11/5/2023 0 Comments Multiview inpaint downloadOne of the advantages of deep learning over traditional mathematical and statistical methods is that it is relatively simple to configure the relationship between the input and the output spaces in various situations. This is mainly because the improvement in computational power makes it possible to deal with large data sets based on highly manipulated neural network structures, thereby allowing them to solve complex problems. Numerous deep learning-based approaches have been proposed in recent years to solve a variety of computer vision problems, with significant results outperforming conventional algorithms 9, 10, 11, 12, 13, 14. This may hinder the use of advanced neuroimage analysis techniques such as voxel-based morphometry and cortical thickness measurement for clinical data evaluation. However, 3D MRI sequences require longer scan times and larger data storage, so 2D images (multiple 2D slices with substantial gaps between them) are commonly acquired for diagnostic purposes in routine clinical practice. Moreover, 3D MR images are useful for accurate anatomical localization and quantification of co-registered functional images such as those obtained using positron emission tomography or single-photon emission tomography 7, 8. The detailed anatomical information of the brain provided by T1-weighted magnetic resonance (MR) imaging with high-resolution three-dimensional (3D) sequences enables a variety of brain imaging studies, including quantitative measurements of brain tissue volume and cortical thickness, and classification of images for early diagnosis of disease 1, 2, 3, 4, 5, 6. The proposed method will be useful for utilizing advanced neuroimaging techniques with 2D MRI data. In voxel-based analysis to assess gray matter volume and cortical thickness, differences between the inpainted data and the original 3D data were observed only in small clusters. Brain anatomy details were efficiently recovered by the proposed neural network. The diagnostic ability using the inpainted data was also evaluated by investigating the pattern of morphological changes in disease groups. In addition, morphological analyzes were performed to investigate whether the inpainted data produced local features similar to the original 3D data. Various methods were used to assess the overall similarity between the inpainted and original 3D data. To train the network, not only fidelity loss but also perceptual loss based on the VGG network were considered. The purpose of this study is to generate 3D images from a sparsely sampled 2D images using an inpainting deep neural network that has a U-net-like structure and DenseNet sub-blocks. However, due to the long scan time for 3D MR images, 2D images are mainly obtained in clinical environments. The detailed anatomical information of the brain provided by 3D magnetic resonance imaging (MRI) enables various neuroscience research.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |