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Guillermo Torres, Sonia Baeza, Carles Sanchez, Ignasi Guasch, Antoni Rosell, & Debora Gil. (2022). "An Intelligent Radiomic Approach for Lung Cancer Screening " . Applied Sciences, 12(3), 1568.
Abstract: The efficiency of lung cancer screening for reducing mortality is hindered by the high rate of false positives. Artificial intelligence applied to radiomics could help to early discard benign cases from the analysis of CT scans. The available amount of data and the fact that benign cases are a minority, constitutes a main challenge for the successful use of state of the art methods (like deep learning), which can be biased, over-fitted and lack of clinical reproducibility. We present an hybrid approach combining the potential of radiomic features to characterize nodules in CT scans and the generalization of the feed forward networks. In order to obtain maximal reproducibility with minimal training data, we propose an embedding of nodules based on the statistical significance of radiomic features for malignancy detection. This representation space of lesions is the input to a feed
forward network, which architecture and hyperparameters are optimized using own-defined metrics of the diagnostic power of the whole system. Results of the best model on an independent set of patients achieve 100% of sensitivity and 83% of specificity (AUC = 0.94) for malignancy detection.
Keywords: Lung cancer; Early diagnosis; Screening; Neural networks; Image embedding; Architecture optimization
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H.Martin Kjer, Jens Fagertuna, Sergio Vera, Debora Gil, Miguel Angel Gonzalez Ballester, & Rasmus R. Paulsena. (2016). "Free-form image registration of human cochlear uCT data using skeleton similarity as anatomical prior " . Patter Recognition Letters, 76(1), 76–82.
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Jaume Garcia, Debora Gil, Luis Badiella, Aura Hernandez-Sabate, Francesc Carreras, Sandra Pujades, et al. (2010). "A Normalized Framework for the Design of Feature Spaces Assessing the Left Ventricular Function " . IEEE Transactions on Medical Imaging, 29(3), 733–745.
Abstract: A through description of the left ventricle functionality requires combining complementary regional scores. A main limitation is the lack of multiparametric normality models oriented to the assessment of regional wall motion abnormalities (RWMA). This paper covers two main topics involved in RWMA assessment. We propose a general framework allowing the fusion and comparison across subjects of different regional scores. Our framework is used to explore which combination of regional scores (including 2-D motion and strains) is better suited for RWMA detection. Our statistical analysis indicates that for a proper (within interobserver variability) identification of RWMA, models should consider motion and extreme strains.
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Jaume Garcia, Debora Gil, Sandra Pujades, & Francesc Carreras. (2008). "Valoracion de la Funcion del Ventriculo Izquierdo mediante Modelos Regionales Hiperparametricos " . Revista Española de Cardiologia, 61(3), 79.
Abstract: La mayoría de la enfermedades cardiovasculares afectan a las propiedades contráctiles de la banda ventricular helicoidal. Esto se refleja en una variación del comportamiento normal de la función ventricular. Parámetros locales tales como los strains, o la deformación experimentada por el tejido, son indicadores capaces de detectar anomalías funcionales en territorios específicos. A menudo, dichos parámetros son considerados de forma separada. En este trabajo presentamos un marco computacional (el Dominio Paramétrico Normalizado, DPN) que permite integrarlos en hiperparámetros funcionales y estudiar sus rangos de normalidad. Dichos rangos permiten valorar de forma objetiva la función regional de cualquier nuevo paciente. Para ello, consideramos secuencias de resonancia magnética etiquetada a nivel basal, medio y apical. Los hiperparámetros se obtienen a partir del movimiento intramural del VI estimado mediante el método Harmonic Phase Flow. El DPN se define a partir de en una parametrización del Ventrículo Izquierdo (VI) en sus coordenadas radiales y circunferencial basada en criterios anatómicos. El paso de los hiperparámetros al DPN hace posible la comparación entre distintos pacientes. Los rangos de normalidad se definen mediante análisis estadístico de valores de voluntarios sanos en 45 regiones del DPN a lo largo de 9 fases sistólicas. Se ha usado un conjunto de 19 (14 H; E: 30.7±7.5) voluntarios sanos para crear los patrones de normalidad y se han validado usando 2 controles sanos y 3 pacientes afectados de contractilidad global reducida. Para los controles los resultados regionales se han ajustado dentro de la normalidad, mientras que para los pacientes se han obtenido valores anormales en las zonas descritas, localizando y cuantificando así el diagnóstico empírico.
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Jaume Garcia, Debora Gil, Sandra Pujades, & Francesc Carreras. (2008). "A Variational Framework for Assessment of the Left Ventricle Motion " . International Journal Mathematical Modelling of Natural Phenomena, 3(6), 76–100.
