Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation
Name
1902.09383.pdf
Description
Submitted version
Size
4.42 MB
Format
Adobe PDF
Checksum (MD5)
51ba08f78ec4d5889df7ca5a382b3e21
Author(s)
Zhao, Amy (Xiaoyu Amy)
Balakrishnan, Guha
Durand, Fredo
Guttag, John V
Dalca, Adrian Vasile
Date Issued
June 2019
Journal
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Zhao, Amy et al. “Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation.” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019, Long Beach, California, Institute of Electrical and Electronics Engineers (IEEE), June 2019. © 2019 The Author(s)
Version
Original manuscript
Abstract
Image segmentation is an important task in many medical applications. Methods based on convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on supervised training with large labeled datasets. Labeling medical images requires significant expertise and time, and typical hand-tuned approaches for data augmentation fail to capture the complex variations in such images. We present an automated data augmentation method for synthesizing labeled medical images. We demonstrate our method on the task of segmenting magnetic resonance imaging (MRI) brain scans. Our method requires only a single segmented scan, and leverages other unlabeled scans in a semi-supervised approach. We learn a model of transformations from the images, and use the model along with the labeled example to synthesize additional labeled examples. Each transformation is comprised of a spatial deformation field and an intensity change, enabling the synthesis of complex effects such as variations in anatomy and image acquisition procedures. We show that training a supervised segmenter with these new examples provides significant improvements over state-of-the-art methods for one-shot biomedical image segmentation.
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.2019.00874
