改进UNet++的杉木CT图像年轮分割

doi:10.3778/j.issn.1002-8331.2210-0212

摘要/Abstract

摘要： 为解决裂纹、虫孔和节子等缺陷影响下的年轮准确分割问题。以医疗CT为实验设备，重构125张杉木横切面CT图像为研究对象，经裁切、旋转、翻转等预处理实现数据扩充，提出改进UNet++模型用于年轮分割。改进UNet++模型采用增加卷积块、下采样层、跳跃连接和上采样层的方式，将学习深度增加至6层，以BCEWithLogitsLoss和ReLU分别作为损失函数和激活函数，RMSProp作为优化函数，对杉木横切面CT图像进行年轮分割，并对年轮分割性能进行评价。结果显示：改进UNet++模型对于杉木横切面CT图像的年轮分割的像素准确率为97.81%，骰子系数为98.89%，交并比为95.29%，平均交并比为84.75%，充分提取杉木年轮特征，分割效果最好。与U-Net模型和UNet++模型相比，改进UNet++模型在多数年轮被裂纹和虫孔切割，无法形成完整圆形闭合曲线的条件下，使分割的年轮具有很好的完整性和连续性，消除分割过程中的断裂和噪声现象；年轮分割结果不受裂纹、节子、虫孔等缺陷影响，结构非常清晰，有效解决多种缺陷干扰下的虫孔误分割和密集年轮欠分割等问题。

关键词: 杉木, 横切面, 年轮分割, CT图像, UNet++模型

Abstract: In order to solve the problem that it is difficult to accurately segment tree rings with defects such as cracks, wormholes and knots. The medical CT is used as experimental equipment to reconstruct 125 CT images of Chinese fir transverse sections, and these images are used as the data set. Data set is expanded by pre-processing such as cutting, rotating and flipping CT images. An improved UNet++ model is proposed for tree rings segmentation. Convolutional blocks, downsampling layers, skip connections and upsampling layers have been added to the improved UNet++ model, and the learning depth is increased to 6 layers. The BCEWithLogitsLoss, ReLU and RMSProp are used as loss function, activation function and optimization function respectively. The improved UNet++ model is used to segment the tree rings of the transverse sections of Chinese fir reconstructed by CT, and the performance of the model is evaluated. The results show that the pixel accuracy of the improved UNet++ model is 97.81%, the dice coefficient is 98.89%, the intersection over union is 95.29%, and the mean intersection over union is 84.75%. The best segmentation result is obtained by fully extracting the characteristics in Chinese fir tree rings. Compared with the U-Net model and the UNet++ model, the improved UNet++ model makes the segmented tree rings complete and continuous, although most tree rings are cut by cracks and wormholes and cannot form a complete circular closed curve, fracture and noise are eliminated. The results show that the improved UNet++ model is not affected by defects such as cracks, knots and wormholes, and the segmentation results are very clear, which effectively solves mis-segmentation and under segmentation of dense tree rings under the interference of wormhole defects.

Key words: Chinese fir, transverse section, tree rings segmentation, CT image, UNet++ model

刘帅, 葛浙东, 刘晓彤, 高宜生, 李阳, 李萌菲. 改进UNet++的杉木CT图像年轮分割[J]. 计算机工程与应用, 2024, 60(5): 232-239.

LIU Shuai, GE Zhedong, LIU Xiaotong, GAO Yisheng, LI Yang, LI Mengfei. Improved UNet++ for Tree Rings Segmentation of Chinese Fir CT Images[J]. Computer Engineering and Applications, 2024, 60(5): 232-239.

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