# SPDA: Superpixel-based Data Augmentation for Biomedical Image Segmentation

arXiv: https://arxiv.org/abs/1903.00035 · HTML: https://mlsanity.com/paper/1903.00035/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2019.

## Built on by (most cited first, 3 of 3)

- [SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix Data Augmentation](https://mlsanity.com/paper/2204.08458/) (2022)
- [H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation](https://mlsanity.com/paper/2206.01309/) (2022)
- [HSMix: Hard and Soft Mixing Data Augmentation for Medical Image Segmentation](https://mlsanity.com/paper/2511.17614/) (2025)

## Abstract (arXiv)

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.