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MFS-Unet: Una red Mamba de visión multipista para la segmentación precisa de nódulos tiroideos

Shaoqiang Wang1, Zhongran Liu1, Guiling Shi1

  • 1Qingdao University of Technology, Qingdao, Shandong, China.

IET systems biology
|February 5, 2026
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Resumen

Este estudio presenta MFS-Unet, una red novedosa para la segmentación precisa de nódulos tiroideos en imágenes de ultrasonido. Aborda eficazmente desafíos como los límites borrosos y el ruido, mejorando la precisión diagnóstica.

Palabras clave:
técnicas biológicasimagen biomédica ópticarectificación de etiquetasmamba de visión multipistasegmentación de nódulos tiroideos

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Área de la Ciencia:

  • Imágenes Médicas
  • Inteligencia Artificial
  • Visión por Computadora

Sus antecedentes:

  • La segmentación automatizada de nódulos tiroideos es crucial para el diagnóstico y tratamiento clínicos.
  • Los desafíos en la segmentación de nódulos tiroideos incluyen límites borrosos, escalas variables, ruido y anotaciones inexactas.

Objetivo del estudio:

  • Proponer una novedosa red de segmentación de imágenes médicas, MFS-Unet, para la segmentación precisa de nódulos tiroideos.
  • Mejorar el rendimiento de la segmentación abordando problemas de tamaño variable de nódulos, ruido de fondo y ruido de etiquetas.

Principales métodos:

  • Se desarrolló MFS-Unet incorporando tres módulos novedosos: Mamba de Visión Multipista (MPV) para contexto global y características multiescala, Feature Gating (FG) para mejorar la información de los límites y Supervised Label Rectification (SLR) para manejar el ruido de las etiquetas.
  • El módulo MPV utiliza modelos de espacio de estados (SSM) para una captura eficiente del contexto global con complejidad lineal.
  • El módulo FG emplea un mecanismo de atención para refinar las características en las conexiones de salto, suprimiendo el ruido y reforzando los límites de los nódulos.
  • El módulo SLR ajusta dinámicamente los pesos de pérdida para mejorar la robustez contra las etiquetas de entrenamiento ruidosas.

Principales resultados:

  • MFS-Unet demostró un rendimiento superior en todas las métricas de evaluación en tres conjuntos de datos públicos de ultrasonido tiroideo (DDTI, TG3K, TN3K).
  • La red propuesta superó a varios métodos de segmentación de vanguardia en precisión y robustez.
  • Los resultados experimentales validan la efectividad de los módulos MPV, FG y SLR para mejorar la precisión de la segmentación.

Conclusiones:

  • MFS-Unet ofrece un avance significativo en la segmentación automatizada de nódulos tiroideos a partir de imágenes de ultrasonido.
  • La red muestra un potencial sustancial para la segmentación precisa en entornos clínicos complejos de ultrasonido.
  • Los módulos innovadores abordan eficazmente los desafíos clave de la segmentación, allanando el camino para mejorar las herramientas de diagnóstico.