DDU-Net: A Domain Decomposition-Based CNN for High-Resolution Image Segmentation on Multiple GPUs

Abstract

The segmentation of ultra-high resolution images poses challenges such as loss of spatial information or computational inefficiency. In this work, a novel approach that combines encoder-decoder architectures with domain decomposition strategies to address these challenges is proposed. Specifically, a domain decomposition-based U-Net (DDU-Net) architecture is introduced, which partitions input images into non-overlapping patches that can be processed independently on separate devices. A communication network is added to facilitate inter-patch information exchange to enhance the understanding of spatial context. Experimental validation is performed on a synthetic dataset that is designed to measure the effectiveness of the communication network. Then, the performance is tested on the DeepGlobe land cover classification dataset as a real-world benchmark data set. The results demonstrate that the approach, which includes inter-patch communication for images divided into $16\times 16$ non-overlapping subimages, achieves a $2-3\%$ higher intersection over union (IoU) score compared to the same network without inter-patch communication. The performance of the network which includes communication is equivalent to that of a baseline U-Net trained on the full image, showing that our model provides an effective solution for segmenting ultra-high-resolution images while preserving spatial context. The code is available at https://github.com/corne00/DDU-Net.

References in corpus (11)

• Adam: A Method for Stochastic Optimization
• Attention U-Net: Learning Where to Look for the Pancreas
• ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data
• U-Net and its variants for medical image segmentation: theory and applications
• DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images
• Mixed Precision Training
• A survey of loss functions for semantic segmentation
• Dense Dilated Convolutions Merging Network for Land Cover Classification
• Medical Image Segmentation Review: The success of U-Net
• Extensive deep neural networks for transferring small scale learning to large scale systems
• High Resolution Medical Image Analysis with Spatial Partitioning