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| 1 | Laparoscopy-Assisted Total Gastrectomy for Early Gastric Cancer: Comparison With Conventional Open Total Gastrectomy显示文摘 | Shinsuke Usui Tatsuya Yoshida Koji Ito Susumu Hiranuma Shin-ei Kudo Takehisa Iwai | 2005 | Surgical Laparoscopy Endoscopy & Percutaneous Techniques2005，，6： | 1 |
| 2 | Narrow-band imaging system with magnifying endoscopy for superficial esophageal lesions显示文摘 | Tatsuya Yoshida Haruhiro Inoue Shinsuke Usui Hitoshi Satodate Norio Fukami Shin-ei Kudo | 2004 | Gastrointestinal Endoscopy2004，，2： | 1 |
| 3 | Laparoscopy-Assisted Total Gastrectomy for Early Gastric Cancer: Comparison With Conventional Open Total Gastrectomy显示文摘 | Shinsuke Usui Tatsuya Yoshida Koji Ito Susumu Hiranuma Shin-ei Kudo Takehisa Iwai | 2005 | Surgical Laparoscopy, Endoscopy & Percutaneous Techniques2005，，6： | 1 |
| 4 | Hand-assisted laparoscopic total gastrectomy for early gastric 显示文摘 | Usui Shinsuke Inoue Haruhiro Yoshida Tatsuya | 2003 | Surg Laparos Endos Percutaneous Tech2003，13，5： | 1 |
| 5 | Hand-assisted Laparoscopic Esophagojejunostomy Using Newly Developed Purse-string Suture Instrument “Endo-PSI”显示文摘 | Shinsuke Usui Koji Ito Susumu Hiranuma Noriaki Takiguchi Akio Matsumoto Takehisa Iwai | 2007 | Surgical Laparoscopy Endoscopy & Percutaneous Techniques2007，，2： | 1 |
| 6 | Preoperative Imaging of Surrounding Arteries by Three-Dimensional CT: Is It Useful for Laparoscopic Gastrectomy?显示文摘 | Shinsuke Usui Susumu Hiranuma Tamaki Ichikawa Manabu Maeda Shin-ei Kudo Takehisa Iwai | 2005 | Surgical Laparoscopy, Endoscopy & Percutaneous Techniques2005，，2： | 1 |
| 7 | Invention of outdoor closed type photobioreactor for microalgae显示文摘 | Toru Sato Shinsuke Usui Yoshihiro Tsuchiya | 2006 | Energy Conversion and Management2006，47，6： | 1 |
| 8 | Narrow-band imaging system with magnifying endoscopy for superficial esophageal lesions显示文摘 | Tatsuya Yoshida Haruhiro Inoue Shinsuke Usui Hitoshi Satodate Norio Fukami Shin-ei Kudo | 2004 | Gastrointestinal Endoscopy2004，，2： | 1 |
| 9 | Laparoscopy-assisted esophagoenteral anastomosis using endoscopic purse-string suture instrument “Endo-PSI (II)” and circular stapler显示文摘 | Shinsuke Usui Kentaro Nagai Susumu Hiranuma Noriaki Takiguchi Akio Matsumoto Katsuhiro Sanada | 2008 | Gastric Cancer2008，，4： | 1 |
| 10 | Laparoscopy-Assisted Total Gastrectomy for Early Gastric Cancer: Comparison With Conventional Open Total Gastrectomy显示文摘 | Shinsuke Usui Tatsuya Yoshida Koji Ito Susumu Hiranuma Shin-ei Kudo Takehisa Iwai | 2005 | Surgical Laparoscopy Endoscopy & Percutaneous Techniques2005，，6： | 1 |
| 11 | Laparoscopy-assisted esophagoenteral anastomosis using endoscopic purse-string suture instrument “Endo-PSI (II)” and circular stapler显示文摘 | Shinsuke Usui Kentaro Nagai Susumu Hiranuma Noriaki Takiguchi Akio Matsumoto Katsuhiro Sanada | 2008 | Gastric Cancer2008，，4： | 1 |
| 12 | Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography显示文摘To minimize radiation risk,dose reduction is important in the diagnostic and therapeutic applications of computed tomography(CT).However,image noise degrades image quality owing to the reduced X-ray dose and a possible unacceptably reduced diagnostic performance.Deep learning approaches with convolutional neural networks(CNNs)have been proposed for natural image denoising;however,these approaches might introduce image blurring or loss of original gradients.The aim of this study was to compare the dose-dependent properties of a CNN-based denoising method for low-dose CT with those of other noise-reduction methods on unique CT noise-simulation images.To simulate a low-dose CT image,a Poisson noise distribution was introduced to normal-dose images while convoluting the CT unit-specific modulation transfer function.An abdominal CT of 100 images obtained from a public database was adopted,and simulated dose-reduction images were created from the original dose at equal 10-step dose-reduction intervals with a final dose of 1/100.These images were denoised using the denoising network structure of CNN(DnCNN)as the general CNN model and for transfer learning.To evaluate the image quality,image similarities determined by the structural similarity index(SSIM)and peak signal-to-noise ratio(PSNR)were calculated for the denoised images.Significantly better denoising,in terms of SSIM and PSNR,was achieved by the DnCNN than by other image denoising methods,especially at the ultra-low-dose levels used to generate the 10%and 5%dose-equivalent images.Moreover,the developed CNN model can eliminate noise and maintain image sharpness at these dose levels and improve SSIM by approximately 10%from that of the original method.In contrast,under small dose-reduction conditions,this model also led to excessive smoothing of the images.In quantitative evaluations,the CNN denoising method improved the low-dose CT and prevented over-smoothing by tailoring the CNN model. | Keisuke Usui Koichi Ogawa Masami Goto Yasuaki Sakano Shinsuke Kyougoku Hiroyuki Daida | 2021 | Visual Computing for Industry,Biomedicine,and Art2021，4，1： | 0 |
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