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Esmitt Ramirez, Carles Sanchez, & Debora Gil. (2019). Localizing Pulmonary Lesions Using Fuzzy Deep Learning. In 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (pp. 290–294).
Abstract: The usage of medical images is part of the clinical daily in several healthcare centers around the world. Particularly, Computer Tomography (CT) images are an important key in the early detection of suspicious lung lesions. The CT image exploration allows the detection of lung lesions before any invasive procedure (e.g. bronchoscopy, biopsy). The effective localization of lesions is performed using different image processing and computer vision techniques. Lately, the usage of deep learning models into medical imaging from detection to prediction shown that is a powerful tool for Computer-aided software. In this paper, we present an approach to localize pulmonary lung lesion using fuzzy deep learning. Our approach uses a simple convolutional neural network based using the LIDC-IDRI dataset. Each image is divided into patches associated a probability vector (fuzzy) according their belonging to anatomical structures on a CT. We showcase our approach as part of a full CAD system to exploration, planning, guiding and detection of pulmonary lesions.
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Esmitt Ramirez, Carles Sanchez, Agnes Borras, Marta Diez-Ferrer, Antoni Rosell, & Debora Gil. (2018). BronchoX: bronchoscopy exploration software for biopsy intervention planning. HTL - Healthcare Technology Letters, 177–182.
Abstract: Virtual bronchoscopy (VB) is a non-invasive exploration tool for intervention planning and navigation of possible pulmonary lesions (PLs). A VB software involves the location of a PL and the calculation of a route, starting from the trachea, to reach it. The selection of a VB software might be a complex process, and there is no consensus in the community of medical software developers in which is the best-suited system to use or framework to choose. The authors present Bronchoscopy Exploration (BronchoX), a VB software to plan biopsy interventions that generate physician-readable instructions to reach the PLs. The authors’ solution is open source, multiplatform, and extensible for future functionalities, designed by their multidisciplinary research and development group. BronchoX is a compound of different algorithms for segmentation, visualisation, and navigation of the respiratory tract. Performed results are a focus on the test the effectiveness of their proposal as an exploration software, also to measure its accuracy as a guiding system to reach PLs. Then, 40 different virtual planning paths were created to guide physicians until distal bronchioles. These results provide a functional software for BronchoX and demonstrate how following simple instructions is possible to reach distal lesions from the trachea.
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Esmitt Ramirez, Carles Sanchez, Agnes Borras, Marta Diez-Ferrer, Antoni Rosell, & Debora Gil. (2018). Image-Based Bronchial Anatomy Codification for Biopsy Guiding in Video Bronchoscopy. In OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis (Vol. 11041). LNCS.
Abstract: Bronchoscopy examinations allow biopsy of pulmonary nodules with minimum risk for the patient. Even for experienced bronchoscopists, it is difficult to guide the bronchoscope to most distal lesions and obtain an accurate diagnosis. This paper presents an image-based codification of the bronchial anatomy for bronchoscopy biopsy guiding. The 3D anatomy of each patient is codified as a binary tree with nodes representing bronchial levels and edges labeled using their position on images projecting the 3D anatomy from a set of branching points. The paths from the root to leaves provide a codification of navigation routes with spatially consistent labels according to the anatomy observes in video bronchoscopy explorations. We evaluate our labeling approach as a guiding system in terms of the number of bronchial levels correctly codified, also in the number of labels-based instructions correctly supplied, using generalized mixed models and computer-generated data. Results obtained for three independent observers prove the consistency and reproducibility of our guiding system. We trust that our codification based on viewer’s projection might be used as a foundation for the navigation process in Virtual Bronchoscopy systems.
Keywords: Biopsy guiding; Bronchoscopy; Lung biopsy; Intervention guiding; Airway codification
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Ernest Valveny, Salvatore Tabbone, Oriol Ramos Terrades, & Emilie Jean-Marie Odile. (2007). Performance Characterization of Shape Descriptors for Symbol Representation. In Seventh IAPR International Workshop on Graphics Recognition (82–83).
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Ernest Valveny, Salvatore Tabbone, & Oriol Ramos Terrades. (2008). Performance Characterization of Shape Descriptors for Symbol Representation. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities (Vol. 5046, 278–287). LNCS.
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Ernest Valveny, Robert Benavente, Agata Lapedriza, Miquel Ferrer, Jaume Garcia, & Gemma Sanchez. (2012). Adaptation of a computer programming course to the EXHE requirements: evaluation five years later (Vol. 37).
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Ernest Valveny, Philippe Dosch, & Alicia Fornes. (2008). Report on the Third Contest on Symbol Recognition. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities (Vol. 5046, 321–328). LNCS.
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Ernest Valveny, Philippe Dosch, Adam Winstanley, Yu Zhou, Su Yang, Luo Yan, et al. (2006). A general framework for the evaluation of symbol recognition methods. International Journal on Document Analysis and Recognition (IJDAR), 9(1): 59–74.
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Ernest Valveny, & Philippe Dosch. (2004). Symbol Recognition Contest: A Synthesis.
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Ernest Valveny, & Philippe Dosch. (2004). Performance Evaluation of Symbol Recognition. In A. D.(E.) S. Marinai (Ed.), Document Analysis Systems (Vol. 3163, 354–365).
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Ernest Valveny, & Philippe Dosch. (2006). A general framework for the evaluation of symbol recognition methods. International Journal on Document Analysis and Recognition.
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Ernest Valveny, & Philippe Dosch. (2007). A General Framework for the Evaluation of Symbol Recognition Methods. International Journal on Document Analysis and Recognition, vol. 9(1), pp 59–74.
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Ernest Valveny, Oriol Ramos Terrades, Joan Mas, & Marçal Rusiñol. (2013). Interactive Document Retrieval and Classification. In Angel Sappa, & Jordi Vitria (Eds.), Multimodal Interaction in Image and Video Applications (Vol. 48, pp. 17–30). Springer Berlin Heidelberg.
Abstract: In this chapter we describe a system for document retrieval and classification following the interactive-predictive framework. In particular, the system addresses two different scenarios of document analysis: document classification based on visual appearance and logo detection. These two classical problems of document analysis are formulated following the interactive-predictive model, taking the user interaction into account to make easier the process of annotating and labelling the documents. A system implementing this model in a real scenario is presented and analyzed. This system also takes advantage of active learning techniques to speed up the task of labelling the documents.
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Ernest Valveny, & Miquel Ferrer. (2008). Application of Graph Embedding to Solve Graph Matchin Problems. In Colloque International Francophone sur l’Ecrit et le Document (13–18).
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Ernest Valveny, & Enric Marti. (2003). A model for image generation and symbol recognition through the deformation of lineal shapes. PRL - Pattern Recognition Letters, 24(15), 2857–2867.
Abstract: We describe a general framework for the recognition of distorted images of lineal shapes, which relies on three items: a model to represent lineal shapes and their deformations, a model for the generation of distorted binary images and the combination of both models in a common probabilistic framework, where the generation of deformations is related to an internal energy, and the generation of binary images to an external energy. Then, recognition consists in the minimization of a global energy function, performed by using the EM algorithm. This general framework has been applied to the recognition of hand-drawn lineal symbols in graphic documents.
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