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

Abstract

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 we collected. The BD loss we proposed can effectively mitigate the adverse effects of data imbalance on the segmentation model. Our proposed method can achieve a dice per case (DPC) of 0.819 (95%CI 0.798–0.841), significantly higher than baseline which achieve a DPC of 0.768(95%CI 0.740–0.796).

Conclusions

The differences in CT images between different types of liver cancer necessitate deep learning models to learn distinct features. Our method addresses this challenge, enabling balanced and accurate segmentation performance across multiple types of liver cancer.