实现术中高光谱相机自动光照校准，提升手术影像精度与效率
Deep intra-operative illumination calibration of hyperspectral cameras
- 基于学习的方法动态校准术中光照变化，替代繁琐的手动白参考校正
- 在742组样本上验证，校准精度优于已有方法且跨物种、跨光照条件通用
- 适合需高精度实时成像的外科手术场景，尤其适用于多变光照环境
高光谱成像（HSI）在手术应用中前景广阔，但现有设备因需关闭灯光或频繁手动校准而难以融入临床流程。本文揭示手术室光照动态变化会显著影响生理参数估计和手术场景分割。为此，提出一种新型学习驱动的自动校准方法，无需白参考即可实现高精度校准。基于742组来自模拟物、猪模型和大鼠的高光谱立方体数据，验证该方法不仅性能超越先前方案，还能在不同物种、光照条件和图像处理任务间良好泛化。其简单集成、高精度、快速及强泛化能力，使其有望成为临床手术高光谱成像的核心组件。
原文摘要 · Abstract (English)
Hyperspectral imaging (HSI) is emerging as a promising novel imaging modality with various potential surgical applications. Currently available cameras, however, suffer from poor integration into the clinical workflow because they require the lights to be switched off, or the camera to be manually recalibrated as soon as lighting conditions change. Given this critical bottleneck, the contribution of this paper is threefold: (1) We demonstrate that dynamically changing lighting conditions in the operating room dramatically affect the performance of HSI applications, namely physiological parameter estimation, and surgical scene segmentation. (2) We propose a novel learning-based approach to automatically recalibrating hyperspectral images during surgery and show that it is sufficiently accurate to replace the tedious process of white reference-based recalibration. (3) Based on a total of 742 HSI cubes from a phantom, porcine models, and rats we show that our recalibration method not only outperforms previously proposed methods, but also generalizes across species, lighting conditions, and image processing tasks. Due to its simple workflow integration as well as high accuracy, speed, and generalization capabilities, our method could evolve as a central component in clinical surgical HSI.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。