Cage Active Contours for image warping and morphing

Cage Active Contours (CACs) have shown to be a framework for segmenting connected objects using a new class of parametric region-based active contours. The CAC approach deforms the contour locally by moving cage's points through affine transformations. The method has shown good performance for image segmentation, but other applications have not been studied. In this paper, we extend the method with new energy functions based on Gaussian mixture models to capture multiple color components per region and extend their applicability to RGB color space. In addition, we provide an extended mathematical formalization of the CAC framework with the purpose of showing its good properties for segmentation, warping, and morphing. Thus, we propose a multiple-step combined method for segmenting images, warping the correspondences of the object cage points, and morphing the objects to create new images. For validation, both quantitative and qualitative tests are used on different datasets. The results show that the new energies produce improvements over the previously developed energies for the CAC. Moreover, we provide examples of the application of the CAC in image segmentation, warping, and morphing supported by our theoretical conclusions

Microscopy Cell Segmentation via Adversarial Neural Networks

We present a novel method for cell segmentation in microscopy images which is inspired by the Generative Adversarial Neural Network (GAN) approach. Our framework is built on a pair of two competitive artificial neural networks, with a unique architecture, termed Rib Cage, which are trained simultaneously and together define a min-max game resulting in an accurate segmentation of a given image. Our approach has two main strengths, similar to the GAN, the method does not require a formulation of a loss function for the optimization process. This allows training on a limited amount of annotated data in a weakly supervised manner. Promising segmentation results on real fluorescent microscopy data are presented. The code is freely available at: https://github.com/arbellea/DeepCellSeg.gitComment: Accepted to IEEE International Symposium on Biomedical Imaging (ISBI) 201

Smart Cage Active Contours and their application to brain image segmentation

In this work we present a new segmentation method named Smart Cage Active Contours (SCAC) that combines a parametrized active contour framework named Cage Active Contours (CAC), based on a ne trans- formations, with Active Shape Models (ASM). Our method e ectively restricts the shapes the evolving contours can take without the need of the training images to be manually landmarked. We apply our method to segment the caudate nuclei subcortical structure of a set of 40 subjects in magnetic resonance brain images, with promising results

3D reconstruction of ribcage geometry from biplanar radiographs using a statistical parametric model approach

Rib cage 3D reconstruction is an important prerequisite for thoracic spine modelling, particularly for studies of the deformed thorax in adolescent idiopathic scoliosis. This study proposes a new method for rib cage 3D reconstruction from biplanar radiographs, using a statistical parametric model approach. Simplified parametric models were defined at the hierarchical levels of rib cage surface, rib midline and rib surface, and applied on a database of 86 trunks. The resulting parameter database served to statistical models learning which were used to quickly provide a first estimate of the reconstruction from identifications on both radiographs. This solution was then refined by manual adjustments in order to improve the matching between model and image. Accuracy was assessed by comparison with 29 rib cages from CT scans in terms of geometrical parameter differences and in terms of line-to-line error distance between the rib midlines. Intra and inter-observer reproducibility were determined regarding 20 scoliotic patients. The first estimate (mean reconstruction time of 2’30) was sufficient to extract the main rib cage global parameters with a 95% confidence interval lower than 7%, 8%, 2% and 4° for rib cage volume, antero-posterior and lateral maximal diameters and maximal rib hump, respectively. The mean error distance was 5.4 mm (max 35mm) down to 3.6 mm (max 24 mm) after the manual adjustment step (+3’30). The proposed method will improve developments of rib cage finite element modeling and evaluation of clinical outcomes.This work was funded by Paris Tech BiomecAM chair on subject specific muscular skeletal modeling, and we express our acknowledgments to the chair founders: Cotrel foundation, Société générale, Protéor Company and COVEA consortium. We extend your acknowledgements to Alina Badina for medical imaging data, Alexandre Journé for his advices, and Thomas Joubert for his technical support

Unsupervised CT lung image segmentation of a mycobacterium tuberculosis infection model

