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.