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liver segmentation in CT Images Based on DRLSE Model

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Journal of Donghua University(English Edition) 2012年第6期29卷 493-496页

作者：黄永锋齐萌严加勇 College of Computer Science and Technology Donghua University College of Electronics and Information Shanghai Dianji University

liver segmentation in CT images is an important step for liver volumetry and vascular evaluation in liver pre-surgical planning. In this paper, a segmentation method based on distance regularized level set evolution(DRLSE) model was proposed, which incorporated a distance regularization term into the conventional Chan-Vese (C-V) model. In addition, the region growing method was utilized to generate the initial liver mask for each slice, which could decrease the computation time for level-set propagation. The experimental results show that the method can dramatically decrease the evolving time and keep the accuracy of segmentation. The new method is averagely 15 times faster than the method based on conventional C-V model in segmenting a slice.

关键词： liver segmentation distance regularized level set evolution (DRLSE) model Chan-Vese (C-V) model region growing

Empirical Comparisons of Deep Learning Networks on liver segmentation

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Computers, Materials & Continua 2020年第3期62卷 1233-1247页

作者： Yi Shen Victor S.Sheng Lei Wang Jie Duan Xuefeng Xi Dengyong Zhang Ziming Cui School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhouChina Department of Computer Science University of Central ArkansasConwayArkansasUSA Huan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangshaChina

Accurate segmentation of CT images of liver tumors is an important adjunct for the liver diagnosis and treatment of liver *** recent years,due to the great improvement of hard device,many deep learning based methods have been proposed for automatic liver *** them,there are the plain neural network headed by FCN and the residual neural network headed by Resnet,both of which have many *** have achieved certain achievements in medical image *** this paper,we firstly select five representative structures,i.e.,FCN,U-Net,Segnet,Resnet and Densenet,to investigate their performance on liver *** original Resnet and Densenet could not perform image segmentation directly,we make some adjustments for them to perform live *** experimental results show that Densenet performs the best on liver segmentation,followed by *** perform much better than Segnet,U-Net,and *** Segnet,U-Net,and FCN,U-Net performs the best,followed by *** performs the worst.

关键词： liver segmentation deep learning FCN U-Net Segnet Resnet Densenet

Automatic liver and tumor segmentation based on deep learning and globally optimized refinement

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Applied Mathematics(A Journal of Chinese Universities) 2021年第2期36卷 304-316页

作者： HONG Yuan MAO Xiong-wei HUI Qing-lei OUYANG Xiao-ping PENG Zhi-yi KONG De-xing School of Mathematical Science Zhejiang UniversityHangzhou 310058China Department of Radiology Zhejiang University HospitalHangzhou 310058China Department of Radiology The First Afiliated HospitalZhejiang University School of MedicineHangzhou 310003China School of Mechanical Engineering Zhejiang UniversityHangzhou 310058China

Automatic segmentation of the liver and hepatic lesions from abdominal 3D comput-ed tomography(CT)images is fundamental tasks in computer-assisted liver surgery ***,due to complex backgrounds,ambiguous boundaries,heterogeneous appearances and highly varied shapes of the liver,accurate liver segmentation and tumor detection are stil-1 challenging *** address these difficulties,we propose an automatic segmentation framework based on 3D U-net with dense connections and globally optimized ***-ly,a deep U-net architecture with dense connections is trained to learn the probability map of the *** the probability map goes into the following refinement step as the initial surface and prior *** segmentation of liver tumor is based on the similar network architecture with the help of segmentation results of *** order to reduce the infuence of the surrounding tissues with the similar intensity and texture behavior with the tumor region,during the training procedure,I x liverlabel is the input of the network for the segmentation of liver *** do-ing this,the accuracy of segmentation can be *** proposed method is fully automatic without any user *** qualitative and quantitative results reveal that the pro-posed approach is efficient and accurate for liver volume estimation in clinical *** high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and non-reproducible manual segmentation method.

