision of skiing-related metrics were revealed to be 0.03 ± 0.01 m (0.01 ± 0.00 m) for relative center of mass position, -0.1 ± 3.8° (3.4 ± 0.9) for lean angle, 0.01 ± 0.03 m (0.02 ± 0.01 m) for center of mass to outside ankle distance, 0.01 ± 0.05 m (0.03 ± 0.01 m) for fore/aft position, and 0.00 ± 0.01 m2 (0.01 ± 0.00 m2) for drag area. Such magnitudes can be considered acceptable for detecting relevant differences in the context of alpine skiing.
Collapse
|
12
|
Mori S, Sakata Y, Hirai R, Furuichi W, Shimabukuro K, Kohno R, Koom WS, Kasai S, Okaya K, Iseki Y. Commissioning of a fluoroscopic-based real-time markerless tumor tracking system in a superconducting rotating gantry for carbon-ion pencil beam scanning treatment. Med Phys 2019; 46:1561-1574. [PMID: 30689205 DOI: 10.1002/mp.13403] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 01/15/2019] [Accepted: 01/21/2019] [Indexed: 11/09/2022] Open
Abstract
PURPOSE To perform the final quality assurance of our fluoroscopic-based markerless tumor tracking for gated carbon-ion pencil beam scanning (C-PBS) radiotherapy using a rotating gantry system, we evaluated the geometrical accuracy and tumor tracking accuracy using a moving chest phantom with simulated respiration. METHODS The positions of the dynamic flat panel detector (DFPD) and x-ray tube are subject to changes due to gantry sag. To compensate for this, we generated a geometrical calibration table (gantry flex map) in 15° gantry angle steps by the bundle adjustment method. We evaluated five metrics: (a) Geometrical calibration was evaluated by calculating chest phantom positional error using 2D/3D registration software for each 5° step of the gantry angle. (b) Moving phantom displacement accuracy was measured (±10 mm in 1-mm steps) with a laser sensor. (c) Tracking accuracy was evaluated with machine learning (ML) and multi-template matching (MTM) algorithms, which used fluoroscopic images and digitally reconstructed radiographic (DRR) images as training data. The chest phantom was continuously moved ±10 mm in a sinusoidal path with a moving cycle of 4 s and respiration was simulated with ±5 mm expansion/contraction with a cycle of 2 s. This was performed with the gantry angle set at 0°, 45°, 120°, and 240°. (d) Four types of interlock function were evaluated: tumor velocity, DFPD image brightness variation, tracking anomaly detection, and tracking positional inconsistency in between the two corresponding rays. (e) Gate on/off latency, gating control system latency, and beam irradiation latency were measured using a laser sensor and an oscilloscope. RESULTS By applying the gantry flex map, phantom positional accuracy was improved from 1.03 mm/0.33° to <0.45 mm/0.27° for all gantry angles. The moving phantom displacement error was 0.1 mm. Due to long computation time, the tracking accuracy achieved with ML was <0.49 mm (=95% confidence interval [CI]) for imaging rates of 15 and 7.5 fps; those at 30 fps were decreased to 1.84 mm (95% CI: 1.79 mm-1.92 mm). The tracking positional accuracy with MTM was <0.52 mm (=95% CI) for all gantry angles and imaging frame rates. The tumor velocity interlock signal delay time was 44.7 ms (=1.3 frame). DFPD image brightness interlock latency was 34 ms (=1.0 frame). The tracking positional error was improved from 2.27 ± 2.67 mm to 0.25 ± 0.24 mm by the tracking anomaly detection interlock function. Tracking positional inconsistency interlock signal was output within 5.0 ms. The gate on/off latency was <82.7 ± 7.6 ms. The gating control system latency was <3.1 ± 1.0 ms. The beam irradiation latency was <8.7 ± 1.2 ms. CONCLUSIONS Our markerless tracking system is now ready for clinical use. We hope to shorten the computation time needed by the ML algorithm at 30 fps in the future.
Collapse
Affiliation(s)
- Shinichiro Mori
- Research Center for Charged Particle Therapy, National Institute of Radiological Sciences, Chiba, 263-8555, Japan
| | - Yukinobu Sakata
- Research and Development Center, Toshiba Corporation, Kanagawa, 212-4582, Japan
| | - Ryusuke Hirai
- Research and Development Center, Toshiba Corporation, Kanagawa, 212-4582, Japan
| | | | | | - Ryosuke Kohno
- Research Center for Charged Particle Therapy, National Institute of Radiological Sciences, Chiba, 263-8555, Japan
| | - Woong Sub Koom
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, 03722, Korea
| | - Shigeru Kasai
- Toshiba Energy System & Solutions Corporation, Kanagawa, 212-8585, Japan
| | - Keiko Okaya
- Toshiba Energy System & Solutions Corporation, Kanagawa, 212-8585, Japan
| | - Yasushi Iseki
- Toshiba Energy System & Solutions Corporation, Kanagawa, 212-8585, Japan
| |
Collapse
|