@inproceedings{DBLP:conf/miccai/AlansaryFVOLBPG18,
  author    = {Amir Alansary and
               Lo{\"{\i}}c Le Folgoc and
               Ghislain Vaillant and
               Ozan Oktay and
               Yuanwei Li and
               Wenjia Bai and
               Jonathan Passerat{-}Palmbach and
               Ricardo Guerrero and
               Konstantinos Kamnitsas and
               Benjamin Hou and
               Steven G. McDonagh and
               Ben Glocker and
               Bernhard Kainz and
               Daniel Rueckert},
  title     = {Automatic View Planning with Multi-scale Deep Reinforcement Learning
               Agents},
  booktitle = {{MICCAI} {(1)}},
  series    = {Lecture Notes in Computer Science},
  volume    = {11070},
  pages     = {277--285},
  publisher = {Springer},
  year      = {2018},
  doi = {10.1007/978-3-030-00928-1\_32},
  url = {https://doi.org/10.1007/978-3-030-00928-1\_32},
  eprint = {1806.03228},
  archiveprefix = {arXiv},
  abstract = {We propose a fully automatic method to find standardized view planes in 3D image acquisitions. Standard view images are important in clinical practice as they provide a means to perform biometric measurements from similar anatomical regions. These views are often constrained to the native orientation of a 3D image acquisition. Navigating through target anatomy to find the required view plane is tedious and operator-dependent. For this task, we employ a multi-scale reinforcement learning (RL) agent framework and extensively evaluate several Deep Q-Network (DQN) based strategies. RL enables a natural learning paradigm by interaction with the environment, which can be used to mimic experienced operators. We evaluate our results using the distance between the anatomical landmarks and detected planes, and the angles between their normal vector and target. The proposed algorithm is assessed on the mid-sagittal and anterior-posterior commissure planes of brain MRI, and the 4-chamber long-axis plane commonly used in cardiac MRI, achieving accuracy of 1.53mm, 1.98mm and 4.84mm, respectively.},
}