Red Generativa Antagónica Pix2Pix para la Segmentación de Núcleos Celulares y Citoplasma en Imágenes de Frotis de Papanicolaou
DOI:
https://doi.org/10.17488/RMIB.46.2.1462Palabras clave:
cáncer, cGAN, segmentación, PAP, Pix2PixResumen
En el campo de las imágenes médicas para la prueba de Papanicolaou, identificar con precisión las regiones de interés, como el núcleo y el citoplasma, sigue siendo un desafío crítico debido a la compleja morfología y las estructuras superpuestas en las imágenes de células cervicales. Esta complejidad aumenta el riesgo de identificaciones erróneas, lo que podría llevar a falsos positivos en el diagnóstico asistido por computadora. Para abordar este problema, este estudio presenta un enfoque novedoso mediante el desarrollo y evaluación de un marco para la segmentación precisa de núcleos y citoplasmas en imágenes de células cervicales, utilizando un modelo basado en cGAN, Pix2Pix, aplicado a un conjunto de datos validado por especialistas. Las imágenes generadas se compararon con las imágenes objetivo, se convirtieron a formato binario y se realizó una operación AND para evaluar la superposición de píxeles en las áreas de interés. Las métricas de evaluación destacaron una precisión de segmentación del 88.8 % y una sensibilidad del 89.62 % para los núcleos, mientras que, para el citoplasma, la precisión alcanzó el 89.62 % y la sensibilidad el 99.34 %. Los índices de Jaccard fueron del 80.89 % para los núcleos y del 96.71 % para el citoplasma. Estos resultados demuestran la efectividad del modelo en la segmentación de núcleos y citoplasmas en células cervicales.
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