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Author Sergio Vera; Debora Gil; Agnes Borras; Marius George Linguraru; Miguel Angel Gonzalez Ballester edit   pdf
url  doi
openurl 
  Title Geometric Steerable Medial Maps Type Journal Article
  Year 2013 Publication Machine Vision and Applications Abbreviated Journal MVA  
  Volume 24 Issue 6 Pages 1255-1266  
  Keywords Medial Representations ,Medial Manifolds Comparation , Surface , Reconstruction  
  Abstract In order to provide more intuitive and easily interpretable representations of complex shapes/organs, medial manifolds should reach a compromise between simplicity in geometry and capability for restoring the anatomy/shape of the organ/volume. Existing morphological methods show excellent results when applied to 2D objects, but their quality drops across dimensions.
This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial manifolds that avoids degenerated medial axis segments. Second, we introduce a continuous operator for accurate and efficient computation of medial structures of arbitrary dimension. We evaluate quantitatively the performance of our method with respect to existing approaches, by applying them to syn- thetic shapes of known medial geometry. We also show its higher performance for medical imaging applications in terms of simplicity of medial structures and capability for reconstructing the anatomical volume.
 
  Address  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor Mubarak Shah  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0932-8092 ISBN Medium  
  Area Expedition Conference  
  Notes IAM; 605.203; 600.060; 600.044 Approved no  
  Call Number IAM @ iam @ VGB2013 Serial 2192  
Permanent link to this record
 

 
Author Francesc Carreras; Jaume Garcia; Debora Gil; Sandra Pujadas; Chi ho Lion; R.Suarez-Arias; R.Leta; Xavier Alomar; Manuel Ballester; Guillem Pons-Llados edit  url
doi  openurl
  Title Left ventricular torsion and longitudinal shortening: two fundamental components of myocardial mechanics assessed by tagged cine-MRI in normal subjects Type Journal Article
  Year 2012 Publication International Journal of Cardiovascular Imaging Abbreviated Journal IJCI  
  Volume 28 Issue 2 Pages 273-284  
  Keywords Magnetic resonance imaging (MRI); Tagging MRI; Cardiac mechanics; Ventricular torsion  
  Abstract Cardiac magnetic resonance imaging (Cardiac MRI) has become a gold standard diagnostic technique for the assessment of cardiac mechanics, allowing the non-invasive calculation of left ventric- ular long axis longitudinal shortening (LVLS) and absolute myocardial torsion (AMT) between basal and apical left ventricular slices, a movement directly related to the helicoidal anatomic disposition of the myocardial fibers. The aim of this study is to determine AMT and LVLS behaviour and normal values from a group of healthy subjects. A group of 21 healthy volunteers (15 males) (age: 23–55 y.o., mean:30.7 ± 7.5) were prospectively included in an obser- vational study by Cardiac MRI. Left ventricular rotation (degrees) was calculated by custom-made software (Harmonic Phase Flow) in consecutive LV short axis planes tagged cine-MRI sequences. AMT was determined from the difference between basal and apical planes LV rotations. LVLS (%) was determined from the LV longitudinal and horizontal axis cine-MRI images. All the 21 cases studied were interpretable, although in three cases the value of the LV apical rotation could not be determined. The mean rotation of the basal and apical planes at end-systole were -3.71° ± 0.84° and 6.73° ± 1.69° (n:18) respectively, resulting in a LV mean AMT of 10.48° ± 1.63° (n:18). End-systolic mean LVLS was 19.07 ± 2.71%. Cardiac MRI allows for the calculation of AMT and LVLS, fundamental functional components of the ventricular twist mechanics conditioned, in turn, by the anatomical helical layout of the myocardial fibers. These values provide complementary information about systolic ventricular function in relation to the traditional parameters used in daily practice.  
  Address  
  Corporate Author Thesis  
  Publisher Springer Netherlands Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1569-5794 ISBN Medium  
  Area Expedition Conference  
  Notes IAM; Approved no  
  Call Number IAM @ iam @ CGG2012 Serial 1496  
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Author Gloria Fernandez Esparrach; Jorge Bernal; Cristina Rodriguez de Miguel; Debora Gil; Fernando Vilariño; Henry Cordova; Cristina Sanchez Montes; I.Araujo ; Maria Lopez Ceron; J.Llach; F. Javier Sanchez edit   pdf
url  openurl
  Title Colonic polyps are correctly identified by a computer vision method using wm-dova energy maps Type Conference Article
  Year 2015 Publication Proceedings of 23 United European- UEG Week 2015 Abbreviated Journal  
  Volume Issue Pages  
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  ISSN ISBN Medium  
  Area Expedition Conference UEG  
  Notes MV; IAM; 600.075;SIAI Approved no  
  Call Number Admin @ si @ FBR2015 Serial 2732  
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Author Oriol Ramos Terrades; Albert Berenguel; Debora Gil edit   pdf
url  openurl
  Title A flexible outlier detector based on a topology given by graph communities Type Miscellaneous
  Year 2020 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Outlier, or anomaly, detection is essential for optimal performance of machine learning methods and statistical predictive models. It is not just a technical step in a data cleaning process but a key topic in many fields such as fraudulent document detection, in medical applications and assisted diagnosis systems or detecting security threats. In contrast to population-based methods, neighborhood based local approaches are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. However, a main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters. This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world data sets show that our approach overall outperforms, both, local and global strategies in multi and single view settings.  
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  Notes IAM; DAG; 600.139; 600.145; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ RBG2020 Serial 3475  
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Author Debora Gil; Katerine Diaz; Carles Sanchez; Aura Hernandez-Sabate edit   pdf
url  openurl
  Title Early Screening of SARS-CoV-2 by Intelligent Analysis of X-Ray Images Type Miscellaneous
  Year 2020 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Future SARS-CoV-2 virus outbreak COVID-XX might possibly occur during the next years. However the pathology in humans is so recent that many clinical aspects, like early detection of complications, side effects after recovery or early screening, are currently unknown. In spite of the number of cases of COVID-19, its rapid spread putting many sanitary systems in the edge of collapse has hindered proper collection and analysis of the data related to COVID-19 clinical aspects. We describe an interdisciplinary initiative that integrates clinical research, with image diagnostics and the use of new technologies such as artificial intelligence and radiomics with the aim of clarifying some of SARS-CoV-2 open questions. The whole initiative addresses 3 main points: 1) collection of standardize data including images, clinical data and analytics; 2) COVID-19 screening for its early diagnosis at primary care centers; 3) define radiomic signatures of COVID-19 evolution and associated pathologies for the early treatment of complications. In particular, in this paper we present a general overview of the project, the experimental design and first results of X-ray COVID-19 detection using a classic approach based on HoG and feature selection. Our experiments include a comparison to some recent methods for COVID-19 screening in X-Ray and an exploratory analysis of the feasibility of X-Ray COVID-19 screening. Results show that classic approaches can outperform deep-learning methods in this experimental setting, indicate the feasibility of early COVID-19 screening and that non-COVID infiltration is the group of patients most similar to COVID-19 in terms of radiological description of X-ray. Therefore, an efficient COVID-19 screening should be complemented with other clinical data to better discriminate these cases.  
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  Notes IAM; 600.139; 600.145; 601.337 Approved no  
  Call Number Admin @ si @ GDS2020 Serial 3474  
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Author Pau Cano; Alvaro Caravaca; Debora Gil; Eva Musulen edit   pdf
url  openurl
  Title Diagnosis of Helicobacter pylori using AutoEncoders for the Detection of Anomalous Staining Patterns in Immunohistochemistry Images Type Miscellaneous
  Year 2023 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages 107241  
  Keywords  
  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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  Notes IAM Approved no  
  Call Number Admin @ si @ CCG2023 Serial 3855  
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Author Debora Gil; Antonio Esteban Lansaque; Sebastian Stefaniga; Mihail Gaianu; Carles Sanchez edit   pdf
url  openurl
  Title Data Augmentation from Sketch Type Conference Article
  Year 2019 Publication International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging Abbreviated Journal  
  Volume 11840 Issue Pages 155-162  
  Keywords Data augmentation; cycleGANs; Multi-objective optimization  
  Abstract State of the art machine learning methods need huge amounts of data with unambiguous annotations for their training. In the context of medical imaging this is, in general, a very difficult task due to limited access to clinical data, the time required for manual annotations and variability across experts. Simulated data could serve for data augmentation provided that its appearance was comparable to the actual appearance of intra-operative acquisitions. Generative Adversarial Networks (GANs) are a powerful tool for artistic style transfer, but lack a criteria for selecting epochs ensuring also preservation of intra-operative content.

We propose a multi-objective optimization strategy for a selection of cycleGAN epochs ensuring a mapping between virtual images and the intra-operative domain preserving anatomical content. Our approach has been applied to simulate intra-operative bronchoscopic videos and chest CT scans from virtual sketches generated using simple graphical primitives.
 
  Address Shenzhen; China; October 2019  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference CLIP  
  Notes IAM; 600.145; 601.337; 600.139; 600.145 Approved no  
  Call Number Admin @ si @ GES2019 Serial 3359  
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Author Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso; Vanesa Vicens; Cubero Noelia; Rosa Lopez Lisbona; Carles Sanchez; Agnes Borras; Antoni Rosell edit  url
openurl 
  Title Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation Type Journal Article
  Year 2016 Publication Chest Journal Abbreviated Journal CHEST  
  Volume 150 Issue 4 Pages 1003A  
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  Notes IAM; 600.096; 600.075 Approved no  
  Call Number Admin @ si @ DGC2016 Serial 3099  
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Author Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso edit  url
openurl 
  Title Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation Type Journal Article
  Year 2017 Publication Journal of Thoracic Oncology Abbreviated Journal JTO  
  Volume 12 Issue 1S Pages S596-S597  
  Keywords Thorax CT; diagnosis; Peripheral Pulmonary Nodule  
  Abstract A main weakness of virtual bronchoscopic navigation (VBN) is unsuccessful segmentation of distal branches approaching peripheral pulmonary nodules (PPN). CT scan acquisition protocol is pivotal for segmentation covering the utmost periphery. We hypothesize that application of continuous positive airway pressure (CPAP) during CT acquisition could improve visualization and segmentation of peripheral bronchi. The purpose of the present pilot study is to compare quality of segmentations under 4 CT acquisition modes: inspiration (INSP), expiration (EXP) and both with CPAP (INSP-CPAP and EXP-CPAP).  
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  Notes IAM; 600.096; 600.075; 600.145 Approved no  
  Call Number Admin @ si @ DGC2017a Serial 2883  
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Author H.Martin Kjer; Jens Fagertuna; Sergio Vera; Debora Gil; Miguel Angel Gonzalez Ballester; Rasmus R. Paulsena edit   pdf
url  openurl
  Title Free-form image registration of human cochlear uCT data using skeleton similarity as anatomical prior Type Journal Article
  Year 2016 Publication Patter Recognition Letters Abbreviated Journal PRL  
  Volume 76 Issue 1 Pages 76-82  
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  Area Expedition Conference  
  Notes IAM; 600.060 Approved no  
  Call Number Admin @ si @ MFV2017b Serial 2941  
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