Development of electronic cleansing system for CT colonograpy using multiple deep learning and material decomposition

• Project/Area Number: 16H05913
• Research Category: Grant-in-Aid for Young Scientists (A)
• Allocation Type: Single-year Grants
• Research Field: Medical systems
• Research Institution: Oshima National College of Maritime Technology
• Principal Investigator: Tachibana Rie 大島商船高等専門学校, その他部局等, 准教授 (90435462)
• Research Collaborator: YOSHIDA Hiroyuki Massachusetts General Hospital, 3D Imaging Research, Director ; NÄPPI Janne J. Massachusetts General Hospital, 3D Imaging Research
• Project Period (FY): 2016-04-01 – 2019-03-31
• Project Status: Completed (Fiscal Year 2018)
• Budget Amount: ¥17,420,000 (Direct Cost: ¥13,400,000、Indirect Cost: ¥4,020,000); Fiscal Year 2018: ¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000); Fiscal Year 2017: ¥5,070,000 (Direct Cost: ¥3,900,000、Indirect Cost: ¥1,170,000); Fiscal Year 2016: ¥8,710,000 (Direct Cost: ¥6,700,000、Indirect Cost: ¥2,010,000)
• Keywords: 深層学習 / 医用システム / CTコロノグラフィ / Dual energy CT / Deep learning / 機械学習

Outline of Final Research Achievements

This goal of this project was to develop an electronic colon cleansing system using multiple deep convolutional neural networks (DCNNs) and material decomposition information. Preliminary results suggested that the EC method using multiple DCNNs and material decomposition information can remove residual fecal material from CTC images without generating major EC artifacts in the accuracy of EC over using previous method. However, the EC method using multiple DCNNs and material decomposition information was computationally expensive because it had been designed to analyze 54 cut-plane images at each voxel.

Academic Significance and Societal Importance of the Research Achievements

提案した医用画像における領域分割のための多列化深層学習は，複数の画像情報を用いて高精度な領域分割を行うことを可能とし，PET-CT画像などの複数モダリティ画像を用いたシステム構築にも応用が可能であると推測する．提案手法により高精度な大腸電子洗浄法が可能となったが，実用化に至るレベルには達しなかった．しかし，本研究による成果は，前処置なしのCT撮像のみによる大腸がん検診の可能性を示唆しており，多くの人が抵抗感なく大腸がん検診を受診できるシステムの実現につながると考える．

Research Products

• [Int'l Joint Research] Massachusetts General Hospital/Harvard Medical School(米国)
• [Int'l Joint Research] Massachusetts General Hospital/Harvard Medical School(米国)
• [Int'l Joint Research] Massachusetts General Hospital/Harvard Medical School(米国)
• [Journal Article] Electronic cleansing in CT colonography using a generative adversarial network (2019)
  • Author(s): Rie Tachibana, Janne J. Nappi, Toru Hironaka, Hiroyuki Yoshida
  • Journal Title: Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications Volume: 10954 Pages: 1095419-1095419
  • DOI: 10.1117/12.2512466
  • Peer Reviewed / Int'l Joint Research
• [Journal Article] Deep Learning Electronic Cleansing for Single- and Dual-Energy CT Colonography (2018)
  • Author(s): Rie Tachibana, Janne J. Nappi, Junko Ota, Nadja Kohlhase, Toru Hironaka, Se Hyung Kim, Daniele Regge, Hiroyuki Yoshida
  • Journal Title: RadioGraphics Volume: 38 Issue: 7 Pages: 2034-2050
  • DOI: 10.1148/rg.2018170173
  • Peer Reviewed / Int'l Joint Research
• [Journal Article] 深層学習を用いた大腸電子洗浄法の検討 (2018)
  • Author(s): 橘理恵, NAPPI Janne J., 弘中亨, 吉田広行
  • Journal Title: 電子情報通信学会技術研究報告 Volume: 118 Pages: 35-37
  • Int'l Joint Research
• [Journal Article] Transfer-learning deep convolutional neural network for classification of polyp candidates on CT colonography (2017)
  • Author(s): 植村 知規, 陸 慧敏, 金 亨燮, 橘 理恵, 弘 中亨, Nappi Janne J., 吉田 広行
  • Journal Title: Medical Imaging and Information Sciences Volume: 34 Issue: 2 Pages: 80-86
  • DOI: 10.11318/mii.34.80
  • NAID: 130006846731
  • ISSN: 0910-1543, 1880-4977
  • Peer Reviewed / Int'l Joint Research
• [Presentation] Fully convolutional network for electronic cleansing in CT colonography (2018)
  • Author(s): Rie Tachibana, Janne J. Nappi, Hiroyuki Yoshida
  • Organizer: CARS 2018 Computer Assisted Radiology and Surgery - 32nd International Congress and Exhibition, Berlin, Germany
  • Int'l Joint Research
• [Presentation] The Next Step in Electronic Cleansing for CT Colonography: Unsupervised Machine Learning (2018)
  • Author(s): Rie Tachibana, Janne J. Nappi, Hiroyuki Yoshida
  • Organizer: Radiological Society of North America 2018 Scientific Assembly and Annual Meeting
  • Int'l Joint Research
• [Presentation] Deep multi-spectral ensemble learning for electronic cleansing in dual-energy CT colonography (2017)
  • Author(s): Rie Tachibana, Janne J. Nappi, Toru Hironaka, Se Hyung Kim, Hiroyuki Yoshida
  • Organizer: SPIE Medical Imaging 2017
  • Place of Presentation: Renaissance Orlando at Sea World/Orlando, Florida, USA
  • Year and Date: 2017-02-11
  • Int'l Joint Research
• [Presentation] Multislice ensemble deep learning of electronic cleansing for computeraided detection of colonic lesions in multispectral CT colonography (2017)
  • Author(s): Rie Tachibana, Janne J. Nappi, Toru Hironaka, Se Hyung Kim, Hiroyuki Yoshida
  • Organizer: Computer Assisted Radiology and Surgery Proceedings of the 31st International Congress and Exhibition (CARS 2017)
  • Int'l Joint Research
• [Presentation] Deep-learning Electronic Cleansing for Single- and Dual-energy CT Colonography (2017)
  • Author(s): Rie Tachibana, Janne J. Nappi, Junko Ota, Se Hyung Kim, Daniele Regge, Hiroyuki Yoshida
  • Organizer: The 103nd Scientific Assembly & Annual Meeting of the Radiological Society of North America (RSNA2017)
  • Int'l Joint Research
• [Presentation] Deep-learning-based Electronic Cleansing for Single- and Dual-Energy CT Colonography (2016)
  • Author(s): Rie Tachibana, Janne J. Nappi, Toru Hironaka, Se Hyung Kim, Daniele Regge, Hiroyuki Yoshida
  • Organizer: The 102nd Scientific Assembly & Annual Meeting of the Radiological Society of North America (RSNA2016)
  • Place of Presentation: McCormick Place, Chicago, IL, USA
  • Year and Date: 2016-11-27
  • Int'l Joint Research