SECP-Net: SE-Connection Pyramid Network of Organ At Risk Segmentation for Nasopharyngeal Carcinoma

TL;DR

SECP-Net is a novel deep learning architecture that enhances organ at risk segmentation in CT images of nasopharyngeal carcinoma by effectively capturing global and multi-scale information, especially improving small organ segmentation accuracy.

Contribution

The paper introduces SECP-Net, which incorporates SE-Connection modules and a pyramid structure, along with an auto-context cascade, to improve segmentation performance over existing methods.

Findings

SECP-Net outperforms recent methods on head and neck CT datasets.

Achieves state-of-the-art Dice and Jaccard scores.

Particularly improves segmentation of small organs.

Abstract

Nasopharyngeal carcinoma (NPC) is a kind of malignant tumor. Accurate and automatic segmentation of organs at risk (OAR) of computed tomography (CT) images is clinically significant. In recent years, deep learning models represented by U-Net have been widely applied in medical image segmentation tasks, which can help doctors with reduction of workload and get accurate results more quickly. In OAR segmentation of NPC, the sizes of OAR are variable, especially, some of them are small. Traditional deep neural networks underperform during segmentation due to the lack use of global and multi-size information. This paper proposes a new SE-Connection Pyramid Network (SECP-Net). SECP-Net extracts global and multi-size information flow with se connection (SEC) modules and a pyramid structure of network for improving the segmentation performance, especially that of small organs. SECP-Net also designs an auto-context cascaded network to further improve the segmentation performance. Comparative experiments are conducted between SECP-Net and other recently methods on a dataset with CT images of head and neck. Five-fold cross validation is used to evaluate the performance based on two metrics, i.e., Dice and Jaccard similarity. Experimental results show that SECP-Net can achieve SOTA performance in this challenging task.