Project Tree
Now limiting view to projects in the following categories:
All Topics :: Biocomputational Focus :: Image Processing [Remove This Filter]
All Topics > Biological Applications > Neuromuscular System |
Browse By: |
13 projects in result set.
OpenArm: Volumetric & Time Series Models of Muscle Deformation
- We invite anyone in the research community to use the OpenArm and OpenArm Multisensor data sets to validate existing muscle deformation models or to devise new ones.
Full details can be found in the following papers:
Laura A. Hallock, Bhavna Sud, Chris Mitchell, Eric Hu, Fayyaz Ahamed, Akash Velu, Amanda Schwartz, and Ruzena Bajcsy. "Toward Real-Time Muscle Force Inference and Device Control via Optical-Flow-Tracked Muscle Deformation." In IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE). IEEE, 2021. (Under review.)
Laura Hallock, Akash Velu, Amanda Schwartz, and Ruzena Bajcsy. "Muscle deformation correlates with output force during isometric contraction." In IEEE RAS/EMBS International Conference on Biomedical Robotics & Biomechatronics (BioRob). IEEE, 2020. (Available at https://people.eecs.berkeley.edu/~lhallock/publication/hallock2020biorob.)
Yonatan Nozik*, Laura A. Hallock*, Daniel Ho, Sai Mandava, Chris Mitchell, Thomas Hui Li, and Ruzena Bajcsy, "OpenArm 2.0: Automated Segmentation of 3D Tissue Structures for Multi-Subject Study of Muscle Deformation Dynamics," in International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2019. *Equal contribution. (Available at https://people.eecs.berkeley.edu/~lhallock/publication/nozikhallock2019embc.)
Laura Hallock, Akira Kato, and Ruzena Bajcsy. "Empirical quantification and modeling of muscle deformation: Toward ultrasound-driven assistive device control." In IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018. (Available at https://people.eecs.berkeley.edu/~lhallock/publication/hallock2018icra.)
This project is currently in development in the Human-Assistive Robotic Technologies (HART) Lab at the University of California, Berkeley (http://hart.berkeley.edu). | |
|
Registered: 2018-11-28 20:40 |
Practical Annotation and Exchange of Virtual Anatomy
- Representation of anatomy in a virtual form is at the heart of clinical decision making, biomedical research, and medical training. Virtual anatomy is not limited to description of geometry but also requires appropriate and efficient labeling of regions - to define spatial relationships and interactions between anatomical objects; effective strategies for pointwise operations - to define local properties, biological or otherwise; and support for diverse data formats and standards - to facilitate exchange between clinicians, scientists, engineers, and the general public. Development of aeva, a free and open source software package (library, user interfaces, extensions) capable of automated and interactive operations for virtual anatomy annotation and exchange, is in response to these currently unmet requirements. This site serves for aeva outreach, including dissemination the software and use cases. The use cases drive design and testing of aeva features and demonstrate various workflows that rely on virtual anatomy.
aeva downloads:
Downloads (https://simtk.org/frs/?group_id=1767)
Kitware data repository (https://data.kitware.com/#folder/5e7a4690af2e2eed356a17f2)
aeva documentation:
Guides and tutorials (https://aeva.readthedocs.io)
aeva videos:
Short instructions (https://www.youtube.com/channel/UCubfUe40LXvBs86UyKci0Fw)
aeva source code:
Kitware source code repository (https://gitlab.kitware.com/aeva)
aeva forum:
Forums (https://simtk.org/plugins/phpBB/indexPhpbb.php?group_id=1767 ) | |
|
Registered: 2019-08-28 01:27 |
Matlab-Opensim Interfaces
- Matlab is a common analysis tool used for data manipulation, signal processing and function integration. These features can be used in conjunction with simulation tools provided by the Opensim interface.
This project provides tools for using different aspects of Opensim within the Matlab environment. This includes 1) using the command line tools by generating XML setup files etc (Scaling, Inverse Kinematics, Inverse Dynamics, Forward Dynamics) 2) using the Java classes that the Opensim GUI is built on to access aspects of the Opensim API.
