dc.contributor | Gómez-Mendoza, Juan Bernardo | |
dc.contributor | Soft and Hard Applied Computing (SHAC) | |
dc.creator | Vargas López, Julián David | |
dc.date.accessioned | 2022-06-13T20:56:36Z | |
dc.date.available | 2022-06-13T20:56:36Z | |
dc.date.created | 2022-06-13T20:56:36Z | |
dc.date.issued | 2022 | |
dc.identifier | https://repositorio.unal.edu.co/handle/unal/81576 | |
dc.identifier | Universidad Nacional de Colombia | |
dc.identifier | Repositorio Institucional Universidad Nacional de Colombia | |
dc.identifier | https://repositorio.unal.edu.co/ | |
dc.description.abstract | El aprendizaje profundo ha tenido un impacto notable en el análisis de imágenes médicas.
Desde clasificar tejidos hasta localizar áreas anormales en una región, herramientas como las redes neuronales convolucionales (CNNs) y sus múltiples arquitecturas han mostrado resultados
prometedores en esta área de la medicina. En patología digital, estos modelos neuronales se están convirtiendo cada vez más en una herramienta vital en el apoyo diagnostico
y pronóstico para los patólogos. Actualmente, múltiples instituciones médicas utilizan CNNs en sus laboratorios para optimizar el tiempo de búsqueda de regiones anormales en imágenes médicas digitales - como lo son las muestras de biopsias -, generando automáticamente información relevante en el diagnóstico y pronóstico de un paciente. Su aplicabilidad se ha
logrado en gran medida gracias a la existencia de habilitadores tecnológicos, como hardware especializado (p.e., procesadores gráficos o GPUs), que permiten manipular y procesar
grandes cantidades de datos de manera simultánea. Sin embargo, las GPUs no puede procesar las imágenes en algunos casos debido a su tamaño. Las imágenes histopatológicas son un ejemplo de este tipo de imágenes, donde el tamaño de las imágenes puede ser del orden de hasta 25.000 x 30.000 píxeles. Se han diseñado estrategias que permiten manipular este tipo de imágenes, desde optimizar la forma de entrenar las CNNs hasta dividir la imagen
en parches con un tamaño manejable. Sin embargo, analizar la biopsia, elegir las áreas de interés y crear las etiquetas correspondientes, son procesos que se realizan de forma manual
y resultan dispendiosos para el especialista. Por lo tanto, es necesario desarrollar nuevas estrategias para apoyar al patólogo en estas tareas. En este documento, se plantean tres
metodologías que permiten apoyar al patólogo en el análisis de imágenes histopatológicas de tejido prostático.
El primer diseño emplea transformaciones de color que proporcionan información adicional sobre la imagen. Se mostró cómo estas técnicas mejoran y resaltan las estructuras presentes
en el tejido (se logra una mejor definición de los núcleos, aumenta el contraste en el estroma y las células epiteliales, etc.). Estas transformaciones de color tienen la ventaja de que su
implementación no genera un costo computacional considerable, permitiendo manipular la imagen de forma rápida, incluso en ordenadores que no posean un hardware especializado.
El segundo diseño analiza el proceso de segmentación de una imagen con redes neuronales convolucionales. Se expuso el problema que se genera cuando se trata de clasificar estas
imágenes dividiéndola en pequeños parches, en donde el tiempo de segmentación por imagen puede llegar a las 24 horas o más. En consecuencia, se diseña una estrategia para mitigar este problema empleando un porcentaje de pixeles de la imagen para segmentarla.
Esta técnica permite disminuir el tiempo de segmentación a solo 5 minutos por imagen.
