Model-Based Interpretation of 3D Medical Images

1993 ◽  
Author(s):  
A. Hill ◽  
A. Thornham ◽  
C. J. Taylor
Keyword(s):  
Medical Images ◽  
Model Based

Model-based deformable surface finding for medical images

1996 ◽  
Vol 15 (5) ◽  
pp. 720-731 ◽  
Author(s):  
L.H. Staib ◽  
J.S. Duncan
Keyword(s):  
Medical Images ◽  
Model Based ◽  
Deformable Surface

scholarly journals A Transfer Deep Generative Adversarial Network Model to Synthetic Brain CT Generation from MR Images

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yi Gu ◽  
Qiankun Zheng
Keyword(s):  
Transfer Learning ◽  
Network Model ◽  
Medical Images ◽  
Image Method ◽  
Data Sets ◽  
Generation Process ◽  
Image Generation ◽  
Generative Adversarial Network ◽  
Model Based ◽  
Adversarial Network

Background. The generation of medical images is to convert the existing medical images into one or more required medical images to reduce the time required for sample diagnosis and the radiation to the human body from multiple medical images taken. Therefore, the research on the generation of medical images has important clinical significance. At present, there are many methods in this field. For example, in the image generation process based on the fuzzy C-means (FCM) clustering method, due to the unique clustering idea of FCM, the images generated by this method are uncertain of the attribution of certain organizations. This will cause the details of the image to be unclear, and the resulting image quality is not high. With the development of the generative adversarial network (GAN) model, many improved methods based on the deep GAN model were born. Pix2Pix is a GAN model based on UNet. The core idea of this method is to use paired two types of medical images for deep neural network fitting, thereby generating high-quality images. The disadvantage is that the requirements for data are very strict, and the two types of medical images must be paired one by one. DualGAN model is a network model based on transfer learning. The model cuts the 3D image into multiple 2D slices, simulates each slice, and merges the generated results. The disadvantage is that every time an image is generated, bar-shaped “shadows” will be generated in the three-dimensional image. Method/Material. To solve the above problems and ensure the quality of image generation, this paper proposes a Dual3D&PatchGAN model based on transfer learning. Since Dual3D&PatchGAN is set based on transfer learning, there is no need for one-to-one paired data sets, only two types of medical image data sets are needed, which has important practical significance for applications. This model can eliminate the bar-shaped “shadows” produced by DualGAN’s generated images and can also perform two-way conversion of the two types of images. Results. From the multiple evaluation indicators of the experimental results, it can be analyzed that Dual3D&PatchGAN is more suitable for the generation of medical images than other models, and its generation effect is better.

Deformation simulations by the subject-specific tongue mechanical model based on medical images

2019 ◽  
Vol 2019.30 (0) ◽  
pp. 1C13
Author(s):  
Shun TAKENAKA ◽  
Tsukasa YOSHINAGA ◽  
Kazunori NOZAKI ◽  
Satoshi II ◽  
Shigeo WADA
Keyword(s):  
Mechanical Model ◽  
Medical Images ◽  
Model Based ◽  
Subject Specific ◽  
The Subject

312 Numerical analysis of the bolus flow from the mouth, to the throat, and to the esophagus by means of 3 D swallowing model based on medical images of a real human

2013 ◽  
Vol 2013.26 (0) ◽  
pp. _312-1_-_312-3_
Author(s):  
Yukihiro MICHIWAKI ◽  
Takahiro KIKUCHI ◽  
Mitsuhiro SONOMURA ◽  
Tetsu KAMIYA ◽  
Yoshio TOYAMA ◽  
...  
Keyword(s):  
Numerical Analysis ◽  
Medical Images ◽  
Model Based

Model-based 3D segmentation of the bones of joints in medical images

2005 ◽  
Author(s):  
Jiamin Liu ◽  
Jayaram K. Udupa ◽  
Punam K. Saha ◽  
Dewey Odhner ◽  
Bruce E. Hirsch ◽  
...  
Keyword(s):  
Medical Images ◽  
3D Segmentation ◽  
Model Based
Sign in / Sign up

Export Citation Format

Share Document