TITLE: 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 

ABSTRACT: Despite the remarkable success on medical image analysis with deep learning,
it is still under exploration regarding how to rapidly transfer AI models from
one dataset to another for clinical applications. This paper presents a novel
and generic human-in-the-loop scheme for efficiently transferring a
segmentation model from a small-scale labelled dataset to a larger-scale
unlabelled dataset for multi-organ segmentation in CT. To achieve this, we
propose to use an igniter network which can learn from a small-scale labelled
dataset and generate coarse annotations to start the process of human-machine
interaction. Then, we use a sustainer network for our larger-scale dataset, and
iteratively updated it on the new annotated data. Moreover, we propose a
flexible labelling strategy for the annotator to reduce the initial annotation
workload. The model performance and the time cost of annotation in each subject
evaluated on our private dataset are reported and analysed. The results show
that our scheme can not only improve the performance by 19.7% on Dice, but also
expedite the cost time of manual labelling from 13.87 min to 1.51 min per CT
volume during the model transfer, demonstrating the clinical usefulness with
promising potentials.
Despite the remarkable success on medical image analysis with deep learning,
it is still under exploration regarding how to rapidly transfer AI models from
one dataset to another for clinical applications. This paper presents a novel
and generic human-in-the-loop scheme for efficiently transferring a
segmentation model from a small-scale labelled dataset to a larger-scale
unlabelled dataset for multi-organ segmentation in CT. To achieve this, we
propose to use an igniter network which can learn from a small-scale labelled
dataset and generate coarse annotations to start the process of human-machine
interaction. Then, we use a sustainer network for our larger-scale dataset, and
iteratively updated it on the new annotated data. Moreover, we propose a
flexible labelling strategy for the annotator to reduce the initial annotation
workload. The model performance and the time cost of annotation in each subject
evaluated on our private dataset are reported and analysed. The results show
that our scheme can not only improve the performance by 19.7% on Dice, but also
expedite the cost time of manual labelling from 13.87 min to 1.51 min per CT
volume during the model transfer, demonstrating the clinical usefulness with
promising 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
http://arxiv.org/abs/2204.06243v1