改进网络模型的局部骨切片图像重构

基于骨切片图像的三维骨多孔结构数字建模是骨组织工程的技术基础,也是生物医学工程领域的研究热点,骨切片图像的质量决定了骨多孔结构数字模型的准确程度。然而,骨切片图像在获取过程中会出现数据丢失、图像受损或图像尺寸过小等问题,导致只能得到局部的切片图像。为了解决这个问题,提出对局部骨切片图像进行完整重构修复的改进条件生成对抗网络,即在条件生成对抗网络基础上,对生成器进行改进,加入嵌套残差密集块,同时,在判别器中加入极化自注意力模块,并对重构的图像进行形态学函数分析和局部孔隙率分布研究来评价生成图像与真实骨多孔图像的相似程度。结果表明该网络能准确、稳定地重构出多样化的完整骨多孔切片图像。

Reconstruction of Partial Slice Bone Images Based on Improved Network Model

Digital modeling of 3D bone porous structure based on bone slice images is the technical basis of bone tissue engineering and a research hotspot in the field of biomedical engineering. The quality of bone slice image determines the accuracy of the digital model of bone porous structure. However, problems such as data loss, image damage or too small image size may occur in the process of acquiring bone section images, resulting in only partial slice images, and thus such incomplete image information seriously affects the accuracy of 3D porous bone structure modeling. In order to solve this problem, an improved conditional generative adversarial network for complete reconstruction and repair of partial bone slice images was proposed, that is, on the basis of conditional generative adversarial network, the nested residual dense block is added to the generator, and the polarized self-attention module is added to the discriminator. Morphological function analysis and partial porosity distribution study were performed to evaluate the similarity between the reconstructed images and the real bone porous images. The results show that the network can accurately and stably reconstruct complete bone porous slice images.