关键词： liver segmentation tumor segmentation CT deep learning

Automatic segmentation of liver from Abdominal Computed Tomography Images Using Energy Feature

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Computers, Materials & Continua 2021年第4期67卷 709-722页

作者： Prabakaran Rajamanickam Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril Raj University Department of Computer Science Engineering Anna UniversityChennai600025India University Department of Information Technology MIT CampusChennai600044India

liver segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography(CT)*** segmentation of hepatic organ is more intricate task,owing to the fact that it possesses a sizeable quantum of *** paper proposes an algorithm for automatic seed point selection using energy feature for use in level set algorithm for segmentation of liver region in CT *** effectiveness of the method can be determined when used in a model to classify the liver CT images as tumorous or *** involves segmentation of the region of interest(ROI)from the segmented liver,extraction of the shape and texture features from the segmented ROI and classification of the ROIs as tumorous or not by using a classifier based on the extracted *** this work,the proposed seed point selection technique has been used in level set algorithm for segmentation of liver region in CT scans and the ROIs have been extracted using Fuzzy C Means clustering(FCM)which is one of the algorithms to segment the *** dataset used in this method has been collected from various repositories and scan *** outcome of this proposed segmentation model has reduced the area overlap error that could offer the intended accuracy and *** gives better results when compared with other existing *** execution in short span of time is another advantage of this method which in turns helps the radiologist to ascertain the abnormalities instantly.

关键词： liver segmentation automatic seed point tumor segmentation classification fuzzy C means clustering

Fuzzy C-means Clustering-based Multilayer Perceptron Neural Network for liver CT Images Automatic segmentation

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Fuzzy C-means Clustering-based Multilayer Perceptron Neural ...

Efficient Computer Aided Diagnosis System for Hepatic Tumors Using Computed Tomography Scans

2010 Chinese Control and Decision Conference

作者： Yuqian Zhao~(1,2),Yunlong Zan~1,Xiaofang Wang~1,Guiyuan Li~2 1.School of Info-Physics & Geomatics Engineering,Central South University,Changsha 410083,China 2.Cancer Research Institute,Central South University,Changsha 410078,China

＜正＞A new liver segmentation algorithm is ***,the threshold method was used to remove the ribs and spines in the initial image,and the fuzzy C-means clustering algorithm and morphological reconstruction filtering were used to segment the initial liver CT *** the multilayer perceptron neural network was trained by the segmentation result of initial image with the back-propagation *** adjacent slice CT image was segmented with the trained multilayer perceptron neural ***,morphological reconstruction filtering was used to smooth the contour of the liver *** experimental results show that the proposed algorithm can effectively segment the livers from CT images,despite the gray level similarity of adjacent organs and different gray level of tumors in the liver.

关键词： liver segmentation Fuzzy C-means Algorithm Multilayer Perceptron Neural Network Morphological Reconstruction Filtering

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Computers, Materials & Continua 2022年第6期71卷 4871-4894页

作者： Yasmeen Al-Saeed Wael A.Gab-Allah Hassan Soliman Maysoon F.Abulkhair Wafaa M.Shalash Mohammed Elmogy Faculty of Computers and Information Mansoura UniversityMansoura35516Egypt Faculty of Computers&Artificial Intelligence South Valley UniversityHurghada84511Egypt Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBanha13511Egypt

One of the leading causes of mortality worldwide is liver *** earlier the detection of hepatic tumors,the lower the mortality *** paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign *** hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range,intensity values overlap between the liver and neighboring organs,high noise from computed tomography scanner,and large variance in tumors *** proposed method consists of three main stages;liver segmentation using Fast Generalized Fuzzy C-Means,tumor segmentation using dynamic thresholding,and the tumor’s classification into malignant/benign using support vector machines *** performance of the proposed system was evaluated using three liver benchmark datasets,which are MICCAI-Sliver07,LiTS17,and *** proposed computer adided diagnosis system achieved an average accuracy of 96.75%,sensetivity of 96.38%,specificity of 95.20%and Dice similarity coefficient of 95.13%.

关键词： liver tumor hepatic tumors diagnosis CT scans analysis liver segmentation tumor segmentation features extraction tumors classification FGFCM CAD system