A reliable criterion for the correct delimitation of the foveal avascular zone in diabetic patients

Background: Manual segmentation of the Foveal Avascular Zone (FAZ) has a high level of variability. Research into retinas needs coherent segmentation sets with low variability. Methods: Retinal optical coherence tomography angiography (OCTA) images from type-1 diabetes mellitus (DM1), type-2 diabetes mellitus (DM2) and healthy patients were included. Superficial (SCP) and deep (DCP) capillary plexus FAZs were manually segmented by different observers. After comparing the results, a new criterion was established to reduce variability in the segmentations. The FAZ area and acircularity were also studied. Results: The new segmentation criterion produces smaller areas (closer to the real FAZ) with lower variability than the different criteria of the explorers in both plexuses for the three groups. This was particularly noticeable for the DM2 group with damaged retinas. The acircularity values were also slightly reduced with the final criterion in all groups. The FAZ areas with lower values showed slightly higher acircularity values. We also have a consistent and coherent set of segmentations with which to continue our research. Conclusions: Manual segmentations of FAZ are generally carried out with little attention to the consistency of the measurements. A novel criterion for segmenting the FAZ allows segmentations made by different observers to be more similar

Caracterización del Edema Macular Diabético mediante análisis automático de Tomografías de Coherencia Óptica

Programa Oficial de Doctorado en Computación. 5009V01[Abstract] Diabetic Macular Edema (DME) is one of the most important complications of diabetes and a leading cause of preventable blindness in the developed countries. Among the di erent image modalities, Optical Coherence Tomography (OCT) is a non-invasive, cross-sectional and high-resolution imaging technique that is commonly used for the analysis and interpretation of many retinal structures and ocular disorders. In this way, the development of Computer-Aided Diagnosis (CAD) systems has become relevant over the recent years, facilitating and simplifying the work of the clinical specialists in many relevant diagnostic processes, replacing manual procedures that are tedious and highly time-consuming. This thesis proposes a complete methodology for the identi cation and characterization of DMEs using OCT images. To do so, the system combines and exploits di erent clinical knowledge with image processing and machine learning strategies. This automatic system is able to identify and characterize the main retinal structures and several pathological conditions that are associated with the DME disease, following the clinical classi cation of reference in the ophthalmological eld. Despite the complexity and heterogeneity of this relevant ocular pathology, the proposed system achieved satisfactory results, proving to be robust enough to be used in the daily clinical practice, helping the clinicians to produce a more accurate diagnosis and indicate adequate treatments[Resumen] El Edema Macular Diabético (EMD) es una de las complicaciones más importantes de la diabetes y una de las principales causas de ceguera prevenible en los países desarrollados. Entre las diferentes modalidades de imagen, la Tomografía de Coherencia Óptica (TCO) es una técnica de imagen no invasiva, transversal y de alta resolución que se usa comúnmente para el análisis e interpretación de múltiples estructuras retinianas y trastornos oculares. De esta manera, el desarrollo de los sistemas de Diagnóstico Asistido por Ordenador (DAO) se ha vuelto relevante en los últimos años, facilitando y simplificando el trabajo de los especialistas clínicos en muchos procesos diagnósticos relevantes, reemplazando procedimientos manuales que son tediosos y requieren mucho tiempo. Esta tesis propone una metodología completa para la identificación y caracterización de EMDs utilizando imágenes TCO. Para ello, el sistema desarrollado combina y explota diferentes conocimientos clínicos con estrategias de procesamiento de imágenes y aprendizaje automático. Este sistema automático es capaz de identificar y caracterizar las principales estructuras retinianas y diferentes afecciones patológicas asociadas con el EMD, siguiendo la clasificación clínica de referencia en el campo oftalmológico. A pesar de la complejidad de esta relevante patología ocular, el sistema propuesto logró resultados satisfactorios, demostrando ser lo sufi cientemente robusto como para ser usado en la práctica clínica diaria, ayudando a los médicos a producir diagnósticos más precisos y tratamientos más adecuados.[Resumo] O Edema Macular Diabético ( EMD) é unha das complicacións máis importantes da diabetes e unha das principais causas de cegueira prevenible nos países desenvoltos. Entre as diferentes modalidades de imaxe, a Tomografía de Coherencia Óptica ( TCO) é unha técnica de imaxe non invasiva, transversal e de alta resolución que se usa comunmente para a análise e interpretación de múltiples estruturas retinianas e trastornos oculares. Desta maneira, o desenvolvemento dos sistemas de Diagnóstico Asistido por Computador ( DAO) volveuse relevante nos últimos anos, facilitando e simplificando o traballo dos especialistas clínicos en moitos procesos diagnósticos relevantes, substituíndo procedementos manuais que son tediosos e requiren moito tempo. Esta tese propón unha metodoloxía completa para a identificación e caracterización de EMDs utilizando imaxes TCO. Para iso, o sistema desenvolto combina e explota diferentes coñecementos clínicos con estratexias de procesamento de imaxes e aprendizaxe automático. Este sistema automático é capaz de identificar e caracterizar as principais estruturas retinianas e diferentes afeccións patolóxicas asociadas co EMD, seguindo a clasificación clínica de referencia no campo oftalmolóxico. A pesar da complexidade desta relevante patoloxía ocular, o sistema proposto logrou resultados satisfactorios, demostrando ser o sufi cientemente robusto como para ser usado na práctica clínica diaria, axudando aos médicos para producir diagnósticos máis precisos e tratamentos máis adecuados

Blood vessel segmentation in the analysis of retinal and diaphragm images

The segmentation and characterization of structures in medical images represents an important part of the diagnostic and research procedures in medicine. This thesis focuses on the characterization methods in two application fields that make use of two imaging modalities. The first topic is the characterization of the blood vessel structure in the human retina and the second is the characterization of diaphragm movement during breathing. The imaged blood vessel structures are considered important landmarks in both applications. The framework for the retinal image processing and analysis starts with the testing of five publicly available blood vessel segmentation methods for retinal images. The parameters of the methods are optimized on five databases with the ground truth for blood vessels. An approach for predicting the method parameters is proposed based on the optimization results. The parameter prediction approach is then applied to obtain vessel segmentation on a new database and an automatic approach to the blood vessel classification and computation of the arteriovenous ratio is proposed and evaluated on the new database. The framework for the diaphragm image processing and analysis is based on the measurement of diaphragm motion. The motion is characterized by a set of features quantifying the amplitude and frequency of the breathing pattern, as well as a portion of the nonharmonic movements that occur. In addition, a set of static features like the diaphragm slope and height are proposed. Two approaches for the motion measurement are proposed and compared. A statistical evaluation of the proposed features is performed by comparing measurements from people with and without spinal findings. The results from the retinal image processing and analysis revealed the possibility of the successful prediction of the parameters of the blood vessel segmentation methods. The automatic approach for the automatic arteriovenous ratio estimation revealed a stronger association with blood pressure than the manually estimated ratio. The results from the diaphragm image processing and analysis confirmed differences in the position, shape and breathing patterns between the healthy people and people suffering from spinal findings. The blood vessel structure was shown to be a reliable marker for characterizing the diaphragm motion.Katedra kybernetik