scholarly journals Studies on x-ray physical analysis and computer-aided diagnosis of x-ray CT images [in Japanese]

1999 ◽  
Vol 26 (7) ◽  
pp. 1405-1405
Author(s):  
Masatoshi Tsuzaka
Keyword(s):  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Physical Analysis ◽  
X Ray ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Computer aided diagnosis for pulmonary nodules by extracting new shape features from X-ray CT images

2008 ◽  
Vol 20 (1) ◽  
pp. 108-116 ◽  
Author(s):  
Kazunori TAKEI ◽  
Noriyasu HOMMA ◽  
Tadashi ISHIBASHI ◽  
Masao SAKAI ◽  
Takakuni GOTO ◽  
...  
Keyword(s):  
Pulmonary Nodules ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Shape Features ◽  
X Ray ◽  
Computer Aided ◽  
Aided Diagnosis

An effective computer aided diagnosis model for pancreas cancer on PET/CT images

2018 ◽  
Vol 165 ◽  
pp. 205-214 ◽  
Author(s):  
Siqi Li ◽  
Huiyan Jiang ◽  
Zhiguo Wang ◽  
Guoxu Zhang ◽  
Yu-dong Yao
Keyword(s):  
Pancreas Cancer ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Pet Ct ◽  
Computer Aided ◽  
Diagnosis Model ◽  
Aided Diagnosis

scholarly journals MAGAN: Mask Attention Generative Adversarial Network for Liver Tumor CT Image Synthesis

2020 ◽  
Author(s):  
Yang Liu ◽  
Lu Meng ◽  
Jianping Zhong
Keyword(s):  
Liver Tumor ◽  
Medical Image ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Generative Adversarial Network ◽  
Liver Ct ◽  
Adversarial Network ◽  
Computer Aided ◽  
Image Dataset ◽  
Aided Diagnosis

Abstract Background: For deep learning, the size of the dataset greatly affects the final training effect. However, in the field of computer-aided diagnosis, medical image datasets are often limited and even scarce.Methods: We aim to synthesize medical images and enlarge the size of the medical image dataset. In the present study, we synthesized the liver CT images with a tumor based on the mask attention generative adversarial network (MAGAN). We masked the pixels of the liver tumor in the image as the attention map. And both the original image and attention map were loaded into the generator network to obtain the synthesized images. Then the original images, the attention map, and the synthesized images were all loaded into the discriminator network to determine if the synthesized images were real or fake. Finally, we can use the generator network to synthesize liver CT images with a tumor.Results: The experiments showed that our method outperformed the other state-of-the-art methods, and can achieve a mean peak signal-to-noise ratio (PSNR) as 64.72dB.Conclusions: All these results indicated that our method can synthesize liver CT images with tumor, and build large medical image dataset, which may facilitate the progress of medical image analysis and computer-aided diagnosis.

Computer-aided diagnosis workstation and database system for chest diagnosis based on multi-helical CT images

2006 ◽  
Author(s):  
Hitoshi Satoh ◽  
Noboru Niki ◽  
Kiyoshi Mori ◽  
Kenji Eguchi ◽  
Masahiro Kaneko ◽  
...  
Keyword(s):  
Helical Ct ◽  
Database System ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis
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