通过结合隐马尔可夫随机场和U-Net实现聚氨酯结构微CT扫描的无监督分割
Unsupervised Segmentation of Micro-CT Scans of Polyurethane Structures By Combining Hidden-Markov-Random Fields and a U-Net
AI总结：
本文提出结合隐马尔可夫随机场和U-Net的无监督分割方法，用于聚氨酯结构微CT图像的高精度分割，无需真实数据并减少对标记数据的依赖。
摘要：
从图像中提取数字材料表示是进行材料性质定量分析的必要前提。过去已对不同的分割方法进行了广泛研究以实现此任务，但这些方法往往缺乏准确性和速度。随着机器学习的出现，监督卷积神经网络（CNNs）在不同分割任务中取得了最先进的性能。然而，这些模型通常以监督方式训练，需要大量标记数据。无监督方法不需要真实数据进行学习，但存在分割时间长和分割准确性差的问题。隐马尔可夫随机场（HMRF）是一种无监督分割方法，它结合了邻域和类分布的概念。我们提出了一种方法，将HMRF理论和CNN分割相结合，利用两个领域的优势：无监督学习和快速分割时间。我们研究了不同邻域项和组件对无监督HMRF损失的贡献。我们证明了HMRF-UNet能够在不使用真实数据的情况下对聚氨酯（PU）泡沫结构的微CT（μCT）图像数据集实现高分割准确性。最后，我们提出并展示了预训练策略，该策略在训练分割模型时显著减少了所需的真实数据量。
英文摘要：
Extracting digital material representations from images is a necessary prerequisite for a quantitative analysis of material properties. Different segmentation approaches have been extensively studied in the past to achieve this task, but were often lacking accuracy or speed. With the advent of machine learning, supervised convolutional neural networks (CNNs) have achieved state-of-the-art performance for different segmentation tasks. However, these models are often trained in a supervised manner, which requires large labeled datasets. Unsupervised approaches do not require ground-truth data for learning, but suffer from long segmentation times and often worse segmentation accuracy. Hidden Markov Random Fields (HMRF) are an unsupervised segmentation approach that incorporates concepts of neighborhood and class distributions. We present a method that integrates HMRF theory and CNN segmentation, leveraging the advantages of both areas: unsupervised learning and fast segmentation times. We investigate the contribution of different neighborhood terms and components for the unsupervised HMRF loss. We demonstrate that the HMRF-UNet enables high segmentation accuracy without ground truth on a Micro-Computed Tomography ($μ$CT) image dataset of Polyurethane (PU) foam structures. Finally, we propose and demonstrate a pre-training strategy that considerably reduces the required amount of ground-truth data when training a segmentation model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
