Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation
Name
1903.03148.pdf
Description
Accepted version
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875.56 KB
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Adobe PDF
Checksum (MD5)
471ce6120b417c72555968d3704d7a26
Author(s)
Dalca, Adrian Vasile
Guttag, John V.
Sabuncu, Mert R.
Date Issued
December 2018
Journal
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Publisher
IEEE
Citation
Dalca, Adrian V., Guttag, John and Sabuncu, Mert R., "Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 18-23 June 2018. Salt Lake City, UT, IEEE, 2018
Version
Author's final manuscript
Abstract
We consider the problem of segmenting a biomedical image into anatomical regions of interest. We specifically address the frequent scenario where we have no paired training data that contains images and their manual segmentations. Instead, we employ unpaired segmentation images that we use to build an anatomical prior. Critically these segmentations can be derived from imaging data from a different dataset and imaging modality than the current task. We introduce a generative probabilistic model that employs the learned prior through a convolutional neural network to compute segmentations in an unsupervised setting. We conducted an empirical analysis of the proposed approach in the context of structural brain MRI segmentation, using a multi-study dataset of more than 14,000 scans. Our results show that an anatomical prior enables fast unsupervised segmentation which is typically not possible using standard convolutional networks. The integration of anatomical priors can facilitate CNN-based anatomical segmentation in a range of novel clinical problems, where few or no annotations are available and thus standard networks are not trainable. The code, model definitions and model weights are freely available at http://github.com/adalca/neuron. Keywords: Image segmentation; Biological system modeling; Biomedical imaging; Convolutional neural networks; Shape; Computational modeling; Decoding.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/cvpr.2018.00968
