Multi-Domain Image Completion for Random Missing Input Data

doi:10.1109/TMI.2020.3046444

Multi-Domain Image Completion for Random Missing Input Data

2021-04-01

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DOI：10.1109/TMI.2020.3046444

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Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e.g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI). However, due to possible data corruption and different imaging protocols, the availability of images for each domain could vary amongst multiple data sources in practice, which makes it challenging to build a universal model with a varied set of input data. To tackle this problem, we propose a general approach to complete the random missing domain(s) data in real applications. Specifically, we develop a novel multi-domain image completion method that utilizes a generative adversarial network (GAN) with a representational disentanglement scheme to extract shared content encoding and separate style encoding across multiple domains. We further illustrate that the learned representation in multi-domain image completion could be leveraged for high-level tasks, e.g., segmentation, by introducing a unified framework consisting of image completion and segmentation with a shared content encoder. The experiments demonstrate consistent performance improvement on three datasets for brain tumor segmentation, prostate segmentation, and facial expression image completion respectively.

Keywords:

Image segmentation

Tumors

Task analysis

Image synthesis

Magnetic resonance imaging

Image reconstruction

Biomedical imaging

Multi-domain image-to-image translation

multi-contrast MRI

missing data problem

missing-domain segmentation

medical image synthesis

Multi-Domain Image Completion for Random Missing Input Data

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