BibTex format
@inproceedings{Alansary:2018:10.1007/978-3-030-00928-1_32,
author = {Alansary, A and Le, Folgoc L and Vaillant, G and Oktay, O and Li, Y and Bai, W and Passerat-Palmbach, J and Guerrero, R and Kamnitsas, K and Hou, B and McDonagh, S and Glocker, B and Kainz, B and Rueckert, D},
doi = {10.1007/978-3-030-00928-1_32},
pages = {277--285},
publisher = {Springer Verlag},
title = {Automatic view planning with multi-scale deep reinforcement learning agents},
url = {http://dx.doi.org/10.1007/978-3-030-00928-1_32},
year = {2018}
}
RIS format (EndNote, RefMan)
TY - CPAPER
AB - We propose a fully automatic method to find standardizedview planes in 3D image acquisitions. Standard view images are impor-tant in clinical practice as they provide a means to perform biometricmeasurements 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 DeepQ-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.
AU - Alansary,A
AU - Le,Folgoc L
AU - Vaillant,G
AU - Oktay,O
AU - Li,Y
AU - Bai,W
AU - Passerat-Palmbach,J
AU - Guerrero,R
AU - Kamnitsas,K
AU - Hou,B
AU - McDonagh,S
AU - Glocker,B
AU - Kainz,B
AU - Rueckert,D
DO - 10.1007/978-3-030-00928-1_32
EP - 285
PB - Springer Verlag
PY - 2018///
SN - 0302-9743
SP - 277
TI - Automatic view planning with multi-scale deep reinforcement learning agents
UR - http://dx.doi.org/10.1007/978-3-030-00928-1_32
UR - https://link.springer.com/chapter/10.1007%2F978-3-030-00928-1_32
UR - http://hdl.handle.net/10044/1/60748
ER -
