No abstract

Although dose prediction for intensity modulated radiation therapy (IMRT) has been accomplished by a deep learning approach, delineation of some structures is needed for the prediction. We sought to develop a fully automated dose-generation framework for IMRT of prostate cancer by entering the patient CT datasets without the contour information into a generative adversarial network (GAN) and to compare its prediction performance to a conventional prediction model trained from patient contours.

This paper reviews the basics and recent researches of computer-aided diagnosis (CAD) systems for assisting neuroradiologists in detection of brain diseases, e.g., asymptomatic unruptured aneurysms, Alzheimer's disease, vascular dementia, and multiple sclerosis (MS), in magnetic resonance (MR) images. The CAD systems consist of image feature extraction based on image processing techniques and machine learning classifiers such as linear discriminant analysis, artificial neural networks, and support vector machines. We introduce useful examples of the CAD systems in the neuroradiology, and conclude with possibilities in the future of the CAD systems for brain diseases in MR images.

No abstract

The limits of applicability of DIR are strongly dependent on the magnitude of deformation. There is a threshold limit beyond which the accuracy of DIR fails in uniform low contrast anatomy. The sensitivity of the DIR performance to the number of fiducial markers present indicates that if DIR performance is solely assessed with the contrast rich features present in clinical anatomy, the results may not be reflective of the true DIR performance in uniform low contrast anatomy.