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Despite the remarkable success on medical image analysis with deep learning,it is still under exploration regarding how to rapidly transfer AI models fromone dataset to another for clinical applications. This paper presents a noveland generic human-in-the-loop scheme for efficiently transferring asegmentation model from a small-scale labelled dataset to a larger-scaleunlabelled dataset for multi-organ segmentation in CT. To achieve this, wepropose to use an igniter network which can learn from a small-scale labelleddataset and generate coarse annotations to start the process of human-machineinteraction. Then, we use a sustainer network for our larger-scale dataset, anditeratively updated it on the new annotated data. Moreover, we propose aflexible labelling strategy for the annotator to reduce the initial annotationworkload. The model performance and the time cost of annotation in each subjectevaluated on our private dataset are reported and analysed. The results showthat our scheme can not only improve the performance by 19.7% on Dice, but alsoexpedite the cost time of manual labelling from 13.87 min to 1.51 min per CTvolume during the model transfer, demonstrating the clinical usefulness withpromising potentials.

Rapid model transfer for medical image segmentation via iterative  human-in-the-loop update: from labelled public to unlabelled clinical  datasets for multi-organ segmentation in CT