An End-to-End Data-Adaptive Pancreas Segmentation System with an Image Quality Control Toolbox

Abstract

With the development of radiology and computer technology, diagnosis by medical imaging is heading toward precision and automation. Due to complex anatomy around the pancreatic tissue and high demands for clinical experience, the assisted pancreas segmentation system will greatly promote clinical efficiency. However, the existing segmentation model suffers from poor generalization among images from multiple hospitals. In this paper, we propose an end-to-end data-adaptive pancreas segmentation system to tackle the problems of lack of annotations and model generalizability. The system employs adversarial learning to transfer features from labeled domains to unlabeled domains, seeking a dynamic balance between domain discrimination and unsupervised segmentation. The image quality control toolbox is embedded in the system, which standardizes image quality in terms of intensity, field of view, and so on, to decrease heterogeneity among image domains. In addition, the system implements a data-adaptive process end-to-end without complex operations by doctors. The experiments are conducted on an annotated public dataset and an unannotated in-hospital dataset. The results indicate that after data adaptation, the segmentation performance measured by the dice similarity coefficient on unlabeled images improves from 58.79% to 75.43%, with a gain of 16.64%. Furthermore, the system preserves quantitatively structured information such as the pancreas' size and volume, as well as objective and accurate visualized images, which assists clinicians in diagnosing and formulating treatment plans in a timely and accurate manner.