Lesion segmentation method for multiple types of liver cancer based on balanced dice loss

作者：Jun Xie, Jiajun Zhou, Meiyi Yang, Lifeng Xu, Tongtong Li, Haoyang Jia, Yu Gong, Xue‐Lei Li, Bin Song, Yi Wei, Ming Liu · 发表于：Medical Physics · 年份：2025 · DOI：10.1002/mp.17624 · 被引用次数：5 · 研究领域：Advanced Neural Network Applications、AI in cancer detection、Brain Tumor Detection and Classification

BACKGROUND: Obtaining accurate segmentation regions for liver cancer is of paramount importance for the clinical diagnosis and treatment of the disease. In recent years, a large number of variants of deep learning based liver cancer segmentation methods have been proposed to assist radiologists. Due to the differences in characteristics between different types of liver tumors and data imbalance, it is difficult to train a deep model that can achieve accurate segmentation for multiple types of liver cancer. PURPOSE: In this paper, We propose a balance Dice Loss(BD Loss) function for balanced learning of multiple categories segmentation features. We also introduce a comprehensive method based on BD Loss to achieve accurate segmentation of multiple categories of liver cancer. MATERIALS AND METHODS: We retrospectively collected computed tomography (CT) screening images and tumor segmentation of 591 patients with malignant liver tumors from West China Hospital of Sichuan University. We use the proposed BD Loss to train a deep model that can segment multiple types of liver tumors and, through a greedy parameter averaging algorithm (GPA algorithm) obtain a more generalized segmentation model. Finally, we employ model integration and our proposed post-processing method, which leverages inter-slice information, to achieve more accurate segmentation of liver cancer lesions. RESULTS: We evaluated the performance of our proposed automatic liver cancer segmentation method on the dataset w...