21.8.3.2 Liver Disease, Tomography, CAT Analysis

Yan, Z., Chen, F., Kong, D.,
Liver Venous Tree Separation via Twin-Line RANSAC and Murray's Law,
MedImg(36), No. 9, September 2017, pp. 1887-1900.
IEEE DOI 1709
blood vessels, computerised tomography, diagnostic radiography, liver, abdominal CT angiography, hepatic surgery, Murray's Law BibRef

Delavari, M.[Mahdi], Foruzan, A.H.[Amir Hossein],
Anatomical decomposition of human liver volume to build accurate statistical shape models,
SIViP(12), No. 2, February 2018, pp. 331-338.
WWW Link. 1802
BibRef

Liu, H.[Hui], Tang, P.[Pinpin], Guo, D.M.[Dong-Mei], Liu, H.X.[Hai-Xia], Zheng, Y.J.[Yuan-Jie], Dan, G.[Guo],
Liver MRI segmentation with edge-preserved intensity inhomogeneity correction,
SIViP(12), No. 4, May 2018, pp. 791-798.
Springer DOI 1805
BibRef

Gloger, O.[Oliver], Tönnies, K.[Klaus],
Subject-Specific prior shape knowledge in feature-oriented probability maps for fully automatized liver segmentation in MR volume data,
PR(84), 2018, pp. 288-300.
Elsevier DOI 1809
Expectation maximization, Subject-specific shape model, 3D prior shape level set segmentation, Bayesian probability, Principal component analysis BibRef

Zhao, J.W.[Jing-Wen], Wang, S.H.[Shuo Hong], Liu, X.[Xiang], Liu, Y.[Ye], Chen, Y.Q.[Yan Qiu],
Early diagnosis of cirrhosis via automatic location and geometric description of liver capsule,
VC(34), No. 12, December 2018, pp. 1677-1689.
WWW Link. 1811
BibRef

Balagourouchetty, L.[Lakshmipriya], Pragatheeswaran, J.K.[Jayanthi K.], Pottakkat, B.[Biju], Govindarajalou, R.[Ramkumar],
Enhancement approach for liver lesion diagnosis using unenhanced CT images,
IET-CV(12), No. 8, December 2018, pp. 1078-1087.
DOI Link 1812
BibRef

Li, X., Chen, H., Qi, X., Dou, Q., Fu, C., Heng, P.,
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes,
MedImg(37), No. 12, December 2018, pp. 2663-2674.
IEEE DOI 1812
Liver, Image segmentation, Feature extraction, Lesions, CT, hybrid features BibRef

Parent, F., Gérard, M., Monet, F., Loranger, S., Soulez, G., Kashyap, R., Kadoury, S.,
Intra-Arterial Image Guidance With Optical Frequency Domain Reflectometry Shape Sensing,
MedImg(38), No. 2, February 2019, pp. 482-492.
IEEE DOI 1902
Catheters, Shape, Optical sensors, Arteries, Strain, Liver cancer, intra-arterial therapies, curvature matching BibRef

Sreeja, P., Hariharan, S.,
Three-dimensional fusion of clustered and classified features for enhancement of liver and lesions from abdominal radiology images,
IET-IPR(13), No. 10, 22 August 2019, pp. 1680-1685.
DOI Link 1909
BibRef

Mirasadi, M.S.[Mansoureh Sadat], Foruzan, A.H.[Amir Hossein],
Content-based medical image retrieval of CT images of liver lesions using manifold learning,
MultInfoRetr(8), No. 4, December 2019, pp. 233-240.
WWW Link. 1912
BibRef

Renukadevi, T.[Thangavel], Karunakaran, S.[Saminathan],
Optimizing deep belief network parameters using grasshopper algorithm for liver disease classification,
IJIST(30), No. 1, 2020, pp. 168-184.
DOI Link 2002
deep belief network (DBN), grasshopper optimization algorithm (GOA), principal component analysis (PCA) BibRef

Wang, J.[Jian], Li, J.[Jing], Han, X.H.[Xian-Hua], Lin, L.F.[Lan-Fen], Hu, H.J.[Hong-Jie], Xu, Y.Y.[Ying-Ying], Chen, Q.Q.[Qing-Qing], Iwamoto, Y.T.[Yu-Taro], Chen, Y.W.[Yen-Wei],
Tensor-based sparse representations of multi-phase medical images for classification of focal liver lesions,
PRL(130), 2020, pp. 207-215.
Elsevier DOI 2002
Multi-phase CT, Tensor analysis, Sparse coding, Image classification, Focal liver lesion BibRef

