Titlebook: Artificial Intelligence in Radiation Therapy; First International Dan Nguyen,Lei Xing,Steve Jiang Conference proceedings 2019 Springer Nat

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Automatically Tracking and Detecting Significant Nodal Mass Shrinkage During Head-and-Neck Radiatio harmful radiation dose. In an institutional IRB-approved protocol, patients were monitored with weekly T2-weighted MRIs. Gross tumor volumes (GTV) from pre-treatment MRI were propagated to weekly MRIs via deformable image registrations (DIR) for tracking the change of GTV nodal volume and detection

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4D-CT Deformable Image Registration Using an Unsupervised Deep Convolutional Neural Network,cycle. It can provide valuable information on the shapes and trajectories of tumor and normal structures to guide treatment planning and improve the accuracy of tumor delineation. Respiration-induced abdominal tissue motion causes significant problems in effective irradiation of abdominal cancer pat

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A Two-Stage Approach for Automated Prostate Lesion Detection and Classification with Mask R-CNN andd visualization of prostate tissues and lesions. Their malignancy can be diagnosed before any necessary invasive approaches, such as needle biopsy, at the risk of damage to or inflammation of the periprostatic nerves, prostate and bladder neck. However, the prostate tissue malignancy on magnetic res

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A Novel Deep Learning Framework for Standardizing the Label of OARs in CT,ich severely hampers the collection and curation of clinical data for research purpose. Currently, data cleaning is mainly done manually, which is time-consuming. The existing methods for automatically relabeling OARs remain unpractical with real patient data, due to the inconsistent delineation and

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Voxel-Level Radiotherapy Dose Prediction Using Densely Connected Network with Dilated Convolutions,d protecting surrounding organs at risk (OARs). Automatic dose prediction can reduce manual adjustments by providing close to optimal radiotherapy planning parameters, which is studied in this work. We developed a voxel-level dose prediction framework based on an end-to-end trainable densely-connect

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One-Dimensional Convolutional Network for Dosimetry Evaluation at Organs-at-Risk in Esophageal Raditherapy treatment plans. Automatic DVH prediction is therefore very use-ful to achieve high-quality esophageal treatment planning. This paper studied stacked denoise auto-encoder (SDAE) to compute correlation between DVH and distance to target histogram (DTH) based on the fact that the geometric inf

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