Számítógéppel segített tervezés csonttörések műtéti helyreállítására = Computer aided planing for surgical reconstruction of fractures

A vázrendszer sérülései lehetnek igen komplexek és ellátásuk nagy pontosságot igényelhet. Kézenfekvőnek tűnik, hogy a sebészi beavatkozás előtt egy geometriai és mechanikai modellen a különböző fixációs lehetőségek kipróbálásra kerülhessenek. Ennek érdekében egy computerizált rendszert hoztunk létre, melyet MedEdit-nek neveztünk el azért, hogy a sebész műtét előtt megtervezhesse és lemérhesse a tervezett rögzitési lehetőségeket. Végeselemes analízist használva a módosítások összehasonlíthatók. A rendszer bevezetésre került és működik. Bizonyos pontoknál még felhasználói interakcióra van szükség. A geometriai és a mechanikai modell elkészítése kb. 5 percig tart, beleértve az interakciókat is. A végeselemes analízis durván hat percet igényel ( 2 Ghz-es computeren 1,5 GB memóriával). A sterssz analízis korrelál a klinikai elvárással, a kvantitatív kalibrálás elkészült. | Surgeries of the skeletal trauma can be highly complex and require extreme accuracy. That is why it seems to be practical that prior to a surgical intervention a geometric and mechanic model has to be prepared, which can be used to simulate various surgical options. A computerized system, called MedEdit was created, to help the surgeon to plan the intervention. Using of Finite Element Analysis (FEA), the modifications can be measured or compared before the surgery. The system is implemented and works. It is able to perform all tasks, but there are still points where some user interaction is needed. Generally, the geometric and mechanical model can be created s in ca. 5 minutes including the user interactions. The FEA takes roughly 6 minutes for a pelvis 3D volume study (on a 2 GHz computer with 1,5 GB memory). Our stress results seem to match the clinical expectations, quantitative calibration and measurements were done

MedEdit : a computer assisted image processing and navigation system for orthopedic trauma surgery

The surgery of fractured bones is often a very complex problem. That is the reason why it would be beneficial to create a geometric and mechanic model of the bones before surgical intervention. The model geometry is based on the CT images of the patient and the known physical properties of the bone. A computerised system is presented here, called MedEdit, which helps a surgeon plan an operation. The system includes a Finite Element Analysis (FEA) program to measure the stress effects of the possible surgical solutions. Following the simulation and analysis of the behaviour of the modelled bone, surgeons can find the best surgical solution for the patient

Automatic lumen segmentation in IVOCT images using binary morphological reconstruction

Abstract\ud \ud \ud \ud Background\ud Atherosclerosis causes millions of deaths, annually yielding billions in expenses round the world. Intravascular Optical Coherence Tomography (IVOCT) is a medical imaging modality, which displays high resolution images of coronary cross-section. Nonetheless, quantitative information can only be obtained with segmentation; consequently, more adequate diagnostics, therapies and interventions can be provided. Since it is a relatively new modality, many different segmentation methods, available in the literature for other modalities, could be successfully applied to IVOCT images, improving accuracies and uses.\ud \ud \ud \ud Method\ud An automatic lumen segmentation approach, based on Wavelet Transform and Mathematical Morphology, is presented. The methodology is divided into three main parts. First, the preprocessing stage attenuates and enhances undesirable and important information, respectively. Second, in the feature extraction block, wavelet is associated with an adapted version of Otsu threshold; hence, tissue information is discriminated and binarized. Finally, binary morphological reconstruction improves the binary information and constructs the binary lumen object.\ud \ud \ud \ud Results\ud The evaluation was carried out by segmenting 290 challenging images from human and pig coronaries, and rabbit iliac arteries; the outcomes were compared with the gold standards made by experts. The resultant accuracy was obtained: True Positive (%) = 99.29 ± 2.96, False Positive (%) = 3.69 ± 2.88, False Negative (%) = 0.71 ± 2.96, Max False Positive Distance (mm) = 0.1 ± 0.07, Max False Negative Distance (mm) = 0.06 ± 0.1.\ud \ud \ud \ud Conclusions\ud In conclusion, by segmenting a number of IVOCT images with various features, the proposed technique showed to be robust and more accurate than published studies; in addition, the method is completely automatic, providing a new tool for IVOCT segmentation.São Paulo Research Foundation – Brazil ( FAPESP – Process Number: 2012/157212), National Council of Scientific and Technological Development, Brazil (CNPq), Heart Institute of São Paulo, Brazil (InCor), Biomedical Engineering Laboratory of the University of São Paulo, Brazil (LEBUSP). The unknown reviewers, who have made important contributions to this work.São Paulo Research Foundation – Brazil ( FAPESP – Process Number: 2012/15721-2), National Council of Scientific and Technological Development, Brazil (CNPq), Heart Institute of São Paulo, Brazil (InCor), Biomedical Engineering Laboratory of the University of São Paulo, Brazil (LEB-USP). The unknown reviewers, who have made important contributions to this work

Evaluation Methods of Accuracy and Reproducibility for Image Segmentation Algorithms

