Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3036
Publisher DOI: 10.1515/cdbme-2020-0001
Title: 4D spatio-temporal convolutional networks for object position estimation in OCT volumes
Language: English
Authors: Bengs, Marcel 
Gessert, Nils Thorben 
Schlaefer, Alexander 
Keywords: convolutional neural networks;optical coherence tomography;position estimation;spatio-temporal data
Issue Date: 17-Sep-2020
Publisher: De Gruyter
Source: Current Directions in Biomedical Engineering 1 (6): 20200001 (2020)
Journal: Current directions in biomedical engineering 
Abstract (english): 
Tracking and localizing objects is a central problem in computer-assisted surgery. Optical coherence tomography (OCT) can be employed as an optical tracking system, due to its high spatial and temporal resolution. Recently, 3D convolutional neural networks (CNNs) have shown promising performance for pose estimation of a marker object using single volumetric OCT images. While this approach relied on spatial information only, OCT allows for a temporal stream of OCT image volumes capturing the motion of an object at high volumes rates. In this work, we systematically extend 3D CNNs to 4D spatio-temporal CNNs to evaluate the impact of additional temporal information for marker object tracking. Across various architectures, our results demonstrate that using a stream of OCT volumes and employing 4D spatio-temporal convolutions leads to a 30% lower mean absolute error compared to single volume processing with 3D CNNs.
URI: http://hdl.handle.net/11420/7722
DOI: 10.15480/882.3036
ISSN: 2364-5504
Institute: Medizintechnische Systeme E-1 
Document Type: Article
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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