Abstract
Although deep networks have been shown to perform very well on a variety of tasks, inference in the presence of pathology in medical images presents challenges to traditional networks. Given that medical image analysis typically requires a sequence of inference tasks to be performed (e.g. registration, segmentation), this results in an accumulation of errors over the sequence of deterministic outputs. In this paper, we explore the premise that, by embedding uncertainty estimates across cascaded inference tasks, the final prediction results should improve over simply cascading the deterministic classification results or performing inference in a single stage. Specifically, we develop a deep learning framework that propagates voxel-based uncertainty measures (e.g. Monte Carlo (MC) dropout sample variance) across inference tasks in order to improve the detection and segmentation of focal pathologies (e.g. lesions, tumours) in brain MR images. We apply the framework to two different contexts. First, we demonstrate that propagating multiple sclerosis T2 lesion segmentation results along with their associated uncertainty measures improves subsequent T2 lesion detection accuracy when evaluated on a proprietary large-scale, multi-site, clinical trial dataset. Second, we show how by propagating uncertainties associated with a regressed 3D MRI volume as an additional input to a follow-on brain tumour segmentation task, one can improve segmentation results on the publicly available BraTS-2018 dataset.
R. Mehta and T. Christinck—Equal contribution.
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Acknowledgements
This work was supported by a Canadian Natural Science and Engineering Research Council (NSERC) Collaborative Research and Development Grant (CRDPJ 505357 - 16), Synaptive Medical, the Canadian NSERC Discovery and CREATE grants, and an award from the International Progressive MS Alliance (PA-1603-08175).
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Mehta, R., Christinck, T., Nair, T., Lemaitre, P., Arnold, D., Arbel, T. (2019). Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference. In: Greenspan, H., et al. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures. CLIP UNSURE 2019 2019. Lecture Notes in Computer Science(), vol 11840. Springer, Cham. https://doi.org/10.1007/978-3-030-32689-0_3
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