9,143 research outputs found
Ultrasound IMT measurement on a multi-ethnic and multi-institutional database: Our review and experience using four fully automated and one semi-automated methods
Automated and high performance carotid intima-media thickness (IMT) measurement is gaining increasing importance in clinical practice to assess the cardiovascular risk of patients. In this paper, we compare four fully automated IMT measurement techniques (CALEX, CAMES, CARES and CAUDLES) and one semi-automated technique (FOAM). We present our experience using these algorithms, whose lumen-intima and media-adventitia border estimation use different methods that can be: (a) edge-based; (b) training-based; (c) feature-based; or (d) directional Edge-Flow based. Our database (DB) consisted of 665 images that represented a multi-ethnic group and was acquired using four OEM scanners. The performance evaluation protocol adopted error measures, reproducibility measures, and Figure of Merit (FoM). FOAM showed the best performance, with an IMT bias equal to 0.025 ± 0.225 mm, and a FoM equal to 96.6%. Among the four automated methods, CARES showed the best results with a bias of 0.032 ± 0.279 mm, and a FoM to 95.6%, which was statistically comparable to that of FOAM performance in terms of accuracy and reproducibility. This is the first time that completely automated and user-driven techniques have been compared on a multi-ethnic dataset, acquired using multiple original equipment manufacturer (OEM) machines with different gain settings, representing normal and pathologic case
Machine learning methods for the characterization and classification of complex data
This thesis work presents novel methods for the analysis and classification of medical images and, more generally, complex data. First, an unsupervised machine learning method is proposed to order anterior chamber OCT (Optical Coherence Tomography) images according to a patient's risk of developing angle-closure glaucoma. In a second study, two outlier finding techniques are proposed to improve the results of above mentioned machine learning algorithm, we also show that they are applicable to a wide variety of data, including fraud detection in credit card transactions. In a third study, the topology of the vascular network of the retina, considering it a complex tree-like network is analyzed and we show that structural differences reveal the presence of glaucoma and diabetic retinopathy. In a fourth study we use a model of a laser with optical injection that presents extreme events in its intensity time-series to evaluate machine learning methods to forecast such extreme events.El presente trabajo de tesis desarrolla nuevos mĂ©todos para el análisis y clasificaciĂłn de imágenes mĂ©dicas y datos complejos en general. Primero, proponemos un mĂ©todo de aprendizaje automático sin supervisiĂłn que ordena imágenes OCT (tomografĂa de coherencia Ăłptica) de la cámara anterior del ojo en funciĂłn del grado de riesgo del paciente de padecer glaucoma de ángulo cerrado. Luego, desarrollamos dos mĂ©todos de detecciĂłn automática de anomalĂas que utilizamos para mejorar los resultados del algoritmo anterior, pero que su aplicabilidad va mucho más allá, siendo Ăştil, incluso, para la detecciĂłn automática de fraudes en transacciones de tarjetas de crĂ©dito. Mostramos tambiĂ©n, cĂłmo al analizar la topologĂa de la red vascular de la retina considerándola una red compleja, podemos detectar la presencia de glaucoma y de retinopatĂa diabĂ©tica a travĂ©s de diferencias estructurales. Estudiamos tambiĂ©n un modelo de un láser con inyecciĂłn Ăłptica que presenta eventos extremos en la serie temporal de intensidad para evaluar diferentes mĂ©todos de aprendizaje automático para predecir dichos eventos extremos.Aquesta tesi desenvolupa nous mètodes per a l’anĂ lisi i la classificaciĂł d’imatges mèdiques i dades complexes. Hem proposat, primer, un mètode d’aprenentatge automĂ tic sense supervisiĂł que ordena
imatges OCT (tomografia de coherència òptica) de la cambra anterior de l’ull en funció del grau de risc del pacient de patir glaucoma d’angle tancat. Després, hem desenvolupat dos mètodes de detecció automà tica d’anomalies que hem utilitzat per millorar els resultats de l’algoritme anterior, però que la seva aplicabilitat va molt més enllà , sent útil, fins i tot, per a la detecció automà tica de fraus en transaccions de targetes de crèdit. Mostrem també, com en analitzar la topologia de la xarxa vascular de la retina considerant-la una xarxa complexa, podem detectar la presència de glaucoma i de retinopatia diabètica a través de diferències estructurals. Finalment, hem estudiat un là ser amb injecció òptica, el qual presenta esdeveniments extrems en la sèrie temporal d’intensitat.
Hem avaluat diferents mètodes per tal de predir-los.Postprint (published version
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