Seo, H., Huang, C., Bassenne, M., Xiao, R., Xing, L.,
Modified U-Net (mU-Net) With Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images,
MedImg(39), No. 5, May 2020, pp. 1316-1325.
IEEE DOI 2005
Feature extraction, Liver, Convolution, Image segmentation, Tumors, Data mining, Biomedical imaging, Frequency analysis, deep learning, U-Net BibRef

Liang, L., Cool, D., Kakani, N., Wang, G., Ding, H., Fenster, A.,
Automatic Radiofrequency Ablation Planning for Liver Tumors With Multiple Constraints Based on Set Covering,
MedImg(39), No. 5, May 2020, pp. 1459-1471.
IEEE DOI 2005
Radiofrequency ablation, treatment planning, set cover, liver tumors BibRef

Daniel, V.A.A.[V. Antony Asir], Ravi, R.,
Noninvasive methods of classification and staging of chronic hepatic diseases,
IJIST(30), No. 2, 2020, pp. 358-366.
DOI Link 2005
chronic hepatic disease, chronic viral hepatitis, cirrhosis, hepatology, liver disease, noninvasive, ultrasound transducer BibRef

Sinduja, A., Suruliandi, A., Raja, S.P.,
Empirical Evaluation of Texture Features and Classifiers for Liver Disease Diagnosis,
IJIG(20), No. 2, April 2020, pp. 2050015.
DOI Link 2005
BibRef

Heiselman, J.S., Jarnagin, W.R., Miga, M.I.,
Intraoperative Correction of Liver Deformation Using Sparse Surface and Vascular Features via Linearized Iterative Boundary Reconstruction,
MedImg(39), No. 6, June 2020, pp. 2223-2234.
IEEE DOI 2006
Deformation, image guided surgery, liver, registration, ultrasound BibRef

Heiselman, J.S., Miga, M.I.,
Strain Energy Decay Predicts Elastic Registration Accuracy From Intraoperative Data Constraints,
MedImg(40), No. 4, April 2021, pp. 1290-1302.
IEEE DOI 2104
Strain, Uncertainty, Boundary conditions, Mathematical model, Tensors, Predictive models, Graphical models, Accuracy, deformation, uncertainty BibRef

Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.,
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation,
MedImg(39), No. 6, June 2020, pp. 1856-1867.
IEEE DOI 2006
Neuronal structure segmentation, liver segmentation, cell segmentation, nuclei segmentation, brain tumor segmentation, model pruning BibRef

Pan, J.H.[Jia-Hui], Zhang, J.H.[Jian-Hao], Luo, S.Q.[Si-Qi], Zhang, J.T.[Jian-Tao], Liang, Y.[Yan],
Automatic annotation of liver computed tomography images based on a vessel-skeletonization method,
IJIST(30), No. 3, 2020, pp. 704-715.
DOI Link 2008
automatic, CT image, liver annotation, liver segment, vessel skeletonization BibRef

Zhang, F., Dvornek, N., Yang, J., Chapiro, J., Duncan, J.,
Layer Embedding Analysis in Convolutional Neural Networks for Improved Probability Calibration and Classification,
MedImg(39), No. 11, November 2020, pp. 3331-3342.
IEEE DOI 2011
Calibration, Liver, Task analysis, Computational modeling, Convolutional neural networks, Predictive models, liver tissue classification BibRef

Mahdy, L.N.[Lamia N.], Ezzat, K.A.[Kadry A.], Torad, M.[Mohamed], Hassanien, A.E.[Aboul E.],
Automatic segmentation system for liver tumors based on the multilevel thresholding and electromagnetism optimization algorithm,
IJIST(30), No. 4, 2020, pp. 1256-1270.
DOI Link 2011
liver, multilevel thresholding, optimization, Otsu, tumor segmentation BibRef

Wang, S., Cao, S., Chai, Z., Wei, D., Ma, K., Wang, L., Zheng, Y.,
Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network,
MedImg(39), No. 12, December 2020, pp. 4174-4185.
IEEE DOI 2012
Liver, Tumors, Computed tomography, Decoding, deep learning BibRef

Mourya, G.K.[Gajendra Kumar], Bhatia, D.[Dinesh], Handique, A.[Akash],
Empirical greedy machine-based automatic liver segmentation in CT images,
IET-IPR(14), No. 14, December 2020, pp. 3333-3340.
DOI Link 2012
BibRef

Zeng, Q., Honarvar, M., Schneider, C., Mohammad, S.K., Lobo, J., Pang, E.H.T., Lau, K.T., Hu, C., Jago, J., Erb, S.R., Rohling, R., Salcudean, S.E.,
Three-Dimensional Multi-Frequency Shear Wave Absolute Vibro-Elastography (3D S-WAVE) With a Matrix Array Transducer: Implementation and Preliminary In Vivo Study of the Liver,
MedImg(40), No. 2, February 2021, pp. 648-660.
IEEE DOI 2102
Liver, Transducers, Elastography, Elasticity, Ultrasound, liver fibrosis BibRef

