Automation of Flow Cytometric Analysis for Quality-Assured Follow-up Assessment to Guide Curative Therapy for Acute Lymphoblastic Leukaemia in Children
Acute Lymphoblastic Leukaemia (ALL) is the most frequent leukaemia entity in children and adolescents. About 15-20% of paediatric patients with the disease still suffer from relapse. Flow cytometry (FCM) is one of the methodologies most useful in this respect, because it is widely available and applicable to most patients. While sample preparation, antibody panels, staining procedures, and FCM acquisition can be harmonized straightforwardly, data analysis and interpretation rely largely on operator skills and experience. AutoFLOW aims at developing an objective and automated tool for multi-parameter FCM data analysis with robust and reliable MRD quantification. The consortium aims at engaging professionals from the medical and ICT fields in a network where the exchange of knowledge will culminate in a valid solution for automated FCM analysis for clinical follow-up assessment of Acute Lymphoblastic Leukaemia.
- 2nd Marie Curie Fellow Position in AutoFLOW: We welcome our Marie Curie recruit Paolo Rota @CVL!
- Check out the 1st release of the FlowVIEW auto-gating and FCM visualisation software – Download and try!
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We proudly present our AutoFLOW-organised satelite workshop on
at the 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2016) on October 21st, 2016 in Athens.
The Call for Papers is now open, deadline: June 21!
AutoFLOW Objectives at a Glance
- Ready-to-use FCM-based MRD-assessment tool for the daily clinical practice for patients
- Assessment of FCM-MRD in samples
- Reduce subjectivity caused by manual operator gating; increase result comparability and reproducibility through automation and standardization.
- Incorporation of the anonymizing algorithm
Diploma Theses and Term Papers (Praktika):
- Flow Cytometry Minimal Residual Disease Assessment using advanced Deep Neural Networks
- Evaluation of automated MRD methods in R
- Porting GMM Training to C++
(See also section Diplomarbeiten.)