Además, se logró demostrar experimentalmente, que la información que se pierde a medida que se disminuye el porcentaje de pixeles es muy pequeña (cerca del 5%), en comparación con el proceso en donde se emplean todos los pixeles de la imagen. Finalmente, nuestro tercer diseño consiste en crear una metodología que permite localizar las áreas sospechosas en imágenes de cáncer de próstata utilizando redes neuronales convolucionales. Empleando los resultados de la etapa anterior, se diseña una red neuronal convolucional que posee una
cantidad pequeña de parámetros de entrenamiento (cerca de 50 mil). Esta red realiza dos tareas distintas: segmentar el estroma y segmentar el tejido sospechoso. Uniendo estos dos resultados y descartando los pixeles que pertenecieran al estroma segmentado, se logra localizar zonas sospechosas en imágenes de tejido prostático. Adicionalmente esta red se diseño pensando en el costo computacional que generan algunas redes en el estado del arte, y en el sobredimensionamiento del problema que puede surgir al emplear dichas redes. (Texto tomado de la fuente) | |
dc.description.abstract | Deep learning has had a noticeable impact on medical image analysis. From classifying
tissues to locating abnormal areas in a region, CNNs and their multiple architectures have
shown a future in this area of medicine. In digital pathology, these neural models are increasingly
becoming a vital tool in diagnostic and prognostic support for pathologists. Currently,
multiple medical institutions use CNNs in their laboratories to optimize the search time
for abnormal regions of a complete tissue slide (biopsy sample), automatically generating
diagnoses and prognoses of a patient, etc. This success of CNN was achieved mainly by
using specialized hardware (GPUs) that allow large amounts of data to be manipulated and
processed. However, analyzing the biopsy, choosing the areas of interest and creating the
corresponding labels are processes that are carried out manually and are costly for the specialist.
Therefore, it is necessary to develop new strategies to support the pathologist in
these tasks. In this document, three methodologies are proposed that allow the pathologist
to be supported in the analysis of histopathological images of prostate tissue.
The rst layout employs color transformations that provide additional information about
the image. It was shown how these techniques improve and highlight the structures present
in the tissue (the shape of the nuclei was better de ned, the contrast increased in the stroma
and epithelial cells, etc.). These color transformations have the advantage that their implementation
does not generate a considerable computational cost, allowing the image to be
manipulated quickly, even on computers that do not have specialized hardware. The second
design analyzes the segmentation process of an image with convolutional neural networks.
The problem generated when we try to classify these images by dividing them into small
patches, where the segmentation time per image can reach 24 hours or more, was exposed.
Consequently, we designed a strategy to mitigate this problem by using a percentage of pixels
in the image to segment it. This technique allowed the segmentation time to be reduced to
only 5 minutes per image. In addition, we were able to demonstrate experimentally that the
information lost as we decrease the percentage of pixels is very small (about 5%), compared
to the process where all the pixels of the image are used. Finally, our third design creates
a methodology that locates suspicious areas in prostate cancer images using convolutional
neural networks. Using the previous stage results, we design a convolutional neural network
with a small number of training parameters (about 50 thousand). This network performs
two distinct tasks: segmenting the stroma and suspect tissue. Combining these two results
and discarding the pixels that belonged to the segmented stroma, it is possible to locate
suspicious areas in images of prostate tissue. Additionally, this network was designed considering
the computational cost generated by some networks in the state of the art and the
over-sizing of the problem that can arise when using these networks. | |
dc.language | eng | |
dc.publisher | Universidad Nacional de Colombia | |
dc.publisher | Manizales - Ingeniería y Arquitectura - Maestría en Ingeniería - Automatización Industrial | |
dc.publisher | Departamento de Ingeniería Eléctrica y Electrónica | |
dc.publisher | Facultad de Ingeniería y Arquitectura | |
dc.publisher | Manizales, Colombia | |
dc.publisher | Universidad Nacional de Colombia - Sede Manizales | |
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dc.rights | Reconocimiento 4.0 Internacional | |
dc.rights | http://creativecommons.org/licenses/by/4.0/ | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.title | Aplicación de técnicas de preprocesamiento y segmentación de imágenes para el apoyo diagnóstico en la detección de cáncer de próstata | |
dc.type | Trabajo de grado - Maestría | |