Stähli, P., Frenz, M., Jaeger, M.,
Bayesian Approach for a Robust Speed-of-Sound Reconstruction Using Pulse-Echo Ultrasound,
MedImg(40), No. 2, February 2021, pp. 457-467.
IEEE DOI 2102
Imaging, Image reconstruction, Graphical models, Distribution functions, Liver, Ultrasonic imaging, Phase noise, inverse problem BibRef

Ali, S.[Safdar], Hassan, M.[Mehdi], Saleem, M.[Muhammad], Tahir, S.F.[Syed Fahad],
Deep transfer learning based hepatitis B virus diagnosis using spectroscopic images,
IJIST(31), No. 1, 2021, pp. 94-105.
DOI Link 2102
blood plasma, deep learning, disease diagnosis, HBV infection, Raman spectroscopy, transfer learning BibRef

Ramalhinho, J., Tregidgo, H.F.J., Gurusamy, K., Hawkes, D.J., Davidson, B., Clarkson, M.J.,
Registration of Untracked 2D Laparoscopic Ultrasound to CT Images of the Liver Using Multi-Labelled Content-Based Image Retrieval,
MedImg(40), No. 3, March 2021, pp. 1042-1054.
IEEE DOI 2103
Computed tomography, Liver, Probes, Veins, Laparoscopes, Surgery, Image retrieval, Multi-modal registration, content-based image retrieval BibRef

Rela, M.[Munipraveena], Rao, S.N.[Suryakari Nagaraja], Reddy, P.R.[Patil Ramana],
Optimized segmentation and classification for liver tumor segmentation and classification using opposition-based spotted hyena optimization,
IJIST(31), No. 2, 2021, pp. 627-656.
DOI Link 2105
abdominal CT images, and convolutional neural network, fuzzy centroid-based optimized region growing algorithm, recurrent neural network BibRef

Gunasekhar, P., Vijayalakshmi, S.,
Analysis on segmentation and biomarker-based approaches for liver cancer detection: A survey,
IET-IPR(15), No. 4, 2021, pp. 845-855.
DOI Link 2106
BibRef

Navaneethakrishnan, M.[Mariappan], Vairamuthu, S.[Subbiah], Parthasarathy, G.[Govindaswamy], Cristin, R.[Rajan],
Atom search-Jaya-based deep recurrent neural network for liver cancer detection,
IET-IPR(15), No. 2, 2021, pp. 337-349.
DOI Link 2106
BibRef

Siri, S.K.[Sangeeta K.], Kumar, S.P.[S. Pramod], Latte, M.V.[Mrityunjaya V.],
Accurate Liver Border Identification Model in CT Scan Images,
IJIG(21), No. 3, July 2021, pp. 2150039.
DOI Link 2107
BibRef

Krishnamurthy, R.K.[Raghesh Krishnan], Radhakrishnan, S.[Sudhakar], Kattuva, M.A.K.[Mohaideen Abdul Kadhar],
Particle swarm optimization-based liver disorder ultrasound image classification using multi-level and multi-domain features,
IJIST(31), No. 3, 2021, pp. 1366-1385.
DOI Link 2108
biomedical image classification, fractals, particle swarm optimization, segmentation, texture features, wavelets BibRef

Choi, C.[Changhoon], Choi, W.[Wonseok], Kim, J.[Jeesu], Kim, C.[Chulhong],
Non-Invasive Photothermal Strain Imaging of Non-Alcoholic Fatty Liver Disease in Live Animals,
MedImg(40), No. 9, September 2021, pp. 2487-2495.
IEEE DOI 2109
Silicides, Platinum alloys, Imaging, Strain, Heating systems, Fats, Laser beams, Photothermal strain imaging, preclinical research, tissue characterization BibRef

Xue, Z.L.[Zhong-Liang], Li, P.[Ping], Zhang, L.[Liang], Lu, X.Y.[Xiao-Yuan], Zhu, G.M.[Guang-Ming], Shen, P.Y.[Pei-Yi], Shah, S.A.A.[Syed Afaq Ali], Bennamoun, M.[Mohammed],
Multi-Modal Co-Learning for Liver Lesion Segmentation on PET-CT Images,
MedImg(40), No. 12, December 2021, pp. 3531-3542.
IEEE DOI 2112
Lesions, Computed tomography, Image segmentation, Liver, Feature extraction, Imaging, Task analysis, PET-CT BibRef

