Deep 3D attention U-Net based whole liver segmentation for anatomical volume analysis in abdominal CT images

2021 ◽  
Author(s):  
JinGyo Jeong ◽  
Young Jae Kim ◽  
Kwang Gi Kim ◽  
Won Suk Lee
Keyword(s):  
Ct Images ◽  
Liver Segmentation ◽  
Abdominal Ct ◽  
Volume Analysis ◽  
3D Attention

scholarly journals Liver Segmentation Based on Snakes Model and Improved GrowCut Algorithm in Abdominal CT Image

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Huiyan Jiang ◽  
Baochun He ◽  
Zhiyuan Ma ◽  
Mao Zong ◽  
Xiangrong Zhou ◽  
...  
Keyword(s):  
Ct Images ◽  
Experimental Results ◽  
Initial Contour ◽  
Segmentation Result ◽  
Ct Image ◽  
Liver Segmentation ◽  
Abdominal Ct ◽  
Running Time ◽  
Novel Method ◽  
Liver Region

A novel method based on Snakes Model and GrowCut algorithm is proposed to segment liver region in abdominal CT images. First, according to the traditional GrowCut method, a pretreatment process using K-means algorithm is conducted to reduce the running time. Then, the segmentation result of our improved GrowCut approach is used as an initial contour for the future precise segmentation based on Snakes model. At last, several experiments are carried out to demonstrate the performance of our proposed approach and some comparisons are conducted between the traditional GrowCut algorithm. Experimental results show that the improved approach not only has a better robustness and precision but also is more efficient than the traditional GrowCut method.

scholarly journals Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Weiwei Wu ◽  
Zhuhuang Zhou ◽  
Shuicai Wu ◽  
Yanhua Zhang
Keyword(s):  
Liver Volume ◽  
Maximum Intensity Projection ◽  
Liver Slice ◽  
Graph Cuts ◽  
Ct Images ◽  
Patient Specific ◽  
Computer Assisted ◽  
Morphological Operations ◽  
Liver Segmentation ◽  
Abdominal Ct

Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despite many years of research, automatic liver segmentation remains a challenging task. In this paper, a novel method was proposed for automatic delineation of liver on CT volume images using supervoxel-based graph cuts. To extract the liver volume of interest (VOI), the region of abdomen was firstly determined based on maximum intensity projection (MIP) and thresholding methods. Then, the patient-specific liver VOI was extracted from the region of abdomen by using a histogram-based adaptive thresholding method and morphological operations. The supervoxels of the liver VOI were generated using the simple linear iterative clustering (SLIC) method. The foreground/background seeds for graph cuts were generated on the largest liver slice, and the graph cuts algorithm was applied to the VOI supervoxels. Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm. Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases.

scholarly journals Liver segmentation in abdominal CT images via auto-context neural network and self-supervised contour attention

2021 ◽  
Vol 113 ◽  
pp. 102023
Author(s):  
Minyoung Chung ◽  
Jingyu Lee ◽  
Sanguk Park ◽  
Chae Eun Lee ◽  
Jeongjin Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Ct Images ◽  
Liver Segmentation ◽  
Abdominal Ct
Sign in / Sign up

Export Citation Format

Share Document