Abstract

Info

Title:: MRFA-Net: a lung cancer CT image segmentation network based on multi-scale residual feature mining

Author(s):: ZHANG Benteng1; WANG Raofen1; HU Lingyan1; WANG Hailing1; GONG Xiaomei2; 1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China 2. Department of Radiation Therapy, Shanghai Pulmonary Hospital Affiliated to Tongji University, Shanghai 200433, China

Keywords:: Keywords: lung tumor segmentation residual feature extraction deep pyramid aggregation reverse attention mechanism

Abstract:: Abstract: Lung tumor segmentation from medical images is crucial for supporting diagnosis and treatment planning. However, tumors exhibit substantial heterogeneity in shape, contour and size, which makes precise segmentation still challenging. Therefore, this paper proposes a deep segmentation network named MRFA-Net (multi-scale residual feature aggregation network). Built upon residual feature mining and deep feature fusion, the network aims to improve the automatic segmentation performance of lung tumors on CT images. Specifically, two key modules are designed: the multi-scale residual feature extraction module and the deep pyramid aggregation module. By extracting residual features at multiple scales and fusing deep features, the network can capture representative tumor characteristics more effectively. Additionally, a reverse attention mechanism is integrated into skip connections to enhance the models perception of critical regions. Experimental results on the public MSD dataset and a private lung cancer CT dataset demonstrate that MRFA-Net achieves excellent performance, with a Dice similarity coefficient of 74.15% and 74.77%, respectively. The proposed method provides an effective solution for lung tumor segmentation and holds promising prospects for clinical practice.