Wu, Y.L.[Yan-Lin], Wang, G.L.[Guang-Lei], Wang, Z.Y.[Zhong-Yang], Wang, H.R.[Hong-Rui],
PCAF-Net: A liver segmentation network based on deep learning,
IET-IPR(16), No. 1, 2022, pp. 229-238.
DOI Link 2112
BibRef

Tummala, B.M.[Bindu Madhavi], Barpanda, S.S.[Soubhagya Sankar],
Liver tumor segmentation from computed tomography images using multiscale residual dilated encoder-decoder network,
IJIST(32), No. 2, 2022, pp. 600-613.
DOI Link 2203
dilated convolutions, encoder-decoder architecture, liver tumor segmentation, medical imaging, semantic segmentation BibRef

Arulappan, A.[Anisha], Thankaraj, A.B.R.[Ajith Bosco Raj],
Liver tumor segmentation using a new asymmetrical dilated convolutional semantic segmentation network in CT images,
IJIST(32), No. 3, 2022, pp. 815-830.
DOI Link 2205
CNN, dilated convolutions, liver segmentation, transposed convolutions, tumor segmentation BibRef

Lyu, F.[Fei], Ma, A.J.[Andy J.], Yip, T.C.F.[Terry Cheuk-Fung], Wong, G.L.H.[Grace Lai-Hung], Yuen, P.C.[Pong C.],
Weakly Supervised Liver Tumor Segmentation Using Couinaud Segment Annotation,
MedImg(41), No. 5, May 2022, pp. 1138-1149.
IEEE DOI 2205
Tumors, Image segmentation, Liver, Annotations, Training, Biomedical imaging, Pathology, Liver tumor segmentation, Couinaud segment BibRef

Affane, A.[Abir], Lebre, M.A.[Marie-Ange], Mittal, U.[Utkarsh], Vacavant, A.[Antoine],
Literature Review of Deep Learning Models for Liver Vessels Reconstruction,
IPTA20(1-6)
IEEE DOI 2206
Deep learning, Image segmentation, Shape, Bibliographies, Liver, Topology, Image reconstruction, Deep learning, SLR BibRef

Lyu, F.[Fei], Ye, M.[Mang], Ma, A.J.[Andy J.], Yip, T.C.F.[Terry Cheuk-Fung], Wong, G.L.H.[Grace Lai-Hung], Yuen, P.C.[Pong C.],
Learning From Synthetic CT Images via Test-Time Training for Liver Tumor Segmentation,
MedImg(41), No. 9, September 2022, pp. 2510-2520.
IEEE DOI 2209
Task analysis, Tumors, Training, Image segmentation, Liver, Image reconstruction, Adaptation models, test-time training BibRef

Zheng, R.C.[Ren-Cheng], Wang, Q.D.[Qi-Dong], Lv, S.Z.[Shuang-Zhi], Li, C.P.[Cui-Ping], Wang, C.Y.[Cheng-Yan], Chen, W.[Weibo], Wang, H.[He],
Automatic Liver Tumor Segmentation on Dynamic Contrast Enhanced MRI Using 4D Information: Deep Learning Model Based on 3D Convolution and Convolutional LSTM,
MedImg(41), No. 10, October 2022, pp. 2965-2976.
IEEE DOI 2210
Image segmentation, Liver, Tumors, Magnetic resonance imaging, Deep learning, Solid modeling, 4D information, deep learning, tumor segmentation BibRef

Xing, S.W.[Shu-Wei], Romero, J.C.[Joeana Cambranis], Cool, D.W.[Derek W.], Mujoomdar, A.[Amol], Chen, E.C.S.[Elvis C. S.], Peters, T.M.[Terry M.], Fenster, A.[Aaron],
3D US-Based Evaluation and Optimization of Tumor Coverage for US-Guided Percutaneous Liver Thermal Ablation,
MedImg(41), No. 11, November 2022, pp. 3344-3356.
IEEE DOI 2211
Tumors, Applicators, Liver, Imaging, Image segmentation, Measurement, 3D ultrasound, liver ablation, tumor coverage, safety margin, intra-procedural evaluation BibRef

Karthikamani, R., Rajaguru, H.[Harikumar],
Detection of liver abnormalities: A new paradigm in medical image processing and classification techniques,
IJIST(32), No. 6, 2022, pp. 2219-2239.
DOI Link 2212
cuckoo search, dragonfly, elephant search, firefly, GLCM features, GMM, PSO, statistical feature, ultrasonic liver cirrhosis BibRef

