Nonlinear Adaptively Learned Optimization for Object Localization in 3D Medical Images View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-09-20

AUTHORS

Mayalen Etcheverry , Bogdan Georgescu , Benjamin Odry , Thomas J. Re , Shivam Kaushik , Bernhard Geiger , Nadar Mariappan , Sasa Grbic , Dorin Comaniciu

ABSTRACT

Precise localization of anatomical structures in 3D medical images can support several tasks such as image registration, organ segmentation, lesion quantification and abnormality detection. This work proposes a novel method, based on deep reinforcement learning, to actively learn to localize an object in the volumetric scene. Given the parameterization of the sought object, an intelligent agent learns to optimize the parameters by performing a sequence of simple control actions. We show the applicability of our method by localizing boxes (9 degrees of freedom) on a set of acquired MRI scans of the brain region. We achieve high speed and high accuracy detection results, with robustness to challenging cases. This method can be applied to a broad range of problems and easily generalized to other type of imaging modalities. More... »

PAGES

254-262

Book

TITLE

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

ISBN

978-3-030-00888-8
978-3-030-00889-5

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-00889-5_29

DOI

http://dx.doi.org/10.1007/978-3-030-00889-5_29

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1107102673


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