TITLE:
Automatic Segmentation of Liver Tumor in CT Images with Deep Convolutional Neural Networks
AUTHORS:
Wen Li, Fucang Jia, Qingmao Hu
KEYWORDS:
Liver Tumor Segmentation, Convolutional Neural Networks, Deep Learning, CT Image
JOURNAL NAME:
Journal of Computer and Communications,
Vol.3 No.11,
November
19,
2015
ABSTRACT: Liver tumors segmentation from
computed tomography (CT) images is an essential task for diagnosis and
treatments of liver cancer. However, it is difficult owing to the variability
of appearances, fuzzy boundaries, heterogeneous densities, shapes and sizes of
lesions. In this paper, an automatic method based on convolutional neural
networks (CNNs) is presented to segment lesions from CT images. The CNNs is one
of deep learning models with some convolutional filters which can learn
hierarchical features from data. We compared the CNNs model to popular machine
learning algorithms: AdaBoost, Random Forests (RF), and support vector machine
(SVM). These classifiers were trained by handcrafted features containing mean,
variance, and contextual features. Experimental evaluation was performed on 30
portal phase enhanced CT images using leave-one-out cross validation. The
average Dice Similarity Coefficient (DSC), precision, and recall achieved of
80.06% ± 1.63%, 82.67% ± 1.43%, and 84.34% ± 1.61%, respectively. The results
show that the CNNs method has better performance than other methods and is
promising in liver tumor segmentation.