Imaging of temporomandibular joint: Approach by direct volume rendering

Materials and Methods: We have studied the temporom-andibular joint anatomy, directly on the living, from 3D images obtained by medical imaging Computed Tomography and Nuclear Magnetic Resonance acquisition, and subsequent re-engineering techniques 3D Surface Rendering and Volume Rendering. Data were analysed with the goal of being able to isolate, identify and distinguish the anatomical structures of the joint, and get the largest possible number of information utilizing software for post-processing work.Results: It was possible to reproduce anatomy of the skeletal structures, as well as through acquisitions of Magnetic Resonance Imaging; it was also possible to visualize the vascular, muscular, ligamentous and tendinous components of the articular complex, and also the capsule and the fibrous cartilaginous disc. We managed the Surface Rendering and Volume Rendering, not only to obtain three-dimensional images for colour and for resolution comparable to the usual anatomical preparations, but also a considerable number of anatomical, minuter details, zooming, rotating and cutting the same images with linking, graduating the colour, transparency and opacity from time to time.Conclusion: These results are encouraging to stimulate further studies in other anatomical districts.Background: The purpose of this study was to conduct a morphological analysis of the temporomandibular joint, a highly specialized synovial joint that permits movement and function of the mandible

Using a Game Engine to Integrate Experimental, Field, and Simulation Data for Science Education: You Are the Scientist!

The purpose of this project is to use a game engine tointegrate geo-referenced research data, whether experimental orsimulated, to present it interactively to the user. Geo-referencedmeans that every image, video, or sound file, every pressuremap, and every simulated temperature chart is attached to aspecific point on a map or body. These data may also be timereferenced,so that different data sets may be available at thesame location for different times of the day or seasons of the year.Target users for the interactive applications are high-school andcollege students who can then conduct their own “experiments”or “explorations” as a way to get exposed to the problems andmethodologies of science and research. We use two examples ofprojects to illustrate the approach

A semantically adaptable integrated visualization and natural exploration of multi-scale biomedical data

The exploration of biomedical data which involves heterogeneous sources coming from different spatial scales and medical domains is a challenging topic in current research. In this work, we combine efforts regarding multi-scale visualization, multimodal interaction and knowledge formalization for the exploration of multi-scale biomedical data. The knowledge formalization stores and organizes the information sources, the integrated visualization captures all relevant information for the domain expertise of the user and the multimodal interaction provides a natural exploration. We present a concrete example of use of the proposed exploratory system designed for a biologist investigating multi-scale pathologies.This work was supported from the EU Marie Curie ITN MultiScaleHuman (FP7-PEOPLE-2011-ITN, Grant agreement no.: 289897). The authors would like to thank all the partners for providing biomedical data sets.info:eu-repo/semantics/publishedVersio

Novel fast semi-automated software to segment cartilage for knee MR acquisitions

AbstractObjectiveValidation of a new fast software technique to segment the cartilage on knee magnetic resonance (MR) acquisitions. Large studies of knee osteoarthritis (OA) will require fast and reproducible methods to quantify cartilage changes for knee MR data. In this report we document and measure the reproducibility and reader time of a software-based technique to quantify the volume and thickness of articular cartilage on knee MR images.MethodsThe software was tested on a set of duplicate sagittal three-dimensional (3D) dual echo steady state (DESS) acquisitions from 15 (8 OA, 7 normal) subjects. The repositioning, inter-reader, and intra-reader reproducibility of the cartilage volume (VC) and thickness (ThC) were measured independently as well as the reader time for each cartilage plate. The root-mean square coefficient of variation (RMSCoV) was used as metric to quantify the reproducibility of VC and mean ThC.ResultsThe repositioning RMSCoV was as follows: VC=2.0% and ThC=1.2% (femur), VC=2.9% and ThC=1.6% (medial tibial plateau), VC=5.5% and ThC=2.4% (lateral tibial plateau), and VC=4.6% and ThC=2.3% (patella). RMSCoV values were higher for the inter-reader reproducibility (VC: 2.5–8.6%) (ThC: 1.9–5.2%) and lower for the intra-reader reproducibility (VC: 1.6–2.5%) (ThC: 1.2–1.9%). The method required an average of 75.4min per knee.ConclusionsWe have documented a fast reproducible semi-automated software method to segment articular cartilage on knee MR acquisitions

K-means Clustering In Knee Cartilage Classification: Data from the OAI

Knee osteoarthritis is a degenerative joint disease which affects people mostly from elderly population. Knee cartilage segmentation is still a driving force in managing early symptoms of knee pain and its consequences of physical disability. However, manual delineation of the tissue of interest by single trained operator is very time consuming. This project utilized a fully-automated segmentation that combined a series of image processing methods to process sagittal knee images. MRI scans undergo Bi-Bezier curve contrast enhancement which increase the distinctiveness of cartilage tissue. Bone-cartilage complex is extracted with dilation of mask resulted from region growing at distal femoral bone. Later, the processed image is clustered with k = 2, into two groups, including coarse cartilage group and background. The thin layer of cartilage is successfully clustered with satisfactory accuracy of 0.987±0.004, sensitivity 0.685±0.065 of and specificity of 0.994±0.004. The results obtained are promising and potentially replace the manual labelling process of training set in convolutional neural network model

Visualization methods for analysis of 3D multi-scale medical data

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Image segmentation methods and edge detection: An application to knee joint articular cartilage edge detection

Image segmentation is the process of partitioning a digital image into multiple segments and regions for further processing. Edge detection methods are widely used in the area of image processing for feature detection and extraction. In this paper we use human’s Knee MRI (Magnetic resonance imaging) images of patients and applied various image segmentation and edge detection methods for knee cartilage visualization. Also this paper focuses on providing an overview of important concepts, methods and algorithms commonly used for image segmentation and edge detection with focus on knee joint articular cartilage image segmentation and visualizatio

Changes in articular cartilage after meniscectomy and meniscus replacement using a biodegradable porous polymer implant

Purpose: To evaluate the long-term effects of implantation of a biodegradable polymer meniscus implant on articular cartilage degeneration and compare this to articular cartilage degeneration after meniscectomy. Methods: Porous polymer polycaprolacton-based polyurethane meniscus implants were implanted for 6 or 24 months in the lateral compartment of Beagle dog knees. Contralateral knees were meniscectomized, or left intact and served as controls. Articular cartilage degeneration was evaluated in detail using India ink staining, routine histology, immunochemistry for denatured (Col2-¾M) and cleaved (Col2-¾Cshort) type II collagen, Mankin’s grading system, and cartilage thickness measurements. Results: Histologically, fibrillation and substantial immunohistochemical staining for both denatured and cleaved type II collagen were found in all three treatment groups. The cartilage of the three groups showed identical degradation patterns. In the 24 months implant group, degradation appeared to be more severe when compared to the 6 months implant group and meniscectomy group. Significantly more cartilage damage (India ink staining, Mankin’s grading system, and cartilage thickness measurements) was found in the 24 months implant group compared to the 6 months implant group and meniscectomy group. Conclusion: Degradation of the cartilage matrix was the result of both mechanical overloading as well as localized cell-mediated degradation. The degeneration patterns were highly variable between animals. Clinical application of a porous polymer implant for total meniscus replacement is not supported by this study.