Madireddy Indeever, Wu Tongge
Computer Science, BASIS Independent Silicon Valley, San Jose, USA.
Department of Mechanical Engineering, University of California, Berkeley, USA.
Cureus. 2022 Jul 25;14(7):e27247. doi: 10.7759/cureus.27247. eCollection 2022 Jul.
Background Image segmentation is a fundamental technique that allows researchers to process images from various sources into individual components for certain applications, such as visual or numerical evaluations. Image segmentation is beneficial when studying medical images for healthcare purposes. However, existing semantic image segmentation models like the U-net are computationally intensive. This work aimed to develop less complicated models that could still accurately segment images. Methodology Rule-based and linear layer neural network models were developed in Mathematica and trained on mouse vertebrae micro-computed tomography scans. These models were tasked with segmenting the cortical shell from the whole bone image. A U-net model was also set up for comparison. Results It was found that the linear layer neural network had comparable accuracy to the U-net model in segmenting the mice vertebrae scans. Conclusions This work provides two separate models that allow for automated segmentation of mouse vertebral scans, which could be potentially valuable in applications such as pre-processing the murine vertebral scans for further evaluations of the effect of drug treatment on bone micro-architecture.
背景
图像分割是一项基础技术，它使研究人员能够将来自各种来源的图像处理成单个组件，以用于某些应用，如视觉或数值评估。在为医疗保健目的研究医学图像时，图像分割是有益的。然而，现有的语义图像分割模型，如U-net，计算量很大。这项工作旨在开发出仍能准确分割图像但复杂度较低的模型。
方法
在Mathematica中开发了基于规则和线性层的神经网络模型，并在小鼠椎骨微型计算机断层扫描上进行训练。这些模型的任务是从整个骨骼图像中分割出皮质骨壳。还建立了一个U-net模型用于比较。
结果
发现在分割小鼠椎骨扫描图像时，线性层神经网络与U-net模型具有相当的准确性。
结论
这项工作提供了两个独立的模型，可对小鼠椎骨扫描进行自动分割，这在诸如预处理小鼠椎骨扫描以进一步评估药物治疗对骨微结构的影响等应用中可能具有潜在价值。
