Department of Computer Science, Western Michigan University, Kalamazoo, MI 49008, USA.
Faculty Engineering and Technology, Multimedia University, Melaka 75450, Malaysia.
Sensors (Basel). 2023 Feb 27;23(5):2640. doi: 10.3390/s23052640.
As a fundamental but difficult topic in computer vision, 3D object segmentation has various applications in medical image analysis, autonomous vehicles, robotics, virtual reality, lithium battery image analysis, etc. In the past, 3D segmentation was performed using hand-made features and design techniques, but these techniques could not generalize to vast amounts of data or reach acceptable accuracy. Deep learning techniques have lately emerged as the preferred method for 3D segmentation jobs as a result of their extraordinary performance in 2D computer vision. Our proposed method used a CNN-based architecture called 3D UNET, which is inspired by the famous 2D UNET that has been used to segment volumetric image data. To see the internal changes of composite materials, for instance, in a lithium battery image, it is necessary to see the flow of different materials and follow the directions analyzing the inside properties. In this paper, a combination of 3D UNET and VGG19 has been used to conduct a multiclass segmentation of publicly available sandstone datasets to analyze their microstructures using image data based on four different objects in the samples of volumetric data. In our image sample, there are a total of 448 2D images, which are then aggregated as one 3D volume to examine the 3D volumetric data. The solution involves the segmentation of each object in the volume data and further analysis of each object to find its average size, area percentage, total area, etc. The open-source image processing package IMAGEJ is used for further analysis of individual particles. In this study, it was demonstrated that convolutional neural networks can be trained to recognize sandstone microstructure traits with an accuracy of 96.78% and an IOU of 91.12%. According to our knowledge, many prior works have applied 3D UNET for segmentation, but very few papers extend it further to show the details of particles in the sample. The proposed solution offers a computational insight for real-time implementation and is discovered to be superior to the current state-of-the-art methods. The result has importance for the creation of an approximately similar model for the microstructural analysis of volumetric data.
作为计算机视觉中的一个基本但困难的课题，3D 物体分割在医学图像分析、自动驾驶汽车、机器人技术、虚拟现实、锂电池图像分析等领域有广泛的应用。过去，3D 分割是使用手工制作的特征和设计技术完成的，但这些技术无法推广到大量数据或达到可接受的精度。由于在 2D 计算机视觉方面的出色表现，深度学习技术最近成为 3D 分割工作的首选方法。我们提出的方法使用了一种基于卷积神经网络的架构，称为 3D UNET，它是受著名的 2D UNET 的启发，2D UNET 已被用于分割体积图像数据。例如，为了观察锂电池图像中复合材料的内部变化，有必要观察不同材料的流动并沿着分析内部特性的方向进行跟踪。在本文中，使用了一种称为 3D UNET 的基于卷积神经网络的架构，并结合 VGG19 对公开的砂岩数据集进行了多类分割，以使用基于体积数据中四个不同对象的图像数据来分析它们的微观结构。在我们的图像样本中，共有 448 张 2D 图像，然后将它们聚合为一个 3D 体，以检查 3D 体积数据。该解决方案涉及对体积数据中的每个对象进行分割，并进一步分析每个对象以找到其平均大小、面积百分比、总面积等。使用开源图像处理包 IMAGEJ 对各个颗粒进行进一步分析。在这项研究中，证明了卷积神经网络可以被训练来识别砂岩微观结构特征，准确率为 96.78%，IOU 为 91.12%。据我们所知，许多先前的工作已经应用 3D UNET 进行分割，但很少有论文进一步扩展它以显示样本中颗粒的细节。所提出的解决方案为实时实现提供了计算洞察力，并被发现优于当前的最先进方法。该结果对于创建一个类似的近似模型来进行体积数据的微观结构分析具有重要意义。
