SPDA: Superpixel-based Data Augmentation for Biomedical Image Segmentation

Yizhe Zhang, Lin Yang, Hao Zheng, Peixian Liang, Colleen Mangold, Raquel G. Loreto, David P. Hughes, Danny Z. Chen

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


Supervised training a deep neural network aims to "teach"the network to mimic human visual perception that is represented by image-and-label pairs in the training data. Superpixelized (SP) images are visually perceivable to humans, but a conventionally trained deep learning model often performs poorly when working on SP images. To better mimic human visual perception, we think it is desirable for the deep learning model to be able to perceive not only raw images but also SP images. In this paper, we propose a new superpixel-based data augmentation (SPDA) method for training deep learning models for biomedical image segmentation. Our method applies a superpixel generation scheme to all the original training images to generate superpixelized images. The SP images thus obtained are then jointly used with the original training images to train a deep learning model. Our experiments of SPDA on four biomedical image datasets show that SPDA is effective and can consistently improve the performance of state-of-the-art fully convolutional networks for biomedical image segmentation in 2D and 3D images. Additional studies also demonstrate that SPDA can practically reduce the generalization gap.

Original languageEnglish (US)
Pages (from-to)572-587
Number of pages16
JournalProceedings of Machine Learning Research
StatePublished - 2019
Event2nd International Conference on Medical Imaging with Deep Learning, MIDL 2019 - London, United Kingdom
Duration: Jul 8 2019Jul 10 2019

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

Cite this