OpenGTN · Open Ground Truth Training Network : Magnetic resonance image simulation for training and validation of image analysis algorithms

Open Ground Truth Training Network : Magnetic resonance image simulationfor training and validation of image analysis algorithms

Problem: Magnetic Resonance (MR) imaging is the major medical imaging modality for brain and spine anatomy and pathology. A clear trend can be observed from visual image interpretation to computer-assisted diagnosis by quantification of disease-specific biomarkers, derived from the MR images. The major components in image quantification applications are tissue and organ segmentation and tissue/disease classification. Manual segmentation is too tedious and cumbersome for daily clinical practice and would lead to large inter-user variability. Much research is therefore performed on automatic segmentation techniques, and especially in the past decade the machine learning technique of deep learning is increasingly used. Training, validation and benchmarking of these techniques is currently impeded by the lack of large MR image databases with exact reference segmentations (ground truth). Objective: The openGTN research followed an innovative approach to overcome the current barriers for wide uptake in clinical practice of automatic MR image segmentation. By combining mathematical organ models with physical and biological tissue properties and image simulation and synthesis methods, a substantial public image databases has been established providing ample MR images with ground truth (exact) segmentations, by which fast and accurate optimization and validation of image segmentation algorithms can be enabled. Importance for society: The large MRI databases significantly contributes to faster and less costly development and validation of MR image segmentation techniques, thus facilitating their faster acceptance in daily clinical practice. With these techniques, the diagnosis and treatment of patients can be performed faster and more accurate, eventually leading to better disease diagnosis and treatment selection and outcome. Conclusions: The project has successfully realized its major goals: - methods were developed for simulating (based on MR physics) and synthesizing (based on deep learning) very realistic MR image data of the brain, spine and heart, with ample anatomical variation, with and without pathology - a large simulated/synthesized public MR image database was made available with ground truth references for the design, optimization, validation & benchmarking of image segmentation methods - improved, fast and accurate MR image segmentation methods were developed, that are less sensitive to variation in the image appearance (such as intensity variations, noise, scanner origin)

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз