Dose Prediction for Prostate Radiation Treatment: Feasibility of a DistanceBased Deep Learning Model This study aims to demonstrate the feasibility of using a novel distancebased representation of 3D CTscan images to train a deep learning model for dose predictions in radiation treatment planning. The distance representation is inspired by previous knowledge of the domain to increase the generalizability of the deep learning models for radiation treatment planning. Conventional knowledgebased planning methods extract engineered features from 3D CTscan images, as well as other patientsu0027 features, to predict the best achievable dose in a cancerous area and other organs at risk. Recent studies have shown higher accuracy in voxellevel dose prediction using deep learning models compared to the conventional machine learning approaches. Since the data resources for training these models are limited, most of the studies use 2D contour information to represent the patient anatomy. This representation loses volumetric information, and it is sensitive to small changes in patient orientation and translation. The distancebased representation introduced in this paper is inspired by the domain knowledge and is able to maintain the volumetric distance information despite the 2D slicing of 3D CTimage. According to prior studies in the radiation treatment planning domain, there is a strong association between the organsatrisk distance from the cancerous volume and the patientu0027s vulnerability to receive excessive dose. Therefore, the contour value in prior representation was replaced by voxel distance from cancerous volume. This modification in representation makes it transition and orientation invariant and adds potential robustness to patient positioning differences during the imagingplanning process. We evaluated the distancebased deep learning models through experiments for prediction of prostate cancer patientsu0027 vulnerability and voxellevel dose distribution using convolutional neural network and Unet models, respectively. The results were compared with contourbased Unet model as well as conventional machine learning with engineered representations. We found that the performance was comparable or higher than the prior stateoftheart results for prostatecancer dose distribution prediction.

Low Dose CT Image Denoising Using Multilevel Feature Fusion Network and Edge Constraints Lowdose computed tomography image denoising is a challenging task that has been studied by many researchers. Current denoising methods based on deep learning tend to produce a blur effect on the final results, especially at high noise levels, which are prone to oversmoothed edges and loss of details. In this paper, we propose a deep learning approach based on deep convolutional and edge constraints to mitigate these problems. Firstly, to avoid the loss of shallow layers details while obtaining semanticallyricher features information, we use dilated convolution instead of standard convolution, and fusion feature maps of different levels to aggregate information from different receptive field. Secondly, in order to improve the networku0027s ability to distinguish between noise and image content, we have designed an attention block to adaptively recalibrate the information relationship of the fusion feature maps. Finally, we incorporate edge prior knowledge into LDCT image denoising task, guiding the network to pay more attention on texture and structure information by edge constraints loss. Extensive experiments demonstrate that the proposed method achieves significant improvements over the stateoftheart methods.