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Author |
Debora Gil;Agnes Borras;Ruth Aris;Mariano Vazquez;Pierre Lafortune; Guillame Houzeaux |
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Title |
What a difference in biomechanics cardiac fiber makes |
Type |
Conference Article |
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Year |
2012 |
Publication |
Statistical Atlases And Computational Models Of The Heart: Imaging and Modelling Challenges |
Abbreviated Journal |
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Volume |
7746 |
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Pages |
253-260 |
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Abstract |
Computational simulations of the heart are a powerful tool for a comprehensive understanding of cardiac function and its intrinsic relationship with its muscular architecture. Cardiac biomechanical models require a vector field representing the orientation of cardiac fibers. A wrong orientation of the fibers can lead to a
non-realistic simulation of the heart functionality. In this paper we explore the impact of the fiber information on the simulated biomechanics of cardiac muscular anatomy. We have used the John Hopkins database to perform a biomechanical simulation using both a synthetic benchmark fiber distribution and the data obtained experimentally from DTI. Results illustrate how differences in fiber orientation affect heart deformation along cardiac cycle. |
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Nice, France |
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Springer Berlin Heidelberg |
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ISSN |
0302-9743 |
ISBN |
978-3-642-36960-5 |
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STACOM |
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IAM @ iam @ GBA2012 |
Serial |
1987 |
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Author |
Sergio Vera; Miguel Angel Gonzalez Ballester; Debora Gil |
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Title |
Optimal Medial Surface Generation for Anatomical Volume Representations |
Type |
Book Chapter |
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Year |
2012 |
Publication |
Abdominal Imaging. Computational and Clinical Applications |
Abbreviated Journal |
LNCS |
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Volume |
7601 |
Issue |
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Pages |
265-273 |
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Keywords |
Medial surface representation; volume reconstruction |
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Abstract |
Medial representations are a widely used technique in abdominal organ shape representation and parametrization. Those methods require good medial manifolds as a starting point. Any medial
surface used to parametrize a volume should be simple enough to allow an easy manipulation and complete enough to allow an accurate reconstruction of the volume. Obtaining good quality medial
surfaces is still a problem with current iterative thinning methods. This forces the usage of generic, pre-calculated medial templates that are adapted to the final shape at the cost of a drop in volume reconstruction.
This paper describes an operator for generation of medial structures that generates clean and complete manifolds well suited for their further use in medial representations of abdominal organ volumes. While being simpler than thinning surfaces, experiments show its high performance in volume reconstruction and preservation of medial surface main branching topology. |
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Nice, France |
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Springer Berlin Heidelberg |
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Yoshida, Hiroyuki and Hawkes, David and Vannier, MichaelW. |
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Lecture Notes in Computer Science |
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ISSN |
0302-9743 |
ISBN |
978-3-642-33611-9 |
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STACOM |
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Call Number |
IAM @ iam @ VGG2012b |
Serial |
1988 |
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Author |
Sergio Vera; Miguel Angel Gonzalez Ballester; Debora Gil |
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Title |
A medial map capturing the essential geometry of organs |
Type |
Conference Article |
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Year |
2012 |
Publication |
ISBI Workshop on Open Source Medical Image Analysis software |
Abbreviated Journal |
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Pages |
1691 - 1694 |
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Keywords |
Medial Surface Representation, Volume Reconstruction,Geometry , Image reconstruction , Liver , Manifolds , Shape , Surface morphology , Surface reconstruction |
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Abstract |
Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Accurate computation of one pixel wide medial surfaces is mandatory. Those surfaces must represent faithfully the geometry of the volume. Although morphological methods produce excellent results in 2D, their complexity and quality drops across dimensions, due to a more complex description of pixel neighborhoods. This paper introduces a continuous operator for accurate and efficient computation of medial structures of arbitrary dimension. Our experiments show its higher performance for medical imaging applications in terms of simplicity of medial structures and capability for reconstructing the anatomical volume |
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Barcelona,Spain |
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IEEE |
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ISSN |
1945-7928 |
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978-1-4577-1857-1 |
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ISBI |
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IAM @ iam @ VGG2012a |
Serial |
1989 |
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Author |
Francesco Brughi |
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Title |
Artistic Heritage Motive Retrieval: an Explorative Study |
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Report |
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Year |
2013 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
176 |
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Master's thesis |
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Call Number |
Admin @ si @ Bru2013 |
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2410 |
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Author |
Ferran Poveda |
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Title |
Computer Graphics and Vision Techniques for the Study of the Muscular Fiber Architecture of the Myocardium |
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Book Whole |
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Year |
2013 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Ph.D. thesis |
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Editor |
Debora Gil;Enric Marti |
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Call Number |
Admin @ si @ Pov2013 |
Serial |
2417 |
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Author |
Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso; Vanesa Vicens; Noelia Cubero de Frutos; Rosa Lopez Lisbona; Carles Sanchez; Agnes Borras; Antoni Rosell |
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Title |
Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation |
Type |
Journal Article |
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Year |
2017 |
Publication |
European Respiratory Journal |
Abbreviated Journal |
ERJ |
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Call Number |
Admin @ si @ DGC2017b |
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3632 |
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Author |
Pau Cano; Alvaro Caravaca; Debora Gil; Eva Musulen |
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Title |
Diagnosis of Helicobacter pylori using AutoEncoders for the Detection of Anomalous Staining Patterns in Immunohistochemistry Images |
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Miscellaneous |
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Year |
2023 |
Publication |
Arxiv |
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Pages |
107241 |
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Abstract |
This work addresses the detection of Helicobacter pylori a bacterium classified since 1994 as class 1 carcinogen to humans. By its highest specificity and sensitivity, the preferred diagnosis technique is the analysis of histological images with immunohistochemical staining, a process in which certain stained antibodies bind to antigens of the biological element of interest. This analysis is a time demanding task, which is currently done by an expert pathologist that visually inspects the digitized samples.
