Automated Segmentation and Anatomical Labeling of Abdominal Arteries Based on Multi-organ Segmentation from Contrast-Enhanced CT Data

  title={Automated Segmentation and Anatomical Labeling of Abdominal Arteries Based on Multi-organ Segmentation from Contrast-Enhanced CT Data},
  author={Yuki Suzuki and Toshiyuki Okada and Masatoshi Hori and Futoshi Yokota and Marius George Linguraru and Noriyuki Tomiyama and Yoshinobu Sato},
A fully automated method is described for segmentation and anatomical labeling of the abdominal arteries from contrast-enhanced CT data of the upper abdomen. By assuming that the regions of the organs and aorta have already been automatically segmented, the problem is formulated as extracting and selecting the optimal paths between the organ and aorta regions based on a basic anatomical constraint that arteries supplying blood to an organ consist of tree structures whose root nodes are located… 
3 Citations
Deep multi-scale feature fusion for pancreas segmentation from CT images
A multi-scale feature fusion model for accurate pancreas segmentation from CT images that outperforms the current state-of-the-art methods and confirms that the incorporation of squeeze-and-excitation and hierarchical fusion modules contributes to a net gain in the performance of this model.
Deep Neural Network for Pancreas Segmentation from CT Images
The proposed Hierarchical Convolutional Neural Network to fuse multi-scale features, which could remedy the lost image details in progressive convolutional and pooling layers, outperforms the current state-of-art methods.


Abdominal Multi-Organ Segmentation of CT Images Based on Hierarchical Spatial Modeling of Organ Interrelations
A method for finding and representing the interrelations based on canonical correlation analysis is proposed and methods are developed for constructing and utilizing the statistical atlas in which inter-organ constraints are explicitly incorporated to improve accuracy of multi-organ segmentation.
Segmentation of multiple organs in non-contrast 3D abdominal CT images
The proposed simultaneous extraction method using an abdominal cavity standardization process and atlas guided segmentation incorporating parameter estimation with the EM algorithm was statistically proved to have better performance in the segmentation of 3D CT volumes.
Automated anatomical labeling of the bronchial branch and its application to the virtual bronchoscopy system
A method for the automated anatomical labeling of the bronchial branch extracted from a three-dimensional chest X-ray CT image and its application to a virtual bronchoscopy system (VBS) and the result showed that the method could segment about 57% of the branches from CT images and extracted a tree structure of about 91% in branches in the segmented bronchus.
Automated anatomical labeling of abdominal arteries from CT data based on optimal path finding between segmented organ and aorta regions : A robust method against topological variability
A robust method against topological variability is proposed to find path finding between segmented organ and aorta regions and it is shown that this method outperforms previous methods on similar subjects by a large margin.
Construction of Hierarchical Multi-Organ Statistical Atlases and Their Application to Multi-Organ Segmentation from CT Images
The experimental results show that segmentation accuracy of the liver was improved by incorporating constraints on inter-organ relationships.
Anatomical Labeling of the Anterior Circulation of the Circle of Willis Using Maximum a Posteriori Classification
The proposed method for anatomical labeling of vessels forming anterior part of the Circle of Willis by detecting the five main vessel bifurcations can naturally handle anatomical variations and is shown to be suitable for labeling arterial segments ofcircle of Willis.
Automated Nomenclature of Upper Abdominal Arteries for Displaying Anatomical Names on Virtual Laparoscopic Images
This paper presents a method for automated nomenclature of abdominal arteries that are extracted from 3D CT images based on the combination optimization approach for the displaying anatomical names
Non-local Shape Descriptor: A New Similarity Metric for Deformable Multi-modal Registration
A new similarity metric for multi-modal registration, the non-local shape descriptor, that is robust against the most considerable differences between modalities, such as non-functional intensity relations, different amounts of noise and non-uniform bias fields is proposed.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010, 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part III
This work proposes an adaptive version of the Linear Minimum Mean Square Error estimator that applies an adaptive filtering kernel that is based on a space-variant estimate of the noise level and a weight consisting of the product of a Gaussian kernel and the diffusion similarity with respect to the central voxel.