Segmentation algorithms perform different on differernt datasets. Sometimes we want to learn which segmentation algoirithm is the best for a specific task, therefore we need to rank the performance of segmentation algorithms and determine which one is most suitable to that task. The performance of segmentation algorithms can be characterized from many aspects, such as accuracy and reproducibility. In many situations, the mean of the accuracies of individual segmentations is regarded as the accuracy of the segmentation algorithm which generated these segmentations. Sometimes a new algorithm is proposed and argued to be best based on mean accuracy of segmentations only, but the distribution of accuracies of segmentations generated by the new segmentation algorithm may not be really better than that of other exist segmentation algorithms. There are some cases where two groups of segmentations have the same mean of accuracies but have different distributions. This indicates that even if the mean accuracies of two group of segmentations are the same, the corresponding segmentations may have different accuracy performances. In addition, the reproducibility of segmentation algorithms are measured by many different metrics. But few works compared the properties of reproducibility measures basing on real segmentation data. In this thesis, we illustrate how to evaluate and compare the accuracy performances of segmentation algorithms using a distribution-based method, as well as how to use the proposed extensive method to rank multiple segmentation algorithms according to their accuracy performances. Different from the standard method, our extensive method combines the distribution information with the mean accuracy to evaluate, compare, and rank the accuracy performance of segmentation algorithms, instead of using mean accuracy alone. In addition, we used two sets of real segmentation data to demonstrate that generalized Tanimoto coefficient is a superior reproducibility measure which is insensitive to segmentation group size (number of raters), while other popular measures of reproducibility exhibit sensitivity to group size

A framework for tumor segmentation and interactive immersive visualization of medical image data for surgical planning

This dissertation presents the framework for analyzing and visualizing digital medical images. Two new segmentation methods have been developed: a probability based segmentation algorithm, and a segmentation algorithm that uses a fuzzy rule based system to generate similarity values for segmentation. A visualization software application has also been developed to effectively view and manipulate digital medical images on a desktop computer as well as in an immersive environment.;For the probabilistic segmentation algorithm, image data are first enhanced by manually setting the appropriate window center and width, and if needed a sharpening or noise removal filter is applied. To initialize the segmentation process, a user places a seed point within the object of interest and defines a search region for segmentation. Based on the pixels\u27 spatial and intensity properties, a probabilistic selection criterion is used to extract pixels with a high probability of belonging to the object. To facilitate the segmentation of multiple slices, an automatic seed selection algorithm was developed to keep the seeds in the object as its shape and/or location changes between consecutive slices.;The second segmentation method, a new segmentation method using a fuzzy rule based system to segment tumors in a three-dimensional CT data was also developed. To initialize the segmentation process, the user selects a region of interest (ROI) within the tumor in the first image of the CT study set. Using the ROI\u27s spatial and intensity properties, fuzzy inputs are generated for use in the fuzzy rules inference system. Using a set of predefined fuzzy rules, the system generates a defuzzified output for every pixel in terms of similarity to the object. Pixels with the highest similarity values are selected as tumor. This process is automatically repeated for every subsequent slice in the CT set without further user input, as the segmented region from the previous slice is used as the ROI for the current slice. This creates a propagation of information from the previous slices, used to segment the current slice. The membership functions used during the fuzzification and defuzzification processes are adaptive to the changes in the size and pixel intensities of the current ROI. The proposed method is highly customizable to suit different needs of a user, requiring information from only a single two-dimensional image.;Segmentation results from both algorithms showed success in segmenting the tumor from seven of the ten CT datasets with less than 10% false positive errors and five test cases with less than 10% false negative errors. The consistency of the segmentation results statistics also showed a high repeatability factor, with low values of inter- and intra-user variability for both methods.;The visualization software developed is designed to load and display any DICOM/PACS compatible three-dimensional image data for visualization and interaction in an immersive virtual environment. The software uses the open-source libraries DCMTK: DICOM Toolkit for parsing of digital medical images, Coin3D and SimVoleon for scenegraph management and volume rendering, and VRJuggler for virtual reality display and interaction. A user can apply pseudo-coloring in real time with multiple interactive clipping planes to slice into the volume for an interior view. A windowing feature controls the tissue density ranges to display. A wireless gamepad controller as well as a simple and intuitive menu interface control user interactions. The software is highly scalable as it can be used on a single desktop computer to a cluster of computers for an immersive multi-projection virtual environment. By wearing a pair of stereo goggles, the surgeon is immersed within the model itself, thus providing a sense of realism as if the surgeon is inside the patient.;The tools developed in this framework are designed to improve patient care by fostering the widespread use of advanced visualization and computational intelligence in preoperative planning, surgical training, and diagnostic assistance. Future work includes further improvements to both segmentation methods with plans to incorporate the use of deformable models and level set techniques to include tumor shape features as part of the segmentation criteria. For the surgical planning components, additional controls and interactions with the simulated endoscopic camera and the ability to segment the colon or a selected region of the airway for a fixed-path navigation as a full virtual endoscopy tool will also be implemented. (Abstract shortened by UMI.

Segmentação interativa de volumes baseada em regiões

Orientador: Alexandre Xavier FalcãoDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação CientificaMestrad