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Author Debora Gil; Antonio Esteban Lansaque; Agnes Borras; Carles Sanchez edit  url
openurl 
  Title Enhancing virtual bronchoscopy with intra-operative data using a multi-objective GAN Type Journal Article
  Year 2019 Publication International Journal of Computer Assisted Radiology and Surgery Abbreviated Journal IJCAR  
  Volume 7 Issue 1 Pages  
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  Abstract This manuscript has been withdrawn by bioRxiv due to upload of an incorrect version of the manuscript by the authors. Therefore, this manuscript should not be cited as reference for this project.  
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  Notes IAM; 600.139; 600.145 Approved no  
  Call Number Admin @ si @ GEB2019 Serial 3307  
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Author Debora Gil; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell edit  url
doi  openurl
  Title Segmentation of Distal Airways using Structural Analysis Type Journal Article
  Year 2019 Publication PloS one Abbreviated Journal Plos  
  Volume 14 Issue 12 Pages  
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  Abstract Segmentation of airways in Computed Tomography (CT) scans is a must for accurate support of diagnosis and intervention of many pulmonary disorders. In particular, lung cancer diagnosis would benefit from segmentations reaching most distal airways. We present a method that combines descriptors of bronchi local appearance and graph global structural analysis to fine-tune thresholds on the descriptors adapted for each bronchial level. We have compared our method to the top performers of the EXACT09 challenge and to a commercial software for biopsy planning evaluated in an own-collected data-base of high resolution CT scans acquired under different breathing conditions. Results on EXACT09 data show that our method provides a high leakage reduction with minimum loss in airway detection. Results on our data-base show the reliability across varying breathing conditions and a competitive performance for biopsy planning compared to a commercial solution.  
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  Notes IAM; 600.139; 600.145 Approved no  
  Call Number Admin @ si @ GSB2019 Serial 3357  
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Author Marta Ligero; Guillermo Torres; Carles Sanchez; Katerine Diaz; Raquel Perez; Debora Gil edit  url
doi  openurl
  Title Selection of Radiomics Features based on their Reproducibility Type Conference Article
  Year 2019 Publication 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society Abbreviated Journal  
  Volume Issue Pages 403-408  
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  Abstract Dimensionality reduction is key to alleviate machine learning artifacts in clinical applications with Small Sample Size (SSS) unbalanced datasets. Existing methods rely on either the probabilistic distribution of training data or the discriminant power of the reduced space, disregarding the impact of repeatability and uncertainty in features.In the present study is proposed the use of reproducibility of radiomics features to select features with high inter-class correlation coefficient (ICC). The reproducibility includes the variability introduced in the image acquisition, like medical scans acquisition parameters and convolution kernels, that affects intensity-based features and tumor annotations made by physicians, that influences morphological descriptors of the lesion.For the reproducibility of radiomics features three studies were conducted on cases collected at Vall Hebron Oncology Institute (VHIO) on responders to oncology treatment. The studies focused on the variability due to the convolution kernel, image acquisition parameters, and the inter-observer lesion identification. The features selected were those features with a ICC higher than 0.7 in the three studies.The selected features based on reproducibility were evaluated for lesion malignancy classification using a different database. Results show better performance compared to several state-of-the-art methods including Principal Component Analysis (PCA), Kernel Discriminant Analysis via QR decomposition (KDAQR), LASSO, and an own built Convolutional Neural Network.  
  Address Berlin; Alemanya; July 2019  
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  Area Expedition Conference EMBC  
  Notes IAM; 600.139; 600.145 Approved no  
  Call Number Admin @ si @ LTS2019 Serial 3358  
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Author Debora Gil; Antonio Esteban Lansaque; Sebastian Stefaniga; Mihail Gaianu; Carles Sanchez edit  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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  Series Editor Series Title Abbreviated Series Title LNCS  
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  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 Carles Sanchez; Miguel Viñas; Coen Antens; Agnes Borras; Debora Gil edit  url
doi  openurl
  Title Back to Front Architecture for Diagnosis as a Service Type Conference Article
  Year 2018 Publication 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Abbreviated Journal  
  Volume Issue Pages 343-346  
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  Abstract Software as a Service (SaaS) is a cloud computing model in which a provider hosts applications in a server that customers use via internet. Since SaaS does not require to install applications on customers' own computers, it allows the use by multiple users of highly specialized software without extra expenses for hardware acquisition or licensing. A SaaS tailored for clinical needs not only would alleviate licensing costs, but also would facilitate easy access to new methods for diagnosis assistance. This paper presents a SaaS client-server architecture for Diagnosis as a Service (DaaS). The server is based on docker technology in order to allow execution of softwares implemented in different languages with the highest portability and scalability. The client is a content management system allowing the design of websites with multimedia content and interactive visualization of results allowing user editing. We explain a usage case that uses our DaaS as crowdsourcing platform in a multicentric pilot study carried out to evaluate the clinical benefits of a software for assessment of central airway obstruction.  
