Paper Info
Title
Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents.
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 1.53 mm, 1.98mm and 4.84 mm, respectively.
Year
DOI
Venue
2018
10.1007/978-3-030-00928-1_32
Lecture Notes in Computer Science
DocType
Volume
ISSN
Conference
11070
0302-9743
Citations 
PageRank 
References 
0
0.34
8
Authors
14
Name
Order
Citations
PageRank
Amir Alansary161.18
Loïc Le Folgoc2516.48
Ghislain Vaillant300.34
Ozan Oktay428020.15
Yuanwei Li552.60
Wenjia Bai644535.84
Jonathan Passerat-Palmbach712614.24
Ricardo Guerrero810010.35
Konstantinos Kamnitsas9383.27
Benjamin Hou10245.65
Steven McDonagh11646.27
Ben Glocker122157119.81
Bernhard Kainz1317920.50
Daniel Rueckert149338637.58