Liver tumor segmentation in CT volumes using an adversarial densely connected network

Lei Chen,Jian Yang,Chi Wang,Le Zhang,Xiaohua Hu,Yutao Cui,Hong Song

doi:10.1186/s12859-019-3069-x

Abstract

BackgroundMalignant liver tumor is one of the main causes of human death. In order to help physician better diagnose and make personalized treatment schemes, in clinical practice, it is often necessary to segment and visualize the liver tumor from abdominal computed tomography images. Due to the large number of slices in computed tomography sequence, developing an automatic and reliable segmentation method is very favored by physicians. However, because of the noise existed in the scan sequence and the similar pixel intensity of liver tumors with their surrounding tissues, besides, the size, position and shape of tumors also vary from one patient to another, automatic liver tumor segmentation is still a difficult task.ResultsWe perform the proposed algorithm to the Liver Tumor Segmentation Challenge dataset and evaluate the segmentation results. Experimental results reveal that the proposed method achieved an average Dice score of 68.4% for tumor segmentation by using the designed network, and ASD, MSD, VOE and RVD improved from 27.8 to 21, 147 to 124, 0.52 to 0.46 and 0.69 to 0.73, respectively after performing adversarial training strategy, which proved the effectiveness of the proposed method.ConclusionsThe testing results show that the proposed method achieves improved performance, which corroborated the adversarial training based strategy can achieve more accurate and robustness results on liver tumor segmentation task.

Highlights

Malignant liver tumor is one of the main causes of human death
Quantitative evaluations criteria We provided quantitative measurements including Dice score, average symmetric surface distance (ASD), mean square symmetric surface distance (MSD), relative volume difference (RVD) and volumetric overlap error (VOE) to evaluate the effectiveness of our proposed model
The liver tumor segmentation challenge was divided into a two cascade binary segmentation tasks and we designed two networks to segment the liver and liver tumor, respectively

Summary

Introduction

Malignant liver tumor is one of the main causes of human death. In order to help physician better diagnose and make personalized treatment schemes, in clinical practice, it is often necessary to segment and visualize the liver tumor from abdominal computed tomography images. During the past few decades, they mainly focused on developing algorithms such as level set, watershed, statistical shape model, region growing, active contour model, threshold processing, graph cuts and traditional machine learning methods that require manually extract tumor features. Traditional machine learning based image segmentation methods have played an active role in liver tumor segmentation scenarios Most of these methods need to manually design tumor feature extraction methods, and developing a model to trained the features, making the model has the ability to identify tumor pixels. Zhou et al [9] proposed support vector machine (SVM) based method for tumor segmentation They first trained a SVM model to segment tumor region from a single slice and extracted its contour through morphological operation. Most machine learning based methods can achieve better performance than traditional ones, but they are still difficult to learn the accurate tumor feature and susceptible to data fluctuations

Methods

Results

Conclusion