Domain Adaptation for Organ Segmentation from Non-Contrast to Contrast Enhanced CT

Artificial intelligence (AI) models are used in medical image processing and analysis tasks like organ segmentation, anomaly detection, image reconstruction, and so on. Most often these models are trained on specific type of source domain images (non-contrast or contrast enhanced, specific field-of-view (FOV), dosage, demography, etc). It is desirable to adapt these models to a different but similar target domain, through unsupervised or semi-supervised learning methods. This paper describes a framework to re-purpose trained organ segmentation models for a target domain on which the model was not trained. The adaptation is proposed using an additional autoencoder network as a post-processing step to improve accuracy of predicted segmentation mask. Unsupervised and semi-supervised versions of adaptation are tested on contrast enhanced Computed Tomography (CT) liver, cardiac and lung exams Experiment results show adaptability of a trained network from non-contrast to contrast enhanced scans with improved accuracy in contrast enhanced volume segmentation. A domain adaptation case study on lung disease exams with bacterial pneumonia or COVID-19 pathology also shows the effectiveness of proposed methodology.