Shi, Y.Y.[Yang-Yang], Deng, X.S.[Xue-Song], Tong, Y.Q.[Yu-Qi], Li, R.T.[Ruo-Tong], Zhang, Y.F.[Yan-Fang], Ren, L.J.[Li-Jie], Si, W.X.[Wei-Xin],
Synergistic Digital Twin and Holographic Augmented-Reality-Guided Percutaneous Puncture of Respiratory Liver Tumor,
HMS(52), No. 6, December 2022, pp. 1364-1374.
IEEE DOI 2212
Liver, Surgery, Navigation, Real-time systems, Tumors, Correlation, Digital twins, Augmented reality, Holography, Respiratory system, respiratory motion BibRef

Pattwakkar, V.N.[Vaidehi Nayantara], Kamath, S.[Surekha], Nanjundappa, M.K.[Manjunath Kanabagatte], Kadavigere, R.[Rajagopal],
Automatic liver tumor segmentation on multiphase computed tomography volume using SegNet deep neural network and K-means clustering,
IJIST(33), No. 2, 2023, pp. 729-745.
DOI Link 2303
computed tomography, contrast enhancement, K-means clustering, liver tumor segmentation, power-law transformation, SegNet, semantic segmentation BibRef

Elghazy, H.L.[Hagar Louye], Fakhr, M.W.[Mohamed Waleed],
Dual- and triple-stream RESUNET/UNET architectures for multi-modal liver segmentation,
IET-IPR(17), No. 4, 2023, pp. 1224-1235.
DOI Link 2303
liver segmentation, medical image segmentation, multiple-stream, UNET BibRef

Tan, Z.G.[Zheng-Guo], Unterberg-Buchwald, C.[Christina], Blumenthal, M.[Moritz], Scholand, N.[Nick], Schaten, P.[Philip], Holme, C.[Christian], Wang, X.Q.[Xiao-Qing], Raddatz, D.[Dirk], Uecker, M.[Martin],
Free-Breathing Liver Fat, R2* and B0 Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-Based Reconstruction,
MedImg(42), No. 5, May 2023, pp. 1374-1387.
IEEE DOI 2305
Fats, Image reconstruction, Liver, Sensitivity, Magnetic resonance imaging, Phantoms, Iron, water/fat separation BibRef

Xie, L.J.[Li-Jie], Zhu, F.[Fubao], Yao, N.[Ni],
MDR-Net: Multiscale dense residual networks for liver image segmentation,
IET-IPR(17), No. 8, 2023, pp. 2309-2320.
DOI Link 2306
biological organs, biological techniques, biological tissues, biomedical imaging, biomedical optical imaging, feature selection BibRef

Gao, Z.[Zhan], Zong, Q.[Qiuhao], Wang, Y.Q.[Yi-Qi], Yan, Y.[Yan], Wang, Y.Q.[Yu-Qing], Zhu, N.[Ning], Zhang, J.[Jin], Wang, Y.[Yunfu], Zhao, L.[Liang],
Laplacian Salience-Gated Feature Pyramid Network for Accurate Liver Vessel Segmentation,
MedImg(42), No. 10, October 2023, pp. 3059-3068.
IEEE DOI 2310
BibRef

Zamanian, H.[Hamed], Shalbaf, A.[Ahmad],
Grading of steatosis, fibrosis, lobular inflammation, and ballooning from liver pathology images using pre-trained convolutional neural networks,
IJIST(33), No. 6, 2023, pp. 2178-2193.
DOI Link 2311
classification, deep convolutional neural networks, hepatology, liver disease, machine learning BibRef

Kuang, H.[Haopeng], Yang, X.[Xue], Li, H.J.[Hong-Jun], Wei, J.W.[Jing-Wei], Zhang, L.H.[Li-Hua],
Adaptive Multiphase Liver Tumor Segmentation With Multiscale Supervision,
SPLetters(31), 2024, pp. 426-430.
IEEE DOI 2402
Tumors, Feature extraction, Liver, Image segmentation, Computed tomography, Annotations, Hospitals, multi-scale supervision BibRef

Ni, Y.F.[Yang-Fan], Chen, G.[Geng], Feng, Z.[Zhan], Cui, H.[Heng], Metaxas, D.N.[Dimitris N.], Zhang, S.T.[Shao-Ting], Zhu, W.T.[Wen-Tao],
DA-Tran: Multiphase liver tumor segmentation with a domain-adaptive transformer network,
PR(149), 2024, pp. 110233.
Elsevier DOI 2403
Multiphase CT, Liver tumor segmentation, Domain adaption, Transformer BibRef

