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Red de fusión de escaneo cruzado: un marco basado en el registro para la segmentación de ultrasonido 3D eficiente en
Pengyu Chen1,2, Zixue Zeng2,3, Xiaoyan Zhao2
1Department of Computer Science, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Medical physics
|January 30, 2026
Resumen
La novedosa Red de Fusión de Escaneo Cruzado (CSFN) segmenta con precisión imágenes de ultrasonido 3D para la investigación del dolor lumbar crónico (DLC). Este método mejora significativamente la segmentación de capas anatómicas con anotaciones manuales mínimas, acelerando el análisis clínico.
Área de la Ciencia:
- Imagenología médica
- Inteligencia artificial
- Ingeniería biomédica
Sus antecedentes:
- El dolor lumbar crónico (DLC) impacta significativamente la calidad de vida, pero sus mecanismos son poco comprendidos.
- La imagenología por ultrasonido 3D proporciona información sobre el DLC, pero enfrenta desafíos de anotación manual debido al volumen de datos y la calidad de la imagen.
Objetivo del estudio:
- Desarrollar y validar la Red de Fusión de Escaneo Cruzado (CSFN) para segmentar capas de tejido anatómico en imágenes de ultrasonido 3D.
- Mejorar la eficiencia de la anotación para datos de ultrasonido 3D en la investigación de DLC.
Principales métodos:
- Se utilizaron volúmenes de ultrasonido 3D en modo B de la región lumbar con seis capas de tejido anotadas.
- Se desarrolló CSFN, una red de registro elástico estilo VoxelMorph con Pérdida de Distancia de Hausdorff Proyectada (PHDL).
- Se implementaron estrategias de aprendizaje eficientes en cuanto a muestras (CSFN-SEL) y semisupervisadas (CSFN-SSL), incluida la síntesis de datos para la aumentación.
Principales resultados:
- CSFN-SEL superó a nnU-Net totalmente supervisado, mejorando el coeficiente de Dice medio en un 5,28 % (p < 0,05/3).
- CSFN-SSL logró una precisión de segmentación del 79,33 % (±1,96 %) y del 81,14 % (±1,43 %) con solo 5 y 19 muestras de entrenamiento, respectivamente.
- CSFN-SSL demostró una ganancia de rendimiento del 9,51 % con anotación mínima utilizando solo cinco exploraciones etiquetadas (tamaño del efecto d = 0,671, p < 0,05/3).
Conclusiones:
- CSFN mejora significativamente la precisión y la robustez de la segmentación en imágenes de ultrasonido 3D, incluso con datos etiquetados limitados.
- El enfoque CSFN ofrece una solución práctica para avanzar en la imagenología y el análisis de DLC.
- La segmentación automatizada con CSFN puede acelerar la evaluación y el manejo clínico del DLC.
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