Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning
This addresses uncertainty estimation for improved quality assurance in radiotherapy treatment planning, particularly for prostate cancer patients, though it is incremental as it builds on existing multi-task learning methods.
The paper tackled the problem of uncertainty in MR-only radiotherapy planning by proposing a probabilistic multi-task network that jointly regresses synthetic CT scans and segments organs-at-risk from MRI, resulting in more accurate and consistent synthetic CTs with state-of-the-art segmentation results on prostate cancer scans.
Multi-task neural network architectures provide a mechanism that jointly integrates information from distinct sources. It is ideal in the context of MR-only radiotherapy planning as it can jointly regress a synthetic CT (synCT) scan and segment organs-at-risk (OAR) from MRI. We propose a probabilistic multi-task network that estimates: 1) intrinsic uncertainty through a heteroscedastic noise model for spatially-adaptive task loss weighting and 2) parameter uncertainty through approximate Bayesian inference. This allows sampling of multiple segmentations and synCTs that share their network representation. We test our model on prostate cancer scans and show that it produces more accurate and consistent synCTs with a better estimation in the variance of the errors, state of the art results in OAR segmentation and a methodology for quality assurance in radiotherapy treatment planning.