Li, J.F.[Jian-Feng], Niu, Y.M.[Yan-Min],
Dual encoding DDS-UNet liver tumour segmentation based on multi-scale deep and shallow feature fusion,
IET-IPR(18), No. 5, 2024, pp. 1189-1199.
DOI Link 2404
arithmetic codes, biomedical imaging, biomedical MRI, cancer, convolutional neural nets, image denoising, image enhancement BibRef

Beuret, S.[Samuel], H©riard-Dubreuil, B.[Baptiste], Martiartu, N.K.[Naiara Korta], Jaeger, M.[Michael], Thiran, J.P.[Jean-Philippe],
Windowed Radon Transform for Robust Speed-of-Sound Imaging With Pulse-Echo Ultrasound,
MedImg(43), No. 4, April 2024, pp. 1579-1593.
IEEE DOI 2404
Imaging, Transforms, Ultrasonic imaging, Image reconstruction, Transducers, Array signal processing, Apertures, liver imaging BibRef

Wen, R.[Ruxue], Yuan, H.J.[Hang-Jie], Ni, D.[Dong], Xiao, W.B.[Wen-Bo], Wu, Y.Y.[Yao-Yao],
From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image Segmentation,
WACV24(453-463)
IEEE DOI Code:
WWW Link. 2404
Training, Image segmentation, Adaptation models, Noise reduction, Liver, Self-supervised learning, Data models, Algorithms, and algorithms BibRef

Hu, Q.X.[Qi-Xin], Chen, Y.X.[Yi-Xiong], Xiao, J.F.[Jun-Fei], Sun, S.W.[Shu-Wen], Chen, J.E.[Jien-Eng], Yuille, A.L.[Alan L.], Zhou, Z.W.[Zong-Wei],
Label-Free Liver Tumor Segmentation,
CVPR23(7422-7432)
IEEE DOI 2309
BibRef

Ali, A.R.[Abder-Rahman], Samir, A.E.[Anthony E.], Guo, P.[Peng],
Self-Supervised Learning for Accurate Liver View Classification in Ultrasound Images with Minimal Labeled Data,
DL-UIA23(3087-3093)
IEEE DOI 2309
BibRef

Shi, J.Y.[Jia-Yin], Kamata, S.I.[Sei-Ichiro],
Extended Res-UNet with Hierarchical Inner-Modules for Liver Tumor Segmentation from CT Volumes,
ICRVC22(169-174)
IEEE DOI 2301
Image segmentation, Liver cancer, Shape, Computed tomography, Liver, Medical services, Feature extraction, liver tumor segmentation, deep learning BibRef

Ali, O.[Omar], Bone, A.[Alexandre], Rohe, M.M.[Marc-Michel], Vibert, E.[Eric], Vignon-Clementel, I.[Irene],
Learning to Jointly Segment the Liver, Lesions and Vessels from Partially Annotated Datasets,
ICIP22(3626-3630)
IEEE DOI 2211
Image segmentation, Fuses, Semantics, Pipelines, Liver, Surgery, Semantic segmentation, multi-task learning, weighted loss function BibRef

Chandra, V.[Vincent], Fan, W.K.[Wen-Kang], Chen, Y.R.[Yin-Ran], Luo, X.B.[Xiong-Biao],
Residual U-Structure Nested Conditional Adversarial Nets Colorized CT Improves Deep Learning Based Abdominal Multi-Organ Segmentation,
ICIP22(2061-2065)
IEEE DOI 2211
Deep learning, Image segmentation, Image color analysis, Computed tomography, Semantics, Liver, Pancreas, Image Colorization, Abdominal Multi-Organ Segmentation BibRef

Pavone, A.M.[Anna Maria], Benfante, V.[Viviana], Stefano, A.[Alessandro], Mamone, G.[Giuseppe], Milazzo, M.[Mariapina], di Pizza, A.[Ambra], Parenti, R.[Rosalba], Maruzzelli, L.[Luigi], Miraglia, R.[Roberto], Comelli, A.[Albert],
Automatic Liver Segmentation in Pre-TIPS Cirrhotic Patients: A Preliminary Step for Radiomics Studies,
AIRCAD22(408-418).
Springer DOI 2208
BibRef

Demir, U.[Ugur], Zhang, Z.Y.[Zhe-Yuan], Wang, B.[Bin], Antalek, M.[Matthew], Keles, E.[Elif], Jha, D.[Debesh], Borhani, A.[Amir], Ladner, D.[Daniela], Bagci, U.[Ulas],
Transformer Based Generative Adversarial Network for Liver Segmentation,
MEDXF22(340-347).
Springer DOI 2208
BibRef

