Purpose: High-speed video (HSV) phase detection (PD) segmentation is vital in nuclear reactors, chemical processing, and electronics cooling for detecting vapor, liquid, and microlayer phases. Traditional segmentation models face pixel-level accuracy and generalization issues in multimodal data. MSEG-VCUQ introduces VideoSAM, a hybrid framework leveraging convolutional neural networks (CNNs) and transformer-based vision models to enhance segmentation accuracy and generalizability across complex multimodal PD tasks. Methods: VideoSAM combines U-Net CNN and the Segment Anything Model (SAM) for advanced feature extraction and segmentation across diverse HSV PD modalities, spanning fluids like water, FC-72, nitrogen, and argon under varied heat flux conditions. The framework also incorporates uncertainty quantification (UQ) to assess pixel-based discretization errors, delivering reliable metrics such as contact line density and dry area fraction under experimental conditions. Results: VideoSAM outperforms SAM and modality-specific CNN models in segmentation accuracy, excelling in environments with complex phase boundaries, overlapping bubbles, and dynamic liquid-vapor interactions. Its hybrid architecture supports cross-dataset generalization, adapting effectively to varying modalities. The UQ module provides accurate error estimates, enhancing the reliability of segmentation outputs for advanced HSV PD research. Conclusion: MSEG-VCUQ, via VideoSAM, offers a robust solution for HSV PD segmentation, addressing previous limitations with advanced deep learning and UQ techniques. The open-source datasets and tools introduced enable scalable, precise, and adaptable segmentation for multimodal PD datasets, supporting advancements in HSV analysis and autonomous experimentation. The codes and data used for this paper are publicly available at: \url{https://github.com/chikap421/mseg_vcuq}

本研究解决了传统分割模型在多模态数据中面临的像素级精度和泛化问题，提出了一种名为VideoSAM的混合框架，结合了卷积神经网络与基于变换器的视觉模型，提高了复杂高速视频相位检测任务中的分割准确性与泛化能力。研究发现，该框架在复杂的相位边界和动态液气相互作用的环境中表现优异，并为高速视频相位检测提供了更可靠的分割输出及错误估计。

MSEG-VCUQ: Multimodal SEGmentation with Enhanced Vision Foundation
  Models, Convolutional Neural Networks, and Uncertainty Quantification for
  High-Speed Video Phase Detection Data

多模态分割与增强视觉基础模型、卷积神经网络及不确定性量化的高速视频相位检测数据