Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurations

2018/12/17 by Ana M. Barragan-Montero, Barragan-Montero, Ana M., Dan Nguyen +11
Computer Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Medical Physics (physics.med-ph) #cs.AI #cs.CV #cs.LG #physics.med-ph

paper · pdf · doi:10.48550/arxiv.1812.06934

arxiv created 2019/04/11 · arxiv updated 2019/04/12

Abstract

The use of neural networks to directly predict three-dimensional dose distributions for automatic planning is becoming popular. However, the existing methods only use patient anatomy as input and assume consistent beam configuration for all patients in the training database. The purpose of this work is to develop a more general model that, in addition to patient anatomy, also considers variable beam configurations, to achieve a more comprehensive automatic planning with a potentially easier clinical implementation, without the need of training specific models for different beam settings.

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