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.

An article presenting the RECOMIA platform can be found here.

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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

This abstract, to be presented at the upcoming virtual European Association of Nuclear Medicine (EANM) congress 2020, was based on the RECOMIA platform:

 

  • Artificial intelligence can warn for focal skeleton/bone marrow uptake in Hodgkin lymphoma patients staged with FDG-PET/CT. M Sadik, J Lopes-Urdaneta, J Ullén, O Enqvist, A Krupic, R Kumar, PO Andersson, E Trägårdh, L Edenbrandt. 

This abstract, presented at the virtual Society of Nuclear Medicine and Molecular Imaging (SNMMI) congress 2020, was based on the RECOMIA platform:

 

  • Application of convolutional neural network to123I-MIBG SPECT imaging: automatic quantitation vs. manual measurements. S Saito, K Nakajima, L Edenbrandt, O Enqvist, J Ulen, S Kinuya. 

 

Three abstracts at the virtual European Congress of Radiology (ECR) 2020 were based on the RECOMIA platform. One of abstracts was:

  • RECOMIA – a Cloud-based platform for Artificial Intelligence research in radiology. P Borrelli, E Trägårdh, R Kaboteh, T Gillberg, J Ulén, O Enqvist, L Edenbrandt. 

Publications
  • RECOMIA-a cloud-based platform for artificial intelligence research in nuclear medicine and radiology. Trägårdh E, Borrelli P, Kaboteh R, Gillberg T, Ulén J, Enqvist O, Edenbrandt L. EJNMMI Phys. 2020:4;7:51.

 

  • 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. 2020;40:106-113.

 

  • 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.