Using deep domain adaptation method to improve liver segmentation performance in Limited MRI images

In clinical practice, it is too expensive to collect large-scale labeled Magnetic Resonance Imaging liver scans which constrains the segmentation performance. However, we noted that plenty of labeled Computed Tomography datasets aimed at liver have been published. Inspired by this, we proposed a deep domain adaptation method which can exploit the published CT datasets to improve the segmentation performance on MRI images. Our experiments showed that the liver segmentation performance is boosted on limited labeled MRI images (20 cases). Lastly, our method achieved competitive performance on both modality images. This work will be benefit to computer-aided diagnosis and treatment planning.