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A fully automated deep learning-based software for fast, robust and accurate detection and segmentation of tumours and metastasis

Project description

Improving lung cancer outcomes via an automated tumour characterisation approach

Fast and accurate diagnosis is crucial for the effective treatment of all types of cancer. Unfortunately, the advanced detection, segmentation and characterisation of tumours hinges on the laborious manual or semi-manual processes used, restricting treatment accuracy and treatment response monitoring. The EU-funded AUTO.DISTINCT project will introduce, demonstrate and evaluate a groundbreaking, fully automated software for the fast, accurate, observer-independent and reproducible detection and volumetric segmentation of lung tumours and metastases on CT images. The project's work will radically improve tumour characterisation in the case of lung cancer patients by refining the detection of lesions on CT images, with significant impacts on patient outcomes and radiotherapy treatment accuracy.

Field of science

  • /natural sciences/computer and information sciences/software
  • /medical and health sciences/clinical medicine/cancer

Call for proposal

ERC-2020-PoC
See other projects for this call

Funding Scheme

ERC-POC-LS - ERC Proof of Concept Lump Sum Pilot

Host institution

UNIVERSITEIT MAASTRICHT
Address
Minderbroedersberg 4-6
6200 MD Maastricht
Netherlands
Activity type
Higher or Secondary Education Establishments
EU contribution
€ 150 000

Beneficiaries (1)

UNIVERSITEIT MAASTRICHT
Netherlands
EU contribution
€ 150 000
Address
Minderbroedersberg 4-6
6200 MD Maastricht
Activity type
Higher or Secondary Education Establishments