Oriented Boundary Graph: A Framework to Design and Implement 3D Segmentation Algorithms

2010 ◽  
Author(s):  
Fabien Baldacci ◽  
Achille Braquelaire ◽  
Jean-Philippe Domenger
Keyword(s):  
3D Segmentation ◽  
Segmentation Algorithms ◽  
Boundary Graph

scholarly journals Geodesic Distance Algorithm for Extracting the Ascending Aorta from 3D CT Images

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Yeonggul Jang ◽  
Ho Yub Jung ◽  
Youngtaek Hong ◽  
Iksung Cho ◽  
Hackjoon Shim ◽  
...  
Keyword(s):  
Geodesic Distance ◽  
Ascending Aorta ◽  
Dice Similarity Coefficient ◽  
3D Segmentation ◽  
Medical Expert ◽  
Distance Transformation ◽  
Segmentation Algorithms ◽  
Initial Seed ◽  
Seed Points ◽  
Axial Slice

This paper presents a method for the automatic 3D segmentation of the ascending aorta from coronary computed tomography angiography (CCTA). The segmentation is performed in three steps. First, the initial seed points are selected by minimizing a newly proposed energy function across the Hough circles. Second, the ascending aorta is segmented by geodesic distance transformation. Third, the seed points are effectively transferred through the next axial slice by a novel transfer function. Experiments are performed using a database composed of 10 patients’ CCTA images. For the experiment, the ground truths are annotated manually on the axial image slices by a medical expert. A comparative evaluation with state-of-the-art commercial aorta segmentation algorithms shows that our approach is computationally more efficient and accurate under the DSC (Dice Similarity Coefficient) measurements.

scholarly journals Gebiss: an ImageJ plugin for the specification of ground truth and the performance evaluation of 3D segmentation algorithms

2011 ◽  
Vol 12 (1) ◽  
Author(s):  
Janos Kriston-Vizi ◽  
Ng Wee Thong ◽  
Cheok Leong Poh ◽  
Kwo Chia Yee ◽  
Joan Sim Poh Ling ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Ground Truth ◽  
3D Segmentation ◽  
Segmentation Algorithms ◽  
Imagej Plugin

Integrating Improved U-Net and Continuous Maximum Flow Algorithm for 3D Brain Tumor Image Segmentation

2020 ◽  
Vol 64 (4) ◽  
pp. 40412-1-40412-11
Author(s):  
Kexin Bai ◽  
Qiang Li ◽  
Ching-Hsin Wang
Keyword(s):  
Brain Tumor ◽  
Data Augmentation ◽  
A Priori ◽  
Class Imbalance ◽  
Maximum Flow ◽  
Magnetic Resonance Images ◽  
Tumor Segmentation ◽  
Similarity Coefficients ◽  
Segmentation Algorithms ◽  
Flow Algorithm

Abstract To address the issues of the relatively small size of brain tumor image datasets, severe class imbalance, and low precision in existing segmentation algorithms for brain tumor images, this study proposes a two-stage segmentation algorithm integrating convolutional neural networks (CNNs) and conventional methods. Four modalities of the original magnetic resonance images were first preprocessed separately. Next, preliminary segmentation was performed using an improved U-Net CNN containing deep monitoring, residual structures, dense connection structures, and dense skip connections. The authors adopted a multiclass Dice loss function to deal with class imbalance and successfully prevented overfitting using data augmentation. The preliminary segmentation results subsequently served as the a priori knowledge for a continuous maximum flow algorithm for fine segmentation of target edges. Experiments revealed that the mean Dice similarity coefficients of the proposed algorithm in whole tumor, tumor core, and enhancing tumor segmentation were 0.9072, 0.8578, and 0.7837, respectively. The proposed algorithm presents higher accuracy and better stability in comparison with some of the more advanced segmentation algorithms for brain tumor images.

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