The Research Consortium for Medical Image Analysis (RECOMIA) is a not for profit organization with the objective of promoting research in the fields of artificial intelligence (AI) and medical imaging.

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Get involved
Get involved in our projects, or apply our tools to your data.
Individual researchers

RECOMIA invites nuclear medicine specialists, radiologists, technologists, physicists and others interested in AI and medical imaging to participate in our projects.

All participants get one month free access to the e-learning site eScan Academy when the project is finished.

Research groups

Are you part of a research group? Are you interested in AI in medical imaging? Do you want to apply our AI-based tools to your data?

Request access by filling out our online form, or send us a mail to contact@recomia.org.

News
Four abstracts have been accepted as oral presentations at the upcoming European Congress of Radiology 2020, March 11-15 in Vienna, Austria. More information will come.
Two abstracts at the Radiological Society of North America (RSNA) 2019 Scientific Assembly and Annual Meeting, December 1 - December 6, 2019, Chicago are based on the RECOMIA platform:
  • Automatic Acquired 18F-Choline PET/CT Biomarkers Association with Prognostic Value in High-Risk Prostate Cancer Patients. Borrelli,P, Kjolhede,H, Enqvist,O, Polymeri,E, Ohlsson,M, Tragardh,E, Edenbrandt,L. Oral presentation
     

  • Deep Learning Takes the Pain Out of Back Breaking Work - Automatic Vertebral Segmentation and Attenuation Measurement for Opportunistic Osteoporosis Screening. Schmidt,D, Enqvist,O, Ulen,J, Persson,E, Tragardh,E, Leander,P, Edenbrandt,L. Poster presentation

Publications

Deep learning‐based quantification of PET/CT prostate gland uptake: association with overall survival. Polymeri E, Sadik M, Kaboteh R, Borrelli P, Enqvist O, Ulén J, Ohlsson M, Trägårdh E, Poulsen MH, Simonsen JA, Hoilund-Carlsen PF, Johnsson ÅA, Edenbrandt L. Clin Physiol Funct Imaging. 2019; Dec 3. doi: 10.1111/cpf.12611[Epub ahead of print]

 

Artificial intelligence-based versus manual assessment of prostate cancer in the prostate gland: a method comparison study. Mortensen MA, Borrelli P, Poulsen MH, Gerke O, Enqvist O, Ulén J, Trägårdh E, Constantinescu C, Edenbrandt L, Lund L, Høilund-Carlsen PF. Clin Physiol Funct Imaging. 2019:39;399-406.

 

Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases. Lindgren Belal S, Sadik M, Kaboteh R, Enqvist O, Ulén J, Poulsen MH, Simonsen J, Høilund-Carlsen PF, Edenbrandt L, Trägårdh E. Eur J Radiol 2019;113:89-95.