# MSEG-VCUQ: Multimodal SEGmentation with Enhanced Vision Foundation Models, Convolutional Neural Networks, and Uncertainty Quantification for High-Speed Video Phase Detection Data Source: [MSEG-VCUQ: Multimodal SEGmentation with Enhanced Vision Foundation Models, Convolutional Neural Networks, and Uncertainty Quantification for High-Speed Video Phase Detection Data]() Collection index: [llms.txt]() Data updated: 2026-03-18T05:00:04.466Z Authors: Chika Maduabuchi, Ericmoore Jossou, Matteo Bucci Published: 2024-11-12T00:54:26.000Z ## Abstract High-speed video (HSV) phase detection (PD) segmentation is crucial for monitoring vapor, liquid, and microlayer phases in industrial processes. While CNN-based models like U-Net have shown success in simplified shadowgraphy-based two-phase flow (TPF) analysis, their application to complex HSV PD tasks remains unexplored, and vision foundation models (VFMs) have yet to address the complexities of either shadowgraphy-based or PD TPF video segmentation. Existing uncertainty quantification (UQ) methods lack pixel-level reliability for critical metrics like contact line density and dry area fraction, and the absence of large-scale, multimodal experimental datasets tailored to PD segmentation further impedes progress. To address these gaps, we propose MSEG-VCUQ. This hybrid framework integrates U-Net CNNs with the transformer-based Segment Anything Model (SAM) to achieve enhanced segmentation accuracy and cross-modality generalization. Our approach incorporates systematic UQ for robust error assessment and introduces the first open-source multimodal HSV PD datasets. Empirical results demonstrate that MSEG-VCUQ outperforms baseline CNNs and VFMs, enabling scalable and reliable PD segmentation for real-world boiling dynamics. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Multimodal Segmentation on FC-72 modality dataset](): Multimodal Segmentation; FC-72 modality dataset; IoU: 79.97. - [Multimodal Segmentation on Nitrogen modality dataset](): Multimodal Segmentation; Nitrogen modality dataset; IoU: 83.17. - [Multimodal Segmentation on Argon modality dataset](): Multimodal Segmentation; Argon modality dataset; mIoU: 83.84. - [Multimodal Segmentation on Water modality dataset](): Multimodal Segmentation; Water modality dataset; IoU: 18.94. ## Original sources and implementation [Original paper]() [PDF]()