Pan, C.[Chao], Zhou, P.Y.[Pei-Yun], Tan, J.R.[Jing-Ru], Sun, B.[Baoye], Guan, R.[Ruoyu], Wang, Z.T.[Zhu-Tao], Luo, Y.[Ye], Lu, J.W.[Jian-Wei],
Liver Tumor Detection Via A Multi-Scale Intermediate Multi-Modal Fusion Network on MRI Images,
ICIP21(299-303)
IEEE DOI 2201
Image segmentation, Magnetic resonance imaging, Semantics, Liver, Medical services, Feature extraction, Deep learning, Enhanced feature pyramid BibRef

Zhang, J.F.[Jian-Feng], Chang, W.[Wanru], Wu, F.[Fa], Kong, D.[Dexing],
Pixel-RRT*: A Novel Skeleton Trajectory Search Algorithm for Hepatic Vessels,
DICTA20(1-8)
IEEE DOI 2201
Image segmentation, Liver diseases, Digital images, Minimization, Skeleton, Trajectory, Tumors, Pixel-RRT, Skeleton Trajectory, Topological Continuity BibRef

Huang, C.F.[Chong-Fei], Qiu, C.H.[Chen-Hui], Peng, Z.Y.[Zhi-Yi], Yuan, J.[Jing], Kong, D.X.[De-Xing],
Iterative Reweighted Local Cross Correlation Method for Nonlinear Registration of Multiphase Liver CT Images,
ICIP21(136-140)
IEEE DOI 2201
Measurement, Correlation, Computed tomography, Liver, Imaging, Radiology, Physiology, Nonlinear Registration, Coarse-to-Fine optimization BibRef

Nakai, K.[Katsuhiro], Qiao, X.[Xu], Han, X.H.[Xian-Hua],
Angular Margin Constrained Loss for Automatic Liver Fibrosis Staging,
MVA21(1-5)
DOI Link 2109
Training, Shape, Magnetic resonance imaging, Neural networks, Liver, Performance gain, Task analysis BibRef

Lamy, J.[Jonas], Merveille, O.[Odyssée], Kerautret, B.[Bertrand], Passat, N.[Nicolas], Vacavant, A.[Antoine],
Vesselness Filters: A Survey with Benchmarks Applied to Liver Imaging,
ICPR21(3528-3535)
IEEE DOI 2105
Knowledge engineering, Magnetic resonance imaging, Liver, Surgery, Benchmark testing, Software, Robustness BibRef

Wang, B.[Bo], Yan, Q.Z.[Qin-Zsen], Xu, Z.Q.[Zheng-Qing], Ai, J.Y.[Jing-Yang], Jin, S.[Shuo], Xu, W.[Wei], Zhao, W.[Wei], Zhang, L.[Liang], You, Z.[Zheng],
A Benchmark Dataset for Segmenting Liver, Vasculature and Lesions from Large-scale Computed Tomography Data,
ICPR21(6584-6591)
IEEE DOI 2105
Measurement, Deep learning, Image segmentation, Systematics, Computed tomography, Liver, Surgery, Computer assisted diagnosis, Liver vasculature segmentation BibRef

Wei, Y.[Yanan], Tian, J.[Jiang], Zhong, C.[Cheng], Shi, Z.C.[Zhong-Chao],
AKFNET: An Anatomical Knowledge Embedded Few-Shot Network for Medical Image Segmentation,
ICIP21(11-15)
IEEE DOI 2201
Knowledge engineering, Training, Image segmentation, Annotations, Transfer learning, Training data, Medical Image, Segmentation, Few-shot Learning BibRef

Zhang, Y.[Yao], Tian, J.[Jiang], Zhong, C.[Cheng], Zhang, Y.[Yang], Shi, Z.C.[Zhong-Chao], He, Z.Q.[Zhi-Qiang],
DARN: Deep Attentive Refinement Network for Liver Tumor Segmentation from 3D CT volume,
ICPR21(7796-7803)
IEEE DOI 2105
Image segmentation, Computed tomography, Semantics, Liver, Surgery, Planning, CT image BibRef

Alksas, A.[Ahmed], Shehata, M.[Mohamed], Saleh, G.A.[Gehad A.], Shaffie, A.[Ahmed], Soliman, A.[Ahmed], Ghazal, M.[Mohammed], Khalifeh, H.A.[Hadil Abu], Razek, A.A.[Ahmed Abdel], El-Baz, A.[Ayman],
A Novel Computer-Aided Diagnostic System for Early Assessment of Hepatocellular Carcinoma,
ICPR21(10375-10382)
IEEE DOI 2105
Solid modeling, Design automation, Shape, Malignant tumors, Liver, Benign tumors, Tools, CE-MRI, HCC, LI-RADS, CAD BibRef