Provided in this project are -
1) Tools for taking motion capture data from C3D files and generating the required input files (marker files {*.trc} motion files {*.mot}, GRF xml files {*.xml}) as well as setup files for each of the different tools that can be called from the command line. Example data from different models and data sets are provided including example pipelines to analyse data using Opensim. Some of this implementation has taken inspiration from Tim Dorn's excellent GaitExtract toolbox. A new page with more up-to-date tools can be found here - http://simtk-confluence.stanford.edu:8080/display/OpenSim/Tools+for+Preparing+Motion+Data
2)Matlab functions and example scripts for accessing the Opensim API through Matlab. This utilises the Java wrapping classes that the Opensim GUI is built on. Examples are shown to open and edit models as well as perform a 'Muscle Analysis'. Please now use the inbuilt support from Opensim rather than this toolbox! (http://simtk-confluence.stanford.edu:8080/display/OpenSim/Scripting+with+Matlab) | |
|
Activity Percentile: 83.97 Registered: 2011-08-06 20:22 |
Motion Analyst Software Suite
- This project is a suite of motion analysis tools that use images from common video cameras to measure 2D and 3D motions. Locations of markers in 2D space can be tracked in time using MotionAnalyst2D. When interested in 3D reconstruction, 2D analysis needs to be completer using two cameras that simultaneously capture the images. By combining the two 2D results with the camera orientation calibration data, then 3D locations for those original markers can be reconstructed using MotionAnalyst3D. | |
|
Activity Percentile: 47.71 Registered: 2011-12-01 21:24 |
Fiber Tractography for Finite-Element Modeling of Transversely Isotropic Tissues
- This project demonstrates the process for fiber tractography of complex biological tissues with transverse isotropy, such as tendon and muscle. This is important for finite element studies of these tissues, as the fiber direction must be specified in the constitutive model. This project contains code, models, and data that can be used to reproduce the results of our publication on this technique. The supplied instructional videos will enable researchers to easily and efficiently apply this method to a variety of other tissues. The software used in the fiber tractography process and demonstrated in this project is Matlab, Autodesk Inventor (free for educators), and Autodesk Simulation CFD (free for educators). Full demonstrations and process instructions can be found in the 7 videos posted at https://vimeo.com/album/3414604:
Contents:
Chapter 1: Introduction (2:35)
This video introduces the CFD fiber tractography software pipeline
<!-- This version of the embed code is no longer supported. Learn more: https://vimeo.com/s/tnm --> <object width="500" height="281"><param name="allowfullscreen" value="true" /><param name="allowscriptaccess" value="always" /><param name="movie" value="https://vimeo.com/moogaloop.swf?clip_id=129107314&force_embed=vimeo.com&fullscreen=1" /><embed src="https://vimeo.com/moogaloop.swf?clip_id=129107314&force_embed=vimeo.com&fullscreen=1" type="application/x-shockwave-flash" allowfullscreen="true" allowscriptaccess="always" width="500" height="281"></embed></object>
Chapter 2: Supplementary materials code, models and data (20:21)
This video shows the shared models, code, and data posted online at simtk.org/m3lab_cfd4fea.
Chapter 3: Finite element simulations (5:38)
This video shows finite element simulations using the fiber mapping process.
Chapter 4: Iliacus example walkthrough (21:38)
This video shows the step-by-step process for fiber mapping the iliacus muscle (a hip flexor).
Chapter 5: Bflh example walkthrough (12:09)
This video shows the step-by-step process for fiber mapping the biceps femoris longhead muscle (a hamstring).
Chapter 6: Autodesk Inventor segmentation (9:09)
This video shows how to do segmentation of medical images in Autodesk Inventor in order to simplify the solid model for the CFD and FEA software.
Chapter 7: Curved inlet surfaces (6:28)
This video shows how to create curved inlet surfaces for use in Autodesk Simulation CFD. | |
|
Activity Percentile: 36.64 Registered: 2015-05-28 18:52 |
Application of PCA and LASSO methods to visualize and quantify neurodegeneration
- This is a public data repository for the PLOS ONE (https://journals.plos.org/plosone/) publication entitled "Data-driven, voxel-based analysis of brain PET images: application of PCA and LASSO methods to visualize and quantify patterns of neurodegeneration". The repository contains imaging and clinical data from healthy controls and Parkinson's disease patients that is necessary to replicate the findings of the study. | |
|
Registered: 2018-10-18 17:57 |
Investigating the effects of pelvic floor muscles during pregnancy
- Developing a risk predictive Model about how the pelvic floor muscles change during pregnancy and how they stretch during the delivery in order to identify and discover knowledge about these muscles to avoid damage during delivery. Which damage increases the risk of urinary incontinence or pelvic organ prolapse later in life. | |
|
Registered: 2016-11-22 20:54 |
NMBL Image to Model Pipeline
- The NMBL Pipeline is a version of NAMIC's (www.na-mic.org) 3D Slicer, adapted to the needs of the Neuromuscular Biomechanics Lab (NMBL) at Stanford University. Slicer is an open-source software tool for performing a diverse array of medical image processing activities within one freely available, easily extensible kit. NMBL Pipeline is intended to coincide with NAMIC's Slicer, and is developed along with 3D Slicer in full collaboration with NAMIC. The differences between NMBL Pipeline and Slicer will be minimal, and probably will include the absence of some of Slicer's modules in NMBL Pipeline, and perhaps some differences in default value settings. This project will continue to be developed for use by NMBL and other members of the general Slicer user community.
