74 research outputs found

    Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty

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    The BraTS dataset contains a mixture of high-grade and low-grade gliomas, which have a rather different appearance: previous studies have shown that performance can be improved by separated training on low-grade gliomas (LGGs) and high-grade gliomas (HGGs), but in practice this information is not available at test time to decide which model to use. By contrast with HGGs, LGGs often present no sharp boundary between the tumor core and the surrounding edema, but rather a gradual reduction of tumor-cell density. Utilizing our 3D-to-2D fully convolutional architecture, DeepSCAN, which ranked highly in the 2019 BraTS challenge and was trained using an uncertainty-aware loss, we separate cases into those with a confidently segmented core, and those with a vaguely segmented or missing core. Since by assumption every tumor has a core, we reduce the threshold for classification of core tissue in those cases where the core, as segmented by the classifier, is vaguely defined or missing. We then predict survival of high-grade glioma patients using a fusion of linear regression and random forest classification, based on age, number of distinct tumor components, and number of distinct tumor cores. We present results on the validation dataset of the Multimodal Brain Tumor Segmentation Challenge 2020 (segmentation and uncertainty challenge), and on the testing set, where the method achieved 4th place in Segmentation, 1st place in uncertainty estimation, and 1st place in Survival prediction.Comment: Presented (virtually) in the MICCAI Brainles workshop 2020. Accepted for publication in Brainles proceeding

    Diffusion Tensor Imaging in Pediatric Brain Tumor Patients

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    In this dissertation, we outline our efforts to introduce an advanced MRI imaging technique called Diffusion Tensor Imaging (DTI) to the pediatric brain tumor population. We discuss the theory and application of DTI as it was performed in a series of translational investigations at St. Jude Children’s Research Hospital. We present evidence of how the introduction of this technique impacted diagnosis, and treatment. And finally, we demonstrate how DTI was used to investigate cognitive morbidities associated with cancer treatment and how this research provided insight into the underlying pathophysiology involved in the development of these treatment sequela. This research has generated important insights into the fundamental causes of neuroanatomical and cognitive deficits associated with cancer and cancer therapy. The use of DTI has permitted us to identify potential targets for improved radiological and surgical techniques as well as targets for pharmacological and behavioral interventions that might improve cognitive function in cancer survivors. The discoveries here afford us an opportunity to reduce the negative effects of cancer therapy on patients treated in the future while maintaining successful survival rates

    Washington University Senior Undergraduate Research Digest (WUURD), Spring 2018

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    From the Washington University Office of Undergraduate Research Digest (WUURD), Vol. 13, 05-01-2018. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Scienc
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