ArticleEJNMMI physics2020
RECOMIA-a cloud-based platform for artificial intelligence research in nuclear medicine and radiology.
Article in EJNMMI physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.
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Who cites it
53 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics.European radiology experimental · 2022Pooled it
- Validation of novel low-dose CT methods for quantifying bone marrow in the appendicular skeleton of patients with multiple myeloma: initial results from the [European journal of nuclear medicine and molecular imaging · 2026Trial
- Application of an artificial intelligence-based tool in [European journal of nuclear medicine and molecular imaging · 2023Trial
- Radiology research platform: automating multicenter radiological research workflows using a cloud-native platform.European radiology · 2026Article
- Deep learning for automatic detection of prostatectomy in CT images of prostate cancer patients.Physical and engineering sciences in medicine · 2026Article
- AI-Driven Bone and Marrow Segmentation on FLT-PET/CT: Technical Multi-organ Validation in AML and HCT.Research square · 2026Article
- Prognostic value of tumour volume based on [EJNMMI reports · 2026Article
- Comparison between artificial intelligence-based and manual organ delineations in pretreatment computed tomography scans of prostate cancer patients: a visual grading study.Radiation protection dosimetry · 2026Article
- coPERCIST: AI-assisted PET-CT response assessment.European journal of nuclear medicine and molecular imaging · 2026Article
- The Prognostic Value of Biomarkers Identified by [Diagnostics (Basel, Switzerland) · 2025Article
- Hyperchaos and the fusion of Moore's automaton with gold sequences for augmented medical image encryption.Scientific reports · 2025Article
- AI Improves Agreement and Reduces Time for Quantifying Metabolic Tumour Burden in Hodgkin Lymphoma.Hematology reports · 2025Article
- NaF-PET/CT imaging of atherosclerosis in type 2 diabetes: Associations with sex and history of cardiovascular events in a 2-year follow-up study.European journal of nuclear medicine and molecular imaging · 2025Article
- Monitoring ctDNA in aggressive B-cell lymphoma: a prospective correlative study of ctDNA kinetics and PET-CT metrics.Blood advances · 2025Article
- Inter-reader agreement of quantitative FDG PET/CT biomarkers in lymphoma: a multicentre evaluation of MTV, TLG and Dmax.BMC medical imaging · 2025Article
- A fully automated AI-based method for tumour detection and quantification on [EJNMMI physics · 2025Article
- AI based automatic measurement of split renal function in [EJNMMI reports · 2025Article
- Deep learning (DL)-based advancements in prostate cancer imaging: Artificial intelligence (AI)-based segmentation ofPrecision radiation oncology · 2025Review
- Automated segmentation of the sacro-iliac joints, posterior spinal joints and discovertebral units on low-dose computed tomography for Na[EJNMMI physics · 2025Article
- Prediction of bone oligometastases in breast cancer using models based on deep learning radiomics of PET/CT imaging.Frontiers in oncology · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundArtificial intelligence (AI) is about to transform medical imaging. The Research Consortium for Medical Image Analysis (RECOMIA), a not-for-profit organisation, has developed an online platform to facilitate collaboration between medical researchers and AI researchers. The aim is to minimise the time and effort researchers need to spend on technical aspects, such as transfer, display, and annotation of images, as well as legal aspects, such as de-identification. The purpose of this article is to present the RECOMIA platform and its AI-based tools for organ segmentation in computed tomography (CT), which can be used for extraction of standardised uptake values from the corresponding positron emission tomography (PET) image.
resultsThe RECOMIA platform includes modules for (1) local de-identification of medical images, (2) secure transfer of images to the cloud-based platform, (3) display functions available using a standard web browser, (4) tools for manual annotation of organs or pathology in the images, (5) deep learning-based tools for organ segmentation or other customised analyses, (6) tools for quantification of segmented volumes, and (7) an export function for the quantitative results. The AI-based tool for organ segmentation in CT currently handles 100 organs (77 bones and 23 soft tissue organs). The segmentation is based on two convolutional neural networks (CNNs): one network to handle organs with multiple similar instances, such as vertebrae and ribs, and one network for all other organs. The CNNs have been trained using CT studies from 339 patients. Experienced radiologists annotated organs in the CT studies. The performance of the segmentation tool, measured as mean Dice index on a manually annotated test set, with 10 representative organs, was 0.93 for all foreground voxels, and the mean Dice index over the organs were 0.86 (0.82 for the soft tissue organs and 0.90 for the bones).
conclusionThe paper presents a platform that provides deep learning-based tools that can perform basic organ segmentations in CT, which can then be used to automatically obtain the different measurement in the corresponding PET image. The RECOMIA platform is available on request at www.recomia.org for research purposes.
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