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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Imaging Biological Samples with Optical Microscopy01:18

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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
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Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Electron Microscope Tomography and Single-particle Reconstruction01:07

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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
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Positron Emission Tomography01:29

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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
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Confocal Fluorescence Microscopy01:16

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Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
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In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography
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Octascope: Un modelo pre-entrenado ligero para la tomografía de coherencia óptica

Haoyang Cui1, Chen Wang2, Paul Calle1

  • 1School of Computer Science, Gallogly College of Engineering, The University of Oklahoma, Norman, OK 73019, USA.

IEEE access : practical innovations, open solutions
|August 28, 2025
PubMed
Resumen

Octascope, un nuevo modelo de aprendizaje profundo, mejora el análisis de imágenes de Tomografía de Coherencia Óptica (OCT). Se logra una alta precisión y velocidades más rápidas para aplicaciones clínicas en tiempo real mediante el uso de entrenamiento previo de múltiples dominios.

Palabras clave:
Aprendizaje profundoImágenes médicas de los PTUOctascopioEspecífico del dominiomodelo de la fundaciónpeso ligerotransferencia de aprendizaje

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Área de la Ciencia:

  • Imágenes biomédicas
  • Inteligencia artificial médica
  • Aprendizaje profundo para el análisis de imágenes médicas

Sus antecedentes:

  • La tomografía de coherencia óptica (OCT) proporciona imágenes de tejido subterráneo de alta resolución.
  • El aprendizaje profundo para el análisis de PTU se enfrenta a desafíos con datos de capacitación limitados y velocidades de inferencia lentas.
  • El desarrollo de modelos de IA eficientes y precisos para los PTU es crucial para las aplicaciones clínicas.

Objetivo del estudio:

  • Desarrollar un modelo de red neuronal convolucional ligero y específico de dominio (CNN) para un análisis de imágenes de los PTU eficiente y preciso.
  • Mejorar la generalización de los modelos de análisis de los PTU en diversos tipos de tejidos.
  • Lograr un equilibrio entre la eficiencia computacional y la precisión del diagnóstico para las aplicaciones de los PTU en tiempo real.

Principales métodos:

  • Desarrolló Octascope, un modelo CNN ligero para el análisis de imágenes OCT.
  • Utilizó un enfoque de aprendizaje curricular para la formación previa: imágenes naturales (ImageNet) seguidas de diversos tejidos de los PTU (retina, abdomen, riñón).
  • Evaluó Octascope en tareas de detección de tejido epidural y diagnóstico de retina, comparándolo con los métodos existentes y un modelo basado en Transformer.

Principales resultados:

  • Octascope demostró una mejor precisión en la detección de tejido epidural (9,13% sobre el aprendizaje de una sola tarea, 5,95% sobre el aprendizaje de transferencia específico de OCT).
  • Octascope superó a VGG16 (5,36%) y ResNet50 (6,66%) en el diagnóstico de la retina.
  • Octascope logró una velocidad de inferencia de 2 a 4,4 veces más rápida que RETFound con una precisión comparable o mejor.

Conclusiones:

  • Octascope ofrece un avance significativo en el análisis de imágenes OCT, equilibrando la eficiencia computacional y la precisión del diagnóstico.
  • La estrategia de preentrenamiento multidominio mejora la generalización del modelo en diferentes tipos de tejidos.
  • Octascope es adecuado para aplicaciones clínicas en tiempo real que requieren una interpretación de imágenes OCT rápida y confiable.