摘要
在医学图像分割领域,下肢骨CT影像的噪声、伪影、对比度低等问题对图像分割的性能和效果提出了更高的要求。针对这一需求场景,提出了基于U-Net卷积神经网络模型,结合三维CT影像输入数据的特点,对分割算法进行针对性改进的图像分割模型,提高了分割的准确度。文中生成的模型基于U-Net卷积神经网络,通过多层卷积池化聚合,结合注意力机制和连续切片间的特征融合,充分挖掘影像中的特征和结构信息,实现了端到端的影像分割方法。基于积水潭医院下肢骨CT影像数据集进行验证,实验结果表明,该模型的平均交并比达到了84.959%,而其他模型的对应数值分别为78.604%(U-Net),80.481%(Nested U-Net),79.877%(Attention U-net),相比其他模型有显著的提高。
There are higher demands for the performance and effectiveness of segmentation algorithms in the domain of medical image segmentation,due to disturbances suchas noise,artifacts,and low contrast in lower limb bone CT images.In response to this demand,a tailored improvement of the image segmentation model based on the U-Net convolutional neural network model and the characteristics of three-dimensional CT image input data is proposed,improving the accuracy of segmentation.The proposed model,which is based on the U-Net module,is employing multiple layers of convolutional pooling aggregation,combined with attention mechanisms and feature fusion between consecutive slices.This approach can fully explore the features and structural information in the image,achieving an end-to-end image segmentation method.The paper validates the model using a dataset of lower limb bone CT images from Xishan Hospital.Experimental results demonstrate that the average intersection over union(IoU)of the proposed model reaches 84.959%,while the corresponding value of other models is 78.604%(U-Net),80.481%(Nested U-Net),and 79.877%(Attention U-Net),respectively.The proposed model shows significant improvements compared to other models.
作者
石辛诚
王宝会
于利韬
杜辉
SHI Xincheng;WANG Baohui;YU Litao;DU Hui(School of Software,Beihang University,Beijing 100191,China)
出处
《计算机科学》
北大核心
2025年第S1期76-84,共9页
Computer Science
关键词
卷积神经网络
图像分割
U-Net
医学影像处理
特征融合
注意力机制
Convolutional neural network
Image segmentation
U-Net
Medical image processing
Feature fusion
Attention mecha-nisms