Li, C., Tan, Y., Chen, W., Luo, X., Gao, Y., Jia, X., Wang, Z.,
Attention Unet++: A Nested Attention-Aware U-Net for Liver CT Image Segmentation,
ICIP20(345-349)
IEEE DOI 2011
Image segmentation, Liver, Logic gates, Feature extraction, Computed tomography, Task analysis, Cancer, Attention, UNet++, Liver Segmentation BibRef

Raju, A.[Ashwin], Cheng, C.T.[Chi-Tung], Huo, Y.K.[Yuan-Kai], Cai, J.Z.[Jin-Zheng], Huang, J.Z.[Jun-Zhou], Xiao, J.[Jing], Lu, L.[Le], Liao, C.H.[Chien-Hung], Harrison, A.P.[Adam P.],
Co-heterogeneous and Adaptive Segmentation from Multi-source and Multi-phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation,
ECCV20(XXIII:448-465).
Springer DOI 2011
BibRef

Yang, J., Dvornek, N.C., Zhang, F., Zhuang, J., Chapiro, J., Lin, M., Duncan, J.S.,
Domain-Agnostic Learning With Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation,
VRMI19(323-331)
IEEE DOI 2004
image representation, image segmentation, learning (artificial intelligence), liver, Cross Modality Segmentation BibRef

Zhao, S., Dong, Y., Chang, E., Xu, Y.,
Recursive Cascaded Networks for Unsupervised Medical Image Registration,
ICCV19(10599-10609)
IEEE DOI 2004
image registration, iterative methods, learning (artificial intelligence), medical image processing, Liver BibRef

Chen, Y., Li, D., Zhu, Q., Wang, C., Li, J.,
Automated Extraction of Liver Outlines From Computed Tomography Scan Images Using a Cuda-based Segmentation Method,
PTVSBB19(31-36).
DOI Link 1912
BibRef

Wu, Y., Zhou, Q., Hu, H., Rong, G., Li, Y., Wang, S.,
Hepatic Lesion Segmentation by Combining Plain and Contrast-Enhanced CT Images with Modality Weighted U-Net,
ICIP19(255-259)
IEEE DOI 1910
Medical Image Segmentation, Deep Neural Networks, Multimodal Fusion BibRef

Liu, Y., Tan, D.S., Chen, J., Cheng, W., Hua, K.,
Segmenting Hepatic Lesions Using Residual Attention U-Net with an Adaptive Weighted Dice Loss,
ICIP19(3322-3326)
IEEE DOI 1910
CT image segmentation, residual block, attention module, hepatic lesion factor BibRef

Ju, H., Wang, G., Men, S., Zhang, H., Gu, L., Zhou, W.,
Discrepancy Steered Conditional Adversarial Network For Deep Feature Based Malignancy Characterization of Hepatocellular Carcinoma,
ICIP19(1342-1345)
IEEE DOI 1910
hepatocellular carcinoma, conditional adversarial network, malignancy characterization, deep feature BibRef

Morales-Navarrete, H., Segovia-Miranda, F., Zerial, M., Kalaidzidis, Y.,
Prediction of Multiple 3D Tissue Structures Based on Single-Marker Images Using Convolutional Neural Networks,
ICIP19(1361-1365)
IEEE DOI 1910
Deep Learning, convolutional neural networks, fluorescence microscopy, biological tissue, liver BibRef

Yu, W., Fang, B., Liu, Y., Gao, M., Zheng, S., Wang, Y.,
Liver Vessels Segmentation Based on 3d Residual U-NET,
ICIP19(250-254)
IEEE DOI 1910
3D Residual U-Net, Weighted Dice Loss Function, 3D Morphological Closed Operation BibRef

Liang, D.[Dong], Lin, L.F.[Lan-Fen], Chen, X.[Xiao], Hu, H.J.[Hong-Jie], Zhang, Q.W.[Qiao-Wei], Chen, Q.Q.[Qing-Qing], Iwamoto, Y.T.[Yu-Taro], Han, X.H.[Xian-Hua], Chen, Y.W.[Yen-Wei], Tong, R.F.[Ruo-Feng], Wu, J.[Jian],
Multi-Stream Scale-Insensitive Convolutional and Recurrent Neural Networks for Liver Tumor Detection in Dynamic Ct Images,
ICIP19(794-798)
IEEE DOI 1910
Liver tumor detection, scale-insensitive, GCLSTM, MSCR BibRef

Chen, X., Lin, L., Liang, D., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y., Tong, R., Wu, J.,
A Dual-Attention Dilated Residual Network for Liver Lesion Classification and Localization on CT Images,
ICIP19(235-239)
