Three-Dimensional Segmentation Research of CT Abdominal Artery Image Sequence

As the role of medical imaging in clinical diagnoses and treatment has been more and more important, CT scan has been applied more widely. How to extract abdominal artery from CT image automatically and accurately has a significant value for abdominal artery disease. Because the medical image is of complexity and diversity, traditional segmentation method cannot complete the segmentation task very well. Therefore, this paper presents a method for extracting abdominal artery from CT images using three-dimensional region growing algorithm combined with image morphology. The experimental results show that the proposed method is an effective way for improving the accuracy of abdominal artery segmentation.

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A Novel Brain Image Segmentation Method Using an Improved 3D U-Net Model

Scientific Programming ◽

10.1155/2021/4801077 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Zhuqing Yang

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Three Dimensional ◽

Research Field ◽

Medical Image Segmentation ◽

Tumor Segmentation ◽

Segmentation Method ◽

Brain Tumor Segmentation ◽

Segmentation Methods ◽

Three Dimensional Segmentation

Medical image segmentation (IS) is a research field in image processing. Deep learning methods are used to automatically segment organs, tissues, or tumor regions in medical images, which can assist doctors in diagnosing diseases. Since most IS models based on convolutional neural network (CNN) are two-dimensional models, they are not suitable for three-dimensional medical imaging. On the contrary, the three-dimensional segmentation model has problems such as complex network structure and large amount of calculation. Therefore, this study introduces the self-excited compressed dilated convolution (SECDC) module on the basis of the 3D U-Net network and proposes an improved 3D U-Net network model. In the SECDC module, the calculation amount of the model can be reduced by 1 × 1 × 1 convolution. Combining normal convolution and cavity convolution with an expansion rate of 2 can dig out the multiview features of the image. At the same time, the 3D squeeze-and-excitation (3D-SE) module can realize automatic learning of the importance of each layer. The experimental results on the BraTS2019 dataset show that the Dice coefficient and other indicators obtained by the model used in this paper indicate that the overall tumor can reach 0.87, the tumor core can reach 0.84, and the most difficult to segment enhanced tumor can reach 0.80. From the evaluation indicators, it can be analyzed that the improved 3D U-Net model used can greatly reduce the amount of data while achieving better segmentation results, and the model has better robustness. This model can meet the clinical needs of brain tumor segmentation methods.

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Coronary Arteries Segmentation Based on the 3D Discrete Wavelet Transform and 3D Neutrosophic Transform

BioMed Research International ◽

10.1155/2015/798303 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 3

Author(s):

Shuo-Tsung Chen ◽

Tzung-Dau Wang ◽

Wen-Jeng Lee ◽

Tsai-Wei Huang ◽

Pei-Kai Hung ◽

...

Keyword(s):

Coronary Arteries ◽

Medical Image ◽

Region Growing ◽

Ground Truth ◽

Discrete Wavelet ◽

Segmentation Method ◽

3D Dwt ◽

Initial Segmentation ◽

3D Discrete Wavelet Transform ◽

3D Region Growing

Purpose. Most applications in the field of medical image processing require precise estimation. To improve the accuracy of segmentation, this study aimed to propose a novel segmentation method for coronary arteries to allow for the automatic and accurate detection of coronary pathologies.Methods. The proposed segmentation method included 2 parts. First, 3D region growing was applied to give the initial segmentation of coronary arteries. Next, the location of vessel information, HHH subband coefficients of the 3D DWT, was detected by the proposed vessel-texture discrimination algorithm. Based on the initial segmentation, 3D DWT integrated with the 3D neutrosophic transformation could accurately detect the coronary arteries.Results. Each subbranch of the segmented coronary arteries was segmented correctly by the proposed method. The obtained results are compared with those ground truth values obtained from the commercial software from GE Healthcare and the level-set method proposed by Yang et al., 2007. Results indicate that the proposed method is better in terms of efficiency analyzed.Conclusion. Based on the initial segmentation of coronary arteries obtained from 3D region growing, one-level 3D DWT and 3D neutrosophic transformation can be applied to detect coronary pathologies accurately.

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Anatomically Based Geometric Modelling Using Medical Image Data: Methods and Programs

The Open Biomedical Engineering Journal ◽

10.2174/1874120701509010126 ◽

2015 ◽

Vol 9 (1) ◽

pp. 126-131

Author(s):

Monan Wang ◽

Lei Sun ◽

Yuming Liu

Keyword(s):

Geometric Modeling ◽

Medical Image ◽

Structural Information ◽

Geometric Model ◽

Three Dimensional ◽

Image Data ◽

Region Growing ◽

Reconstruction Algorithm ◽

Medical Image Segmentation ◽

Image Browsing

Geometric modeling software that can realize two-dimensional medical image browsing, preprocessing, and three-dimensional (3D) reconstruction is designed for modeling human organs. This software performs medical image segmentation using a method that combines the region growing and the interactive segmentation methods. The Marching Cubes surface reconstruction algorithm is used to obtain a 3D geometric model. The program is compiled using Visual Studio 2010. The software is employed to obtain the geometric model of the human femur, hipbone, and muscle. The geometric modeling results can accurately express the structural information of the skeleton and muscle.

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