A two-stage approach for fully automatic segmentation of venous vascular structures in liver CT images

2009 ◽  
Author(s):  
Jens N. Kaftan ◽  
Hüseyin Tek ◽  
Til Aach
Keyword(s):  
Automatic Segmentation ◽  
Ct Images ◽  
Two Stage ◽  
Liver Ct ◽  
Vascular Structures ◽  
Fully Automatic

SU-E-J-50: Fully Automatic Segmentation of Brain Tumor in CT Images

2011 ◽  
Vol 38 (6Part8) ◽  
pp. 3453-3453
Author(s):  
M Gao ◽  
D Wei ◽  
S Chen
Keyword(s):  
Brain Tumor ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Fully Automatic

Fully automatic segmentation of liver from multiphase liver CT

2007 ◽  
Author(s):  
Yalin Zheng ◽  
Xiaoyun Yang ◽  
Xujiong Ye ◽  
Xinyu Lin
Keyword(s):  
Automatic Segmentation ◽  
Liver Ct ◽  
Fully Automatic

scholarly journals Segmentation of Lung Ct Images using Cascaded Fully Convolutional Neural Networks

2019 ◽  
Vol 8 (2) ◽  
pp. 5472-5474
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Fully Convolutional Neural Networks ◽  
Proposal A ◽  
Fully Automatic ◽  
Lung Ct

Interpretation of CT Lung images by the radiologist can be enhanced to a greater extent by automatic segmentation of nodules. The efficiency of this interpretation depends on the completeness and non-ambiguousness of the CT Lung images. Here, a fully automatic cascaded basis was proposed for CT Lung image segmentation. In this proposal a customized FCN was used feature extractions exploration from many visual scales and differentiate anatomy with a thick forecast map. Widespread experimental outcomes demonstrate that this technique can address the incompleteness in boundary and this technique can achieve best accuracy in segmentation of Lung CT Images when compared to other techniques which address the same area

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