Glioma; Longitudinal brain image analysis; MRI; Medical image analysis; Brain cancer
Jungo Alain, Balsiger Fabian, Reyes Mauricio (2020), Analyzing the Quality and Challenges of Uncertainty Estimations for Brain Tumor Segmentation, in Frontiers in Neuroscience
, 14, 282.
Jungo Alain, Reyes Mauricio (2019), Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation, in Medical Image Computing and Computer Assisted Intervention – MICCAI 201922nd International Conferenc
, Shenzhen Springer International Publishing, China.
Jungo Alain, Meier Raphael, Ermis Ekin, Blatti-Moreno Marcela, Herrmann Evelyn, Wiest Roland, Reyes Mauricio (2018), On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation, in Medical Image Computing and Computer Assisted Intervention – MICCAI 201821st International Conferenc
, Springer International Publishing, Spain.
Jungo Alain, McKinley Richard, Meier Raphael, Knecht Urspeter, Vera Luis, Pérez-Beteta Julián, Molina-García David, Pérez-García Víctor M., Wiest Roland, Reyes Mauricio (2018), Towards Uncertainty-Assisted Brain Tumor Segmentation and Survival Prediction, in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
, Granada, Spain474-485, Springer International Publishing, Granada, Spain474-485.
Jungo Alain, Meier Raphael, Ermis Ekin, Herrmann Evelyn, Reyes Mauricio (2018), Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation, in 1st Conference on Medical Imaging with Deep Learning (MIDL 2018)
, Amsterdam In Press, MIDL OpenReview, Amsterdam In Press.
Background:Automated brain tumor segmentation from multimodal MR images has the potential to leverage diagnosis, treatment, patient follow-up, radiomics studies, etc. However, despite progress in brain tumor image segmentation current algorithms still lack spatial consistency and do not provide a measure of certainty of their results, which is critical for adequate exploitation of results in subsequent steps.Methods:In the proposed project we aim at developing an automated multimodal MR brain tumor segmentation. Based on preliminary results, we will extend our previously proposed supervised-based segmentation approach with a fully-connected Conditional Random Field (CRF) regularization module, featuring improved segmentation accuracy and spatial consistency of tumor compartments. To produce measures of uncertainty of the segmentation results, we will further develop a perturb-and-maximum-posterior-marginal estimation technique. Both improvements feature low computational costs, in agreement with the requirements of the clinical workflow. The approach is to be evaluated on both local glioma datasets and publicly available databases used for benchmarking of brain tumor segmentation algorithms. The evaluation will lead us to test the following hypotheses: Hypotheses:H0) Employing a Fully Connected Conditional Random Field (CRF) approach for brain lesion segmentation yields improved segmentation results, in terms of accuracy and spatial consistency, as compared to a standard pair-wise CRF.H1) Segmentation uncertainty can be obtained through a perturbation & MPM approach under a fully-connected CRF setup. Expected added value of the proposed study:The project will provide improved accuracy and spatial consistency for automated quantification and delineation of brain tumor compartments. The ability of the proposed approach to yield uncertainty measures of the segmentation opens up opportunities to create time-effective human-machine interfaces necessary to monitor and correct results of the automated approach. This can have a substantial impact in clinical scenarios such as radio-therapy and neuro-surgery, where a precise delineation of the tumor is needed in order to precisely target the tumor while preserving as much healthy tissues as possible. In turn, uncertainty estimates of the segmentation can be used as data-quality measures for high-throughput radiomics studies relying on these segmentation results to extract imaging biomarkers of diagnosis, assessment of prognosis, and prediction of therapy response.