Convolutional-Neural-Network-Based Feature Extraction for Liver Segmentation from CT Images

被引:20
作者
Ahmad, Mubashir [1 ]
Ding, Yuan [1 ]
Qadri, Syed Furqan [2 ]
Yang, Jian [1 ]
机构
[1] Beijing Inst Technol, Sch Opt & Photon, Beijing Engn Res Ctr Mixed Real & Adv Display, Beijing 100081, Peoples R China
[2] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China
来源
ELEVENTH INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING (ICDIP 2019) | 2019年 / 11179卷
基金
国家重点研发计划; 美国国家科学基金会;
关键词
Convolutional Neural Network (CNN); Deep Learning; Liver Segmentation;
D O I
10.1117/12.2540175
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Over the last few years, major breakthroughs were achieved in the application of deep learning in many computer vision tasks, such as image classification and segmentation. The automatic liver segmentation from CT images has become an important area in clinical research, including radiotherapy, liver volume measurement, and liver transplant surgery. This paper proposes a novel convolutional neural network for liver segmentation (CNN-LivSeg) algorithm that involves three convolutional (each convolutional layer followed by max-pooling layer) and two fully connected layers with a final 2-way softmax is used for liver discrimination. The weight initialization is based on a random Gaussian, which performed a distance preserving-embedding of the data. To avoid using the fully 3D CNN network which is computationally expensive and time-consuming, 2D patches were extracted and processed for segmentation. Experiments were performed on MICCAI-SLiver07 as a benchmark dataset. The mean ratios of Dice similarity coefficient, Jaccard similarity index, accuracy, specificity, and sensitivity were 0.9541, 0.9122, 0.9725, 0.9904, and 0.9652, respectively, thereby suggesting that the proposed method performed well on the test images.
引用
收藏
页数:7
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