Dan Nguyen

TL;DR: A convolutional deep network model, U-net, is modified for predicting dose from patient image contours of the planning target volume (PTV) and organs at risk (OAR) and a desired radiation dose distribution from a patient’s PTV and OAR contours is mapped.

TL;DR: A deep learning-based dose prediction model, Hierarchically Densely Connected U-nets, based on two highly popular network architectures: U-net and DenseNet is investigated, finding that this new architecture is able to accurately and efficiently predict the dose distribution, outperforming the other two models in homogeneity, dose conformity, and dose coverage on the test data.

TL;DR: In this paper, the authors proposed an anatomy and beam (AB) model that considers variable beam configurations in addition to patient anatomy to achieve more comprehensive automatic planning with a potentially easier clinical implementation, without the need to train specific models for different beam settings.

TL;DR: Wang et al. as discussed by the authors investigated a deep learning-based dose prediction model, Hierarchically Densely Connected U-Net, based on two highly popular network architectures: U-net and DenseNet.

TL;DR: The GAN model developed produced highly accurate synthetic CT images from conventional, single-sequence MRI images in seconds, and has strong potential to perform well in a clinical workflow for MRI-only brain treatment planning.