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Updated: Jun 30, 2026

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Un marco de aprendizaje profundo para la segmentación integral de los núcleos grises profundos

Abhinabha Barat1, Shridhar Singh2, Ranjani Ramesh3

  • 1Cornell Tech, New York, NY.

medRxiv : the preprint server for health sciences
|December 25, 2025
PubMed
Resumen

Este estudio presenta THOMASINA, un pipeline de aprendizaje profundo para la segmentación rápida y precisa de estructuras cerebrales profundas a partir de imágenes de resonancia magnética. El método reduce significativamente el tiempo de procesamiento y mejora la precisión de la segmentación, allanando el camino para estudios de neuroimagen a gran escala.

Palabras clave:
aprendizaje profundoresonancia magnéticasegmentación de imágenesneuroimageninteligencia artificialanálisis de imágenes médicastrastornos neurológicosestructuras cerebrales profundas

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Área de la Ciencia:

  • Neuroimagen; Inteligencia Artificial; Análisis de Imágenes Médicas

Sus antecedentes:

  • La segmentación precisa de las estructuras de la materia gris profunda (tálamo, núcleos basales) es crucial para comprender los trastornos neurológicos.; Los desafíos incluyen el bajo contraste de RM, el largo tiempo de procesamiento y las herramientas fragmentadas.

Objetivo del estudio:

  • Desarrollar un pipeline de aprendizaje profundo (THOMASINA) para la segmentación subcortical integral.; Permitir la segmentación a partir de RM estándar ponderada en T1 (T1w) y con anulación de la sustancia blanca (WMn).

Principales métodos:

  • Se entrenaron múltiples modelos de aprendizaje profundo 3D (SwinUNETR, DiNTS, SegResNet) utilizando etiquetas de un método multi-atlas de última generación.; Se emplearon volúmenes recortados para el entrenamiento y se probó en diversos conjuntos de datos.; Se utilizó un paso de síntesis para generar contraste similar a WMn a partir de RM T1w.

Principales resultados:

  • SegResNet logró el mayor rendimiento (DICE medio 0.89 en el dominio, 0.85 fuera del dominio), superando a otros modelos.; El contraste sintético WMn produjo una segmentación comparable a las imágenes reales de WMn.; Reducción del tiempo de segmentación de minutos a segundos por sujeto.

Conclusiones:

  • THOMASINA proporciona una solución rápida, reproducible y escalable para la segmentación subcortical utilizando RM T1w estándar.; Aborda las principales barreras de implementación y apoya el descubrimiento de biomarcadores en imágenes a gran escala.; Demuestra robustez en diferentes intensidades de campo, fabricantes y cohortes de enfermedades.