Abstract
Identification of anatomical regions of interest is a prerequisite in many medical image analysis tasks. We propose a method that automatically identifies a slice of interest (SOI) in 3D images with a convolutional neural network (ConvNet) regressor.
In 150 chest CT scans two reference slices were manually identified: one containing the aortic root and another superior to the aortic arch. In two independent experiments, the ConvNet regressor was trained with 100 CTs to determine the distance between each slice and the SOI in a CT. To identify the SOI, a first order polynomial was fitted through the obtained distances.
In 50 test scans, the mean distances between the reference and the automatically identified slices were 5.7 mm (4.0 slices) for the aortic root and 5.6 mm (3.7 slices) for the aortic arch.
The method shows similar results for both tasks and could be used for automatic slice identification.
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Acknowledgments
This study was funded by the Netherlands Organization for Scientific Research (NWO)/Foundation for Technology Sciences (STW); Project 12726.
The authors thank the National Cancer Institute for access to NCI’s data collected by the National Lung Screening Trial. The statements contained herein are solely those of the authors and do not represent or imply concurrence or endorsement by NCI.
The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.
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de Vos, B.D., Viergever, M.A., de Jong, P.A., Išgum, I. (2016). Automatic Slice Identification in 3D Medical Images with a ConvNet Regressor. In: Carneiro, G., et al. Deep Learning and Data Labeling for Medical Applications. DLMIA LABELS 2016 2016. Lecture Notes in Computer Science(), vol 10008. Springer, Cham. https://doi.org/10.1007/978-3-319-46976-8_17
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DOI: https://doi.org/10.1007/978-3-319-46976-8_17
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