Segmentation for regions of interest in radiotherapy by self-supervised learning

摘要

Segmentation of regions of interest (ROIs) is crucial in radiotherapy, which is a time-consuming and labor-intensive work performed manually by oncologists. In the addressing of this challenge, training deep neural networks (DNNs) with a large quantity of labeled data has delivered promising results. Yet, perfectly-sized and carefully-labeled datasets for model training are typically expensive to acquire. This potential limitation has critically restrained the wide application of DNNs-based segmentation methods in clinical practice. Self-supervised learning (SSL) that utilizing the massive unannotated dataset by pretext task provides a possible solution to this limitation. Currently, existing SSL related methods mainly aim at natural images, which take a less obvious advantage of the characteristics of medical images. In this paper, a novel SSL-based approach is proposed to explore the property of computed tomography (CT) image features is proposed. To give the supervised signal, the spatial distance between CT pairs is utilized to develop a new pretext task, which is based on the tomography characteristic and can be easily acquired from DICOM data based on their tomography characteristic. Models pretrained using the proposed method can be transferred to downstream tasks with significantly alleviated dependency on the annotated dataset. Multiple segmentation and classification tasks are carried out to evaluate the effectiveness of the proposed method. Empirical results demonstrate that the proposed method achieves superior performance over the ImageNet pretrained model and prevalent SSL methods.