HSN: Hybrid Segmentation Network for Small Cell Lung Cancer Segmentation

Abstract

Small cell lung cancer (SCLC) is one of the most common types of malignant tumors, characterized by rapid growth and early metastasis spread. Early and accurate diagnosis of SCLC is vital for improved survival. Accurate cancer segmentation helps doctors understand the location and size of cancer and make better diagnostic decisions. However, manual segmentation of lung cancers from large amounts of medical images is a time-consuming and challenging task. In this paper, we propose a hybrid segmentation network (referred to as HSN) based on convolutional neural network (CNN) to automatically segment SCLC from computed tomography (CT) images. The design philosophy of our model is to combine a lightweight 3D CNN to learn long-range 3D contextual information and a 2D CNN to learn fine-grained semantic information, which is essential for accurate cancer segmentation. We propose a hybrid features fusion module to effectively fuse the 2D and 3D features and to jointly train these two CNNs. We utilize a generalized Dice loss function to tackle the severe class imbalance problem in data. A dataset consists of 134 CT scans was constructed to evaluate our model. Our model achieved high performances with a mean Dice score of 0.888, a mean sensitivity score of 0.872 and a mean precision of 0.909, outperforming the other state-of-the-art 2D and 3D CNN methods by a large margin. INDEX TERMS Small cell lung cancer, CT, deep convolutional neural network, hybrid features fusion. I. INTRODUCTION Lung cancer is one of the most common types of malignant tumors in the world. It is the second most common cancer among both men and women in the United States [1], the first most common cancer among men and the second most common cancer among women in China [2]. Moreover, lung cancer is the leading cause of cancer death among both men and women in both countries. Small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) are the two main types of lung cancers. SCLC accounts for 15-20% of all lung cancer cases. Compared with NSCLC, SCLC is characterized by high malignancy, rapid progress, early metastasis spread, and poor prognosis, and has a severe impact on the physical and mental health of patients [3]. SCLC can be staged into two categories: limited stage and extensive stage. Being at a specific stage indicates how much cancer has spread through The associate editor coordinating the review of this manuscript and approving it for publication was Shovan Barma.

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