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

scholarly journals BE-FNet: 3D Bounding Box Estimation Feature Pyramid Network for Accurate and Efficient Maxillary Sinus Segmentation

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Zhuofu Deng ◽  
Binbin Wang ◽  
Zhiliang Zhu
Keyword(s):  
Neural Networks ◽  
Maxillary Sinus ◽  
Convolutional Neural Networks ◽  
Feature Fusion ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
3D Segmentation ◽  
Deep Convolutional Neural Networks ◽  
Bounding Box ◽  
Proposed Model

Maxillary sinus segmentation plays an important role in the choice of therapeutic strategies for nasal disease and treatment monitoring. Difficulties in traditional approaches deal with extremely heterogeneous intensity caused by lesions, abnormal anatomy structures, and blurring boundaries of cavity. 2D and 3D deep convolutional neural networks have grown popular in medical image segmentation due to utilization of large labeled datasets to learn discriminative features. However, for 3D segmentation in medical images, 2D networks are not competent in extracting more significant spacial features, and 3D ones suffer from unbearable burden of computation, which results in great challenges to maxillary sinus segmentation. In this paper, we propose a deep neural network with an end-to-end manner to generalize a fully automatic 3D segmentation. At first, our proposed model serves a symmetrical encoder-decoder architecture for multitask of bounding box estimation and in-region 3D segmentation, which cannot reduce excessive computation requirements but eliminate false positives remarkably, promoting 3D segmentation applied in 3D convolutional neural networks. In addition, an overestimation strategy is presented to avoid overfitting phenomena in conventional multitask networks. Meanwhile, we introduce residual dense blocks to increase the depth of the proposed network and attention excitation mechanism to improve the performance of bounding box estimation, both of which bring little influence to computation cost. Especially, the structure of multilevel feature fusion in the pyramid network strengthens the ability of identification to global and local discriminative features in foreground and background achieving more advanced segmentation results. At last, to address problems of blurring boundary and class imbalance in medical images, a hybrid loss function is designed for multiple tasks. To illustrate the strength of our proposed model, we evaluated it against the state-of-the-art methods. Our model performed better significantly with an average Dice 0.947±0.031, VOE 10.23±5.29, and ASD 2.86±2.11, respectively, which denotes a promising technique with strong robust in practice.

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 Rigid model-based 3D segmentation of the bones of joints in MR and CT images for motion analysis

2008 ◽  
Vol 35 (8) ◽  
pp. 3637-3649 ◽  
Author(s):  
Jiamin Liu ◽  
Jayaram K. Udupa ◽  
Punam K. Saha ◽  
Dewey Odhner ◽  
Bruce E. Hirsch ◽  
...  
Keyword(s):  
Motion Analysis ◽  
Ct Images ◽  
3D Segmentation ◽  
Model Based ◽  
Rigid Model

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.

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