We propose to use autoencoders to learn latent patterns of healthy tissue and detect H. pylori as an anomaly in image staining. Unlike existing classification approaches, an autoencoder is able to learn patterns in an unsupervised manner (without the need of image annotations) with high performance. In particular, our model has an overall 91% of accuracy with 86\% sensitivity, 96% specificity and 0.97 AUC in the detection of H. pylori. |
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Admin @ si @ CCG2023 |
Serial |
3855 |
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Author |
Guillermo Torres; Debora Gil |
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Title |
A multi-shape loss function with adaptive class balancing for the segmentation of lung structures |
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Journal Article |
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Year |
2020 |
Publication |
International Journal of Computer Assisted Radiology and Surgery |
Abbreviated Journal |
IJCAR |
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Volume |
15 |
Issue |
1 |
Pages |
S154-55 |
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Call Number |
Admin @ si @ ToG2020 |
Serial |
3590 |
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Author |
Sonia Baeza; Debora Gil; I.Garcia Olive; M.Salcedo; J.Deportos; Carles Sanchez; Guillermo Torres; G.Moragas; Antoni Rosell |
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Title |
A novel intelligent radiomic analysis of perfusion SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients |
Type |
Journal Article |
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Year |
2022 |
Publication |
EJNMMI Physics |
Abbreviated Journal |
EJNMMI-PHYS |
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Volume |
9 |
Issue |
1, Article 84 |
Pages |
1-17 |
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Abstract |
Background: COVID-19 infection, especially in cases with pneumonia, is associated with a high rate of pulmonary embolism (PE). In patients with contraindications for CT pulmonary angiography (CTPA) or non-diagnostic CTPA, perfusion single-photon emission computed tomography/computed tomography (Q-SPECT/CT) is a diagnostic alternative. The goal of this study is to develop a radiomic diagnostic system to detect PE based only on the analysis of Q-SPECT/CT scans.
Methods: This radiomic diagnostic system is based on a local analysis of Q-SPECT/CT volumes that includes both CT and Q-SPECT values for each volume point. We present a combined approach that uses radiomic features extracted from each scan as input into a fully connected classifcation neural network that optimizes a weighted crossentropy loss trained to discriminate between three diferent types of image patterns (pixel sample level): healthy lungs (control group), PE and pneumonia. Four types of models using diferent confguration of parameters were tested.
Results: The proposed radiomic diagnostic system was trained on 20 patients (4,927 sets of samples of three types of image patterns) and validated in a group of 39 patients (4,410 sets of samples of three types of image patterns). In the training group, COVID-19 infection corresponded to 45% of the cases and 51.28% in the test group. In the test group, the best model for determining diferent types of image patterns with PE presented a sensitivity, specifcity, positive predictive value and negative predictive value of 75.1%, 98.2%, 88.9% and 95.4%, respectively. The best model for detecting
pneumonia presented a sensitivity, specifcity, positive predictive value and negative predictive value of 94.1%, 93.6%, 85.2% and 97.6%, respectively. The area under the curve (AUC) was 0.92 for PE and 0.91 for pneumonia. When the results obtained at the pixel sample level are aggregated into regions of interest, the sensitivity of the PE increases to 85%, and all metrics improve for pneumonia.
Conclusion: This radiomic diagnostic system was able to identify the diferent lung imaging patterns and is a frst step toward a comprehensive intelligent radiomic system to optimize the diagnosis of PE by Q-SPECT/CT. |
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5 dec 2022 |
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Springer |
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Call Number |
Admin @ si @ BGG2022 |
Serial |
3759 |
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Author |
Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez |
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Title |
Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules – Intermediate Results of the RadioLung Project |
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Journal Article |
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Year |
2023 |
Publication |
International Journal of Computer Assisted Radiology and Surgery |
Abbreviated Journal |
IJCARS |
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Admin @ si @ TGM2023 |
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3830 |
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