Semi-supervised segmentation of orbit in CT images with paired copy-paste strategy

Abstract

The segmentation of the orbit in computed tomography (CT) images plays a crucial role in facilitating the quantitative analysis of orbital decompression surgery for patients with Thyroid-associated Ophthalmopathy (TAO). However, the task of orbit segmentation, particularly in postoperative images, remains challenging due to the significant shape variation and limited amount of labeled data. In this paper, we present a two-stage semi-supervised framework for the automatic segmentation of the orbit in both preoperative and postoperative images, which consists of a pseudo-label generation stage and a semi-supervised segmentation stage. A Paired Copy-Paste strategy is concurrently introduced to proficiently amalgamate features extracted from both preoperative and postoperative images, thereby augmenting the network discriminative capability in discerning changes within orbital boundaries. More specifically, we employ a random cropping technique to transfer regions from labeled preoperative images (foreground) onto unlabeled postoperative images (background), as well as unlabeled preoperative images (foreground) onto labeled postoperative images (background). It is imperative to acknowledge that every set of preoperative and postoperative images belongs to the identical patient. The semi-supervised segmentation network (stage 2) utilizes a combination of mixed supervisory signals from pseudo labels (stage 1) and ground truth to process the two mixed images. The training and testing of the proposed method have been conducted on the CT dataset obtained from the Eye Hospital of Wenzhou Medical University. The experimental results demonstrate that the proposed method achieves a mean Dice similarity coefficient (DSC) of 91.92% with only 5% labeled data, surpassing the performance of the current state-of-the-art method by 2.4%.