Affiliations: [a] Department of Electronic Engineering, Fudan University, Shanghai, China | [b] Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China | [c] Department of Radiology, Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
Correspondence:
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Corresponding authors: Yi Guo and Yuanyuan Wang, Department of Electronic Engineering, Fudan University, Shanghai 200433, China. Email: guoyi@fudan.edu.cn and yywang@fudan.edu.cn.
Abstract: BACKGROUND: The incidence of liver tumors is among the top three in China. The treatments of benign and malignant tumors are different. Accurate diagnosis plays an important role in guiding the treatment of tumors. OBJECTIVE: The aim of this study is to solve the following: (1) blurred boundary between the liver tumor and other organs causes incorrect segmentation of liver tumor boundaries; (2) large difference in tumor size and the diversity in texture and grayscale are major challenges in liver tumor classification tasks. METHODS: Firstly, the liver tumor is segmented from the original CT images by a tumor segmentation network, UNet++ with fusion loss and atrous spatial pyramid pooling (FLAS-UNet++). The proposed segmentation method can solve the problem of tumor edge segmentation error by learning the tumor edge information. Secondly they are adaptively cropped according to the tumor volume to reduce the over-fitting and over-sensitivity of the deep network. Thirdly an improved Dense Block is designed to pay more attention to the changes in grayscale and texture between benign and malignant tumors. Finally, the features extracted from the network combined with tumor volume, patient’s sex and age, are sent to a classifier for diagnosis. RESULT: Liver tumor segmentation results show that the dice, HD95 reached 71.9%, 12.1 mm, respectively. The classification results show that the accuracy, specificity, sensitivity and area under curve reached 82.4%, 79.8%, 84.4%, 87.5%, respectively. The segmentation and classification results are both better than other’s methods and mainstream networks. CONCLUSIONS: In order to solve existing problems of liver tumor CT image classification methods, our method realizes the accurate segmentation and classification of liver tumors in CT images and has important clinical application value.
