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TSMS-SAM2: Muestreo Temporal Multiescala y Poda con División de Memoria para Segmentación y Seguimiento de Objetos en

Guoping Xu¹, Hua-Chieh Shao¹, You Zhang¹

¹The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.

Machine Learning. Health

|January 26, 2026

Resumen

Este estudio presenta TSMS-SAM2, un nuevo marco para la segmentación y el seguimiento de objetos en videos quirúrgicos. Mejora la precisión y la eficiencia al abordar el movimiento rápido y los problemas de memoria en los modelos fundacionales.

Área de la Ciencia:

Visión por Computadora
Imágenes Médicas
Robótica Quirúrgica

Sus antecedentes:

Los modelos fundacionales como Segment Anything Model 2 (SAM2) han avanzado en la segmentación y el seguimiento de objetos en video (VOST) indicables.
La aplicación de VOST a videos quirúrgicos es difícil debido al movimiento complejo y la redundancia de memoria en SAM2, lo que dificulta el aprendizaje eficaz.
Los métodos existentes tienen dificultades con la naturaleza dinámica e intrincada de los entornos quirúrgicos.

Objetivo del estudio:

Mejorar la VOST indicable en videos quirúrgicos mediante el desarrollo de un marco novedoso, TSMS-SAM2.
Abordar los desafíos que plantean el movimiento rápido de objetos y la redundancia de memoria en SAM2 para aplicaciones quirúrgicas.
Mejorar la robustez y la eficiencia de la segmentación en escenarios quirúrgicos complejos.

Principales métodos:

Se propuso el marco TSMS-SAM2 que presenta aumentación de muestreo de video multiescala temporal para la variabilidad del movimiento.
Se implementó un mecanismo de división y poda de memoria para organizar y filtrar las características de los fotogramas anteriores.
Se utilizaron los conjuntos de datos EndoVis2017 y EndoVis2018 para la evaluación.

Principales resultados:

TSMS-SAM2 logró un rendimiento de vanguardia con puntuaciones medias de Dice del 95,24±0,96 % en EndoVis2017 y del 86,73±15,46 % en EndoVis2018.
Superó a los métodos anteriores basados en SAM y específicos de la tarea en la segmentación de videos quirúrgicos.
Los estudios de ablación validaron la efectividad de la aumentación temporal multiescala y la división de memoria.

Conclusiones:

TSMS-SAM2 ofrece una solución robusta y eficiente para VOST indicable en videos quirúrgicos complejos.
El marco propuesto mejora significativamente la precisión de la segmentación al abordar la dinámica del movimiento y la redundancia de memoria.
Los hallazgos resaltan el potencial de TSMS-SAM2 para avanzar en la cirugía asistida por computadora y el análisis.

Palabras clave:

Aumento Aprendizaje Profundo Memoria Segment Anything 2 Seguimiento y Segmentación de Objetos en Video

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