I intend to use SimTK.org in exactly those ways that are intended: namely to make my software available to SimTK users and provide users with documentation, while the users are encouraged to provide feedback to me for improvements. | |
|
Activity Percentile: 0.00 Registered: 2005-07-25 22:48 |
Muscle Fiber Analysis and Visualization with Diffusion Tensor Imaging
- This software allows users to attain functionally pertinent biological information about the fibrous structure of living muscle tissue using Diffusion Tensor Images (DTI). | |
|
Activity Percentile: 0.00 Registered: 2006-04-10 22:45 |
Finite Element Mesh Overclosure Reduction and Slicing (FEMORS)
- The code was developed with the project to make freely available 3D geometries of the lower limbs of the Visible Human Female and Visible Human Male. The FEMORS code was used to remove all overclosures between adjacent geometries. The VH 3D geometries are available at: https://simtk.org/projects/3d-vh-geometry
The code was implemented in MATLAB utilizing the Machine Learning Toolbox and is available free and open-source, but we ask that you cite the following two works:
Andreassen, T. E., Hume, D. R., Hamilton, L. D., Higinbotham, S. E. & Shelburne, K. B. "An Automated Process for 2D and 3D Finite Element Overclosure and Gap Adjustment using Radial Basis Function Networks". 1–13 (2022) https://doi.org/10.48550/arXiv.2209.06948
TE Andreassen, DR Hume, LD Hamilton, K Walker, SE Higinbotham, KB Shelburne, "Three-dimensional lower extremity musculoskeletal geometry of the Visible Human Female and Male,” Sci Data 10, 34 (2023). https://doi.org/10.1038/s41597-022-01905-2.
Adding changes to the code is encouraged and can be added to the repository by contacting the author. The author will check new or revised content for accuracy and completeness and add it to the repository.
Future/ongoing work aims to recreate the code using code that does not need the Machine Learning Toolbox, as well as implementing the code into a Python Toolbox for widespread use. | |
|
Registered: 2023-03-27 19:58 |
The Musculoskeletal Atlas Project
- We have built an open-source software framework called the Musculoskeletal Atlas Project (MAP) for creating musculoskeletal models. The software is built with a Python plug-in architecture, to enable quick and easy development from the community. The client-side application (MAP Client) facilitates dicom and motion capture integration, registration tools, and meshing capabilities. The MAP database stores meshes from a larger population of medical imaging data, known as the Melbourne Femur Collection, which consists of 320 full body CT scans. The MAP Client uses statistical shape modelling to provide a best-match to your mocap and medical imaging data and generate surface geometry to generate an OpenSim model.
MAPClient Homepage :
http://map-client.readthedocs.io/en/latest/
MAPClient - FAI Workshop :
http://map-client-fai-workshop.readthedocs.io/en/stable/
Git Hub Repo Generic MAP plugins :
https://github.com/mapclient-plugins
| |
|
Activity Percentile: 0.00 Registered: 2012-08-15 00:07 |
Connectivity Tracking (ConTrack)
- The Connectivity Tracking (ConTrack) technique contains three stages 1) pathway candidate generation, 2) candidate scoring, and 3) inference of the pathways representing the connection.
The ConTrack algorithms use knowledge of DTI scanning physics and apriori information about tissue architecture to identify the location of connections between two regions within the DTI data. Assuming a course of connection or pathway between these two regions is known to exist within the measured tissue, ConTrack can be used to estimate properties of these connections in-vivo. | |
|
Activity Percentile: 0.00 Registered: 2008-06-10 23:26 |
The Osteoporotic Virtual Physiological Human
- Nearly four million osteoporotic bone fractures cost the European health system more than 30 billion Euro per year. This figure could double by 2050. After the first fracture, the chances of having another one increase by 86%. We need to prevent osteoporotic fractures. The first step is an accurate prediction of the patient-specific risk of fracture that considers not only the
skeletal determinants but also the neuromuscular condition. The aim of VPHOP is to develop a multiscale modelling technology based on conventional diagnostic imaging methods that makes it possible, in a clinical setting, to predict for each patient the strength of his/her bones, how this strength is likely to change over time, and the probability that the he/she will overload his/her bones during daily life. With these three predictions, the evaluation of the
absolute risk of bone fracture will be much more accurate than any prediction based on
external and indirect determinants, as it is current clinical practice. These predictions will be used to: i) improve the diagnostic accuracy of the current clinical standards; ii) to provide the basis for an evidence-based prognosis with respect to the natural evolution of the disease, to pharmacological treatments, and/or to preventive interventional treatments aimed to selectively strengthen particularly weak regions of the skeleton. For patients at high risk of fracture, and for which the pharmacological treatment appears insufficient, the VPHOP system will also assist the interventional radiologist in planning the augmentation procedure.
The various modelling technologies developed during the project will be validated not only in vitro, on animal models, or against retrospective clinical outcomes, but will also be assessed in term of clinical impact and safety on small cohorts of patients enrolled at four different clinical institutions, providing the factual basis for effective clinical and industrial exploitations. | |
|
Registered: 2010-03-08 08:57 |