Efficient-PSP-Net: A Lightweight Model for the Segmentation of Liver and Tumor in CT Scans
Chitimireddy Sindhura, Harsha Vardhan Reddy Vengama, Subrahmanyam Gorthi · 2022
The segmentation of the liver and tumors present in the liver is an essential task for the diagnosis of liver cancer and surgical treatment planning. While most of the works were developed with the goal of improving segmentation accuracy, there is a high demand for the development of lightweight architectures to deploy in clinical settings with limited computational resources. Developing such resource-efficient models while maintaining the performance of the state-of-the-art architectures is challenging due to the high class imbalance and small observable changes between tumorous and non-tumorous regions, which would generally require extensive and complex deep learning architectures. The contributions of this paper are twofold; First, this paper proposes to encode information extracted at different scales using a “Pyramid Pooling Module” (PPM) architecture. While different variants of pyramidal architectures are widely used in the context of scene parsing in computer vision, this paper explores the use of the PPM architecture for the segmentation of liver and tumor. Second, the paper proposes using the popular lightweight “Efficient-Net” as the backbone network of PSP-Net for feature extraction, thereby reducing the overall model size while achieving relatively similar performance to state-of-the-art architectures. The performance of the proposed model is evaluated on the MICCAI 2017 LiTS dataset. The model achieved a DSC of 95.06% and 79.08% for liver and tumor, respectively, with only 1.76M parameters.