Identificación de lesiones ocupantes de espacio en imágenes médicas del riñón: Una revisión
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Fecha
2020
Autores
Saenz, Frank
Vera, Miguel
Rodríguez, Raúl
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Editor
Saber UCV, Universidad Central de Venezuela
Resumen
La tecnología existente para el reconocimiento automático
de imágenes ha impactado el campo de la medicina apoyando, en forma cada vez más confiable, los diagnósticos
que los profesionales médicos realizan de forma manual. En
el caso de la detección de lesiones ocupantes de espacio
(LOE) renales, se han desarrollado muchos estudios que
aplican diferentes técnicas para la segmentación de imágenes del riñón, y que han generado nuevos modelos propuestos que aportan al fortalecimiento del trabajo que se
viene realizando en el reconocimiento de imágenes médicas
de forma automática. El presente artículo hace una descripción de los diversos avances que se han reportado en la literatura científica con respecto a la segmentación del riñón
y de sus LOE en imágenes médicas de diferentes fuentes
como tomografía computarizada, resonancia magnética y ultrasonido. En ese sentido, se realizó una revisión sistemática
de los artículos publicados, validando el nivel científico y el
medio donde se publicó a través de la plataforma Scimago
Journal & Country Rank, seleccionando fechas de publicación desde el año 2005 en adelante. Las palabras claves
para realizar búsqueda fueron «Kidney Tumor», «Kidney
Cancer», «Kidney Segmentation», «Renal Cell Carcinoma»,
«Renal Tumors», «Renal Cysts», «Automatic Segmentation
Kidney». Este artículo brinda un panorama del trabajo que
viene desarrollando la comunidad académica y científica con
respecto al reconocimiento automático de tumores renales y
el avance en el desarrollo de modelos más avanzados que
ofrecen un nivel más alto de sensibilidad, especificidad y precisión en la detección de enfermedades del referido órgano,
particularmente, en imágenes médicas.
Existing technology for automatic image recognition has impacted the medicine field by supporting, in an increasingly reliable way, the diagnoses that clinicians perform manually. In the space-occupying renal lesions (SORL) detection cases, many studies have been developed that apply different techniques for kidney segmentation and they have generated new models that contribute to the strengthening of the work that has been carried out in the recognition of medical images automatically. This article describes several advances that have been reported in the scientific literature regarding kidney segmentation and it’s SORL in medical images from different sources such as computed tomography, magnetic resonance imaging, and ultrasound. In this sense, a systematic review of the published articles was carried out, validating the scientific level and the medium where it was published through the Scimago Journal & Country Rank platform, selecting publication dates from 2005 onwards. The keywords to search were “Kidney Tumor”, “Kidney Cancer”, “Kidney Segmentation”, “Renal Cell Carcinoma”, “Renal Tumors”, “Renal Cysts”, “Automatic Segmentation Kidney”. This paper provides an overview of the work that the academic and scientific community has been developing to the automatic recognition of kidney tumors and progress in the development of more advanced models that offer a higher level of sensitivity, specificity, and precision in the detection of diseases of the referred organ, particularly, in medical images.
Existing technology for automatic image recognition has impacted the medicine field by supporting, in an increasingly reliable way, the diagnoses that clinicians perform manually. In the space-occupying renal lesions (SORL) detection cases, many studies have been developed that apply different techniques for kidney segmentation and they have generated new models that contribute to the strengthening of the work that has been carried out in the recognition of medical images automatically. This article describes several advances that have been reported in the scientific literature regarding kidney segmentation and it’s SORL in medical images from different sources such as computed tomography, magnetic resonance imaging, and ultrasound. In this sense, a systematic review of the published articles was carried out, validating the scientific level and the medium where it was published through the Scimago Journal & Country Rank platform, selecting publication dates from 2005 onwards. The keywords to search were “Kidney Tumor”, “Kidney Cancer”, “Kidney Segmentation”, “Renal Cell Carcinoma”, “Renal Tumors”, “Renal Cysts”, “Automatic Segmentation Kidney”. This paper provides an overview of the work that the academic and scientific community has been developing to the automatic recognition of kidney tumors and progress in the development of more advanced models that offer a higher level of sensitivity, specificity, and precision in the detection of diseases of the referred organ, particularly, in medical images.
Descripción
Palabras clave
Riñón, Tumor renal, Imágenes médicas, Resonancia magnética, Ultrasonido, Tomografía computarizada, Kidney, Kidney tumor, Medical images, Magnetic resonance, Computerized tomography