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis that produces pulmonary damage. Radiological imaging is the preferred technique for the assessment of TB longitudinal course. Computer-assisted identification of biomarkers eases the work of the radiologist by providing a quantitative assessment of disease. Lung segmentation is the step before biomarker extraction. In this study, we present an automatic procedure that enables robust segmentation of damaged lungs that have lesions attached to the parenchyma and are affected by respiratory movement artifacts in a Mycobacterium Tuberculosis infection model. Its main steps are the extraction of the healthy lung tissue and the airway tree followed by elimination of the fuzzy boundaries. Its performance was compared with respect to a segmentation obtained using: (1) a semi-automatic tool and (2) an approach based on fuzzy connectedness. A consensus segmentation resulting from the majority voting of three experts' annotations was considered our ground truth. The proposed approach improves the overlap indicators (Dice similarity coefficient, 94% ± 4%) and the surface similarity coefficients (Hausdorff distance, 8.64 mm ± 7.36 mm) in the majority of the most difficult-to-segment slices. Results indicate that the refined lung segmentations generated could facilitate the extraction of meaningful quantitative data on disease burden.The research leading to these results received funding from the Innovative Medicines Initiative (www.imi.europa.eu) Joint Undertaking under grant agreement no. 115337, whose resources comprise funding from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies’ in kind contribution. This work was partially funded by projects TEC2013-48552-C2-1-R, RTC-2015-3772-1, TEC2015-73064-EXP and TEC2016-78052-R from the Spanish Ministerio de Economía, Industria y Competitividad, TOPUS S2013/MIT-3024 project from the regional government of Madrid and by the Department of Health, UK

Unsupervised CT Lung Image Segmentation of a Mycobacterium Tuberculosis Infection Model

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis that produces pulmonary damage. Radiological imaging is the preferred technique for the assessment of TB longitudinal course. Computer-assisted identification of biomarkers eases the work of the radiologist by providing a quantitative assessment of disease. Lung segmentation is the step before biomarker extraction. In this study, we present an automatic procedure that enables robust segmentation of damaged lungs that have lesions attached to the parenchyma and are affected by respiratory movement artifacts in a Mycobacterium Tuberculosis infection model. Its main steps are the extraction of the healthy lung tissue and the airway tree followed by elimination of the fuzzy boundaries. Its performance was compared with respect to a segmentation obtained using: (1) a semi-automatic tool and (2) an approach based on fuzzy connectedness. A consensus segmentation resulting from the majority voting of three experts' annotations was considered our ground truth. The proposed approach improves the overlap indicators (Dice similarity coefficient, 94\% +/- 4\%) and the surface similarity coefficients (Hausdorff distance, 8.64 mm +/- 7.36 mm) in the majority of the most difficult-to-segment slices. Results indicate that the refined lung segmentations generated could facilitate the extraction of meaningful quantitative data on disease burden.We thank Estibaliz Gomez de Mariscal, Paula Martin Gonzalez and Mario Gonzalez Arjona for helping with the manual lung annotation. The research leading to these results received funding from the Innovative Medicines Initiative (www.imi.europa.eu) Joint Undertaking under grant agreement no. 115337, whose resources comprise funding from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies' in kind contribution. This work was partially funded by projects TEC2013-48552-C2-1-R, RTC-2015-3772-1, TEC2015-73064-EXP and TEC2016-78052-R from the Spanish Ministerio de Economia, Industria y Competitividad, TOPUS S2013/MIT-3024 project from the regional government of Madrid and by the Department of Health, UK.S

SEGMENTATION OF SINGLE ORIENTED IMAGES WITH CAGE ACTIVE CONTOURS

Image segmentation primarily based on parametrized active contours. Active contours are of geometric and parametric types. Geometric active contours are used for curve topologies and parametric active contours are used for set of discrete points. Contours are used for identifying the shape of the object. The points which are touching to form the boundary make a set. With help of this level sets the contour is formed. The evolving contour is parametrized in keeping with a reduced set of manipulate points that shape a closed polygon and have a clean visual interpretation. The parametrization, referred to as mean value coordinates, stems from the strategies used in computer pictures to animate digital fashions.The framework allows to effortlessly formulate region based energies to segment an image. The Gaussian model is one of the region based energy term in the segmentation. The behavior of the method is shown on synthetic and real images and examines the overall performance with trendy level set methods

Cage active contours: Extension to color spaces and application to image morphing

The main purpose of this master thesis is to enhance the performance of Cage Active Contours (CAC) in the context of color image object segmentation as well as provide a theoretical framework on which to justify the potential applications of the segmentation produced in particular to image morphing

Evaluation of automated segmentation of hip joint in revision arthroplasty

We present a case of a 72-year-old female patient with a history of degenerative hip joint disease for whom a custom-made prosthesis—an acetabulum cage—was designed. With the growing number of total hip arthroplasty (THA) operations and the rapid development of technology, biology, and tissue bioengineering, there is a market to develop new artificial hip joints. The quality of the custom made prosthesis depends on the quality of segmentation to delineate accurately patient’s anatomy. The error of segmentation may propagate to the overall error of the final prosthesis. We evaluate an in-house segmentation method, that was used in the design of the custom made prosthesis, and a commercial segmentation method, using qualitative and quantitative approaches