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.

Multi-organ segmentation from 3D abdominal CT images using patient-specific weighted-probabilistic atlas

2013 ◽  
Author(s):  
Chengwen Chu ◽  
Masahiro Oda ◽  
Takayuki Kitasaka ◽  
Kazunari Misawa ◽  
Michitaka Fujiwara ◽  
...  
Keyword(s):  
Ct Images ◽  
Patient Specific ◽  
Abdominal Ct ◽  
Probabilistic Atlas

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.

Pancreas segmentation from 3D abdominal CT images using patient-specific weighted subspatial probabilistic atlases

2015 ◽  
Author(s):  
Kenichi Karasawa ◽  
Masahiro Oda ◽  
Yuichiro Hayashi ◽  
Yukitaka Nimura ◽  
Takayuki Kitasaka ◽  
...  
Keyword(s):  
Ct Images ◽  
Patient Specific ◽  
Abdominal Ct ◽  
Pancreas Segmentation

Fully automatic liver segmentation in CT images using modified graph cuts and feature detection

2018 ◽  
Vol 95 ◽  
pp. 198-208 ◽  
Author(s):  
Qing Huang ◽  
Hui Ding ◽  
Xiaodong Wang ◽  
Guangzhi Wang
Keyword(s):  
Feature Detection ◽  
Graph Cuts ◽  
Ct Images ◽  
Liver Segmentation ◽  
Fully Automatic

scholarly journals Liver 3D modeling and hepatectomy simulation for the residents’ preoperative education

2015 ◽  
Author(s):  
Apollon Zygomalas ◽  
Vasileios Megalooikonomou ◽  
Dimitrios Koutsouris ◽  
Dimitrios Karavias ◽  
Ioannis Karagiannidis ◽  
...  
Keyword(s):  
Liver Resection ◽  
3D Modeling ◽  
Preoperative Planning ◽  
Medical Images ◽  
Patient Specific ◽  
Computer Assisted ◽  
Liver Imaging ◽  
Liver Segmentation ◽  
Preoperative Education ◽  
Liver Remnant

Background. Liver segmentation from medical images produces high quality patient specific 3D liver models which are used for preoperative planning and intraoperative guidance. These 3D models can be manipulated and visualized in various ways and can be useful for residents’ education. Objective. The aim of this study was to evaluate the implementation of a novel liver segmentation and hepatectomy simulation application as a tool for the residents’ preoperative education. Method. We developed in MATLAB® 2013a a liver segmentation and preoperative planning application. Ten liver imaging datasets of a prospectively selected random sample of patients undergoing elective hepatectomies at our institution were used for liver segmentation and 3D modeling. Residents were asked to identify anatomical and pathological structures and propose liver resection plans. Intraoperatively, they could consult the computer models in real time. Their surgical scenarios were evaluated and discussed with specialized liver surgeons. Learning objectives were defined and their accomplishment was evaluated using the Kirkpatrick’s four levels model. Results. The residents learned to 1) identify anatomical and pathological structures 2) calculate future liver remnant volume (FLR) from segmented liver images 3) propose liver resection plans based on FLR and liver vascular tree and tumor relations 4) consult liver medical images (CT and MRI) 5) understand the role of computer assisted surgery. They evaluated in-vivo their preoperative planning decisions and understood better the surgical operations. Conclusions. Our proposed liver segmentation and hepatectomy simulation application appears to be appropriate for the preoperative education of resident surgeons.

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
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