  Address Timisoara; Rumania; September 2018  
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  Area Expedition Conference SYNASC  
  Notes IAM; no menciona Approved no  
  Call Number Admin @ si @ SVA2018 Serial 3360  
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Author Debora Gil; Antoni Rosell edit  openurl
  Title Advances in Artificial Intelligence – How Lung Cancer CT Screening Will Progress? Type Abstract
  Year 2019 Publication World Lung Cancer Conference Abbreviated Journal  
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  Address Barcelona; September 2019  
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  Area Expedition Conference IASLC WCLC  
  Notes IAM; Approved no  
  Call Number Admin @ si @ GiR2019 Serial 3361  
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Author Debora Gil; Antonio Esteban Lansaque; Agnes Borras; Esmitt Ramirez; Carles Sanchez edit  url
doi  openurl
  Title Intraoperative Extraction of Airways Anatomy in VideoBronchoscopy Type Journal Article
  Year 2020 Publication IEEE ACCESS Abbreviated Journal ACCESS  
  Volume 8 Issue Pages 159696 - 159704  
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  Abstract A main bottleneck in bronchoscopic biopsy sampling is to efficiently reach the lesion navigating across bronchial levels. Any guidance system should be able to localize the scope position during the intervention with minimal costs and alteration of clinical protocols. With the final goal of an affordable image-based guidance, this work presents a novel strategy to extract and codify the anatomical structure of bronchi, as well as, the scope navigation path from videobronchoscopy. Experiments using interventional data show that our method accurately identifies the bronchial structure. Meanwhile, experiments using simulated data verify that the extracted navigation path matches the 3D route.  
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  Notes IAM; 600.139; 600.145 Approved no  
  Call Number Admin @ si @ GEB2020 Serial 3467  
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Author Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Klaus McDonald Maier edit  doi
openurl 
  Title Effects of Non-Driving Related Tasks during Self-Driving mode Type Journal Article
  Year 2020 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
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  Abstract Perception reaction time and mental workload have proven to be crucial in manual driving. Moreover, in highly automated cars, where most of the research is focusing on Level 4 Autonomous driving, take-over performance is also a key factor when taking road safety into account. This study aims to investigate how the immersion in non-driving related tasks affects the take-over performance of drivers in given scenarios. The paper also highlights the use of virtual simulators to gather efficient data that can be crucial in easing the transition between manual and autonomous driving scenarios. The use of Computer Aided Simulations is of absolute importance in this day and age since the automotive industry is rapidly moving towards Autonomous technology. An experiment comprising of 40 subjects was performed to examine the reaction times of driver and the influence of other variables in the success of take-over performance in highly automated driving under different circumstances within a highway virtual environment. The results reflect the relationship between reaction times under different scenarios that the drivers might face under the circumstances stated above as well as the importance of variables such as velocity in the success on regaining car control after automated driving. The implications of the results acquired are important for understanding the criteria needed for designing Human Machine Interfaces specifically aimed towards automated driving conditions. Understanding the need to keep drivers in the loop during automation, whilst allowing drivers to safely engage in other non-driving related tasks is an important research area which can be aided by the proposed study.  
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  Notes IAM; 600.139; 600.145 Approved no  
  Call Number Admin @ si @ MHE2020 Serial 3468  
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Author Pere Serra; Carmen Centeno; Jose Sanz-Santos; Mohamed Torky; Sonia Baeza; Leire Mendiluce; Carlos Martinez-Barenys; Pedro de Castro; Jorge Abad; Antoni Rosell; Felipe Andreo edit  url
openurl 
  Title Is it necessary to sample the contralateral nodal stations by EBUS-TBNA in patients with lung cancer and clinical N0 / N1 on PET-CT? Type Journal Article
  Year 2020 Publication Lung Cancer Abbreviated Journal  
  Volume 142 Issue Pages 9-12  
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  Abstract Systematic mediastinal staging (sampling all visible nodes measuring ≥ 5 mm from N3 station to N1, regardless of PET/CT (positron emission tomography/computed tomography) by endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) is a decisive step in patients with non-small cell lung cancer (NSCLC). We analyzed the prevalence of N3 disease and the utility of systematic staging in the subgroup of patients who underwent EBUS-TBNA staging without showing mediastinal lesions on the PET/CT (N0/N1).  
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  Notes IAM; 600.139; 600.145 Approved no  
  Call Number Admin @ si @ SCS2020 Serial 3469  
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Author Angels Barbera; Ruth Marginet Flinch; Montserrat Martin; Jose L Mate; Albert Oriol; Fina Martinez-Soler; Tomas Santalucia; Pedro L. Fernandez edit  url
doi  openurl
  Title The Immunohistochemical Expression of Programmed Death Ligand 1 Type Journal Article
  Year 2020 Publication Applied Immunohistochemistry & Molecular Morphology Abbreviated Journal AIMM  
  Volume Issue Pages  
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  Abstract Humanized antibodies targeting programmed death receptor 1 (PD-1) or its ligand (PD-L1) have been approved for the treatment of different cancers. Some of these antibodies show a correlation between the tissue expression of PD-L1 and response. Evaluation of PD-L1 expression presents multiple challenges, but some preanalytical issues such as tissue fixation have been scarcely evaluated. With the hypothesis that immunohistochemical staining of PD-L1 may be impacted by the time of specimen fixation, we evaluated differences in its expression in tonsil samples exposed to predefined fixation times. Random nontumoral tonsillectomy specimens were blindly evaluated in tissue microarray slides after staining with SP142 and SP263 antibodies. With fixation times ranging from 12 to 72 hours, between 2.8% and 6.1% of the samples were considered to be suboptimally stained, with no differences between the 2 antibodies within these fixation times. A significantly higher proportion of samples exposed to a fixation time of 96 hours presented suboptimal immunostaining (15.6%, P<0.0001). In addition, suboptimally stained spots were 20.8% using SP142 and 10.4% using SP263 after 96 hours of fixation (P=0.046). In conclusion, the quality of staining for PD-L1 in tonsil samples decreased with overfixation of the specimen at times >72 hours. Samples exposed to formaldehyde for longer periods presented suboptimal results for both clones, but the SP142 antibody presented a significantly lower tolerance to formalin overexposure than SP263. These results indicate the relevance of a controlled preanalytical processing of samples and particularly the length of fixation of tumor specimens.  
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  Notes IAM; 600.139; 600.145 Approved no  
  Call Number Admin @ si @ BMM2020 Serial 3470  
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