Abstract: Impairment of left ventricular contractility due to cardiovascular diseases is reflected in left ventricle (LV) motion patterns. An abnormal change of torsion or long axis shortening LV values can help with the diagnosis and follow-up of LV dysfunction. Tagged Magnetic Resonance (TMR) is a widely spread medical imaging modality that allows estimation of the myocardial tissue local deformation. In this work, we introduce a novel variational framework for extracting the left ventricle dynamics from TMR sequences. A bi-dimensional representation space of TMR images given by Gabor filter banks is defined. Tracking of the phases of the Gabor response is combined using a variational framework which regularizes the deformation field just at areas where the Gabor amplitude drops, while restoring the underlying motion otherwise. The clinical applicability of the proposed method is illustrated by extracting normality models of the ventricular torsion from 19 healthy subjects.
Keywords: Key words: Left Ventricle Dynamics, Ventricular Torsion, Tagged Magnetic Resonance, Motion Tracking, Variational Framework, Gabor Transform.
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Jorge Bernal, F. Javier Sanchez, Gloria Fernandez Esparrach, Debora Gil, Cristina Rodriguez de Miguel, & Fernando Vilariño. (2015). "WM-DOVA Maps for Accurate Polyp Highlighting in Colonoscopy: Validation vs. Saliency Maps from Physicians " . Computerized Medical Imaging and Graphics, 43, 99–111.
Abstract: We introduce in this paper a novel polyp localization method for colonoscopy videos. Our method is based on a model of appearance for polyps which defines polyp boundaries in terms of valley information. We propose the integration of valley information in a robust way fostering complete, concave and continuous boundaries typically associated to polyps. This integration is done by using a window of radial sectors which accumulate valley information to create WMDOVA1 energy maps related with the likelihood of polyp presence. We perform a double validation of our maps, which include the introduction of two new databases, including the first, up to our knowledge, fully annotated database with clinical metadata associated. First we assess that the highest value corresponds with the location of the polyp in the image. Second, we show that WM-DOVA energy maps can be comparable with saliency maps obtained from physicians' fixations obtained via an eye-tracker. Finally, we prove that our method outperforms state-of-the-art computational saliency results. Our method shows good performance, particularly for small polyps which are reported to be the main sources of polyp miss-rate, which indicates the potential applicability of our method in clinical practice.
Keywords: Polyp localization; Energy Maps; Colonoscopy; Saliency; Valley detection
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Jose Elias Yauri, M. Lagos, H. Vega-Huerta, P. de-la-Cruz, G.L.E Maquen-Niño, & E. Condor-Tinoco. (2023). "Detection of Epileptic Seizures Based-on Channel Fusion and Transformer Network in EEG Recordings " . International Journal of Advanced Computer Science and Applications, 14(5), 1067–1074.
Abstract: According to the World Health Organization, epilepsy affects more than 50 million people in the world, and specifically, 80% of them live in developing countries. Therefore, epilepsy has become among the major public issue for many governments and deserves to be engaged. Epilepsy is characterized by uncontrollable seizures in the subject due to a sudden abnormal functionality of the brain. Recurrence of epilepsy attacks change people’s lives and interferes with their daily activities. Although epilepsy has no cure, it could be mitigated with an appropriated diagnosis and medication. Usually, epilepsy diagnosis is based on the analysis of an electroencephalogram (EEG) of the patient. However, the process of searching for seizure patterns in a multichannel EEG recording is a visual demanding and time consuming task, even for experienced neurologists. Despite the recent progress in automatic recognition of epilepsy, the multichannel nature of EEG recordings still challenges current methods. In this work, a new method to detect epilepsy in multichannel EEG recordings is proposed. First, the method uses convolutions to perform channel fusion, and next, a self-attention network extracts temporal features to classify between interictal and ictal epilepsy states. The method was validated in the public CHB-MIT dataset using the k-fold cross-validation and achieved 99.74% of specificity and 99.15% of sensitivity, surpassing current approaches.
Keywords: Epilepsy; epilepsy detection; EEG; EEG channel fusion; convolutional neural network; self-attention
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Josep Llados, & Enric Marti. (1999)." A graph-edit algorithm for hand-drawn graphical document recognition and their automatic introduction into CAD systems" . Machine Graphics & Vision, 8, 195–211.
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Josep Llados, & Enric Marti. (1999)." Graph-edit algorithms for hand-drawn graphical document recognition and their automatic introduction" . Machine Graphics & Vision journal, special issue on Graph transformation, .
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Josep Llados, Enric Marti, & Juan J.Villanueva. (2001)." Symbol recognition by error-tolerant subgraph matching between region adjacency graphs" . IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10), 1137–1143.
Abstract: The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content.
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