UMInSe: Método no Supervisado para la Segmentación y Detección de Instrumentos Quirúrgicos Basado en K-means
DOI:
https://doi.org/10.17488/RMIB.45.3.2Palabras clave:
base de datos JIGSAWS, K-means, segmentación instrumentos quirúrgicos, segmentación no supervisadaResumen
La segmentación de instrumentos quirúrgicos en imágenes es crucial para mejorar la precisión y eficiencia en cirugía, pero actualmente depende de anotaciones manuales costosas y laboriosas. Un enfoque no supervisado es una solución prometedora para este desafío. Este artículo introduce un método de segmentación de instrumentos quirúrgicos utilizando aprendizaje automático no supervisado, basado en el algoritmo K-means, para identificar Regiones de Interés (ROI) en imágenes y crear el ground truth de las imágenes para el entrenamiento de redes neuronales. La corrección Gamma ajusta el brillo de la imagen y mejora la identificación de áreas que contienen instrumentos quirúrgicos. El algoritmo K-means agrupa píxeles similares y detecta las ROI a pesar de los cambios en la iluminación, logrando una segmentación eficiente a pesar de las variaciones en la iluminación de la imagen y los objetos obstructores. Por lo tanto, la red neuronal generaliza el aprendizaje de las características de la imagen para la segmentación de instrumentos en diferentes tareas. Los resultados experimentales utilizando las bases de datos JIGSAWS y EndoVis demuestran la efectividad y robustez del método, con un error mínimo (0.0297) y alta precisión (0.9602). Estos resultados subrayan la precisión en la detección y segmentación de instrumentos quirúrgicos, lo cual es crucial para automatizar la detección de instrumentos en procedimientos quirúrgicos sin conjuntos de datos pre-etiquetados. Además, esta técnica podría aplicarse en aplicaciones quirúrgicas como la evaluación de habilidades del cirujano y la planificación de movimientos de robots, donde la detección precisa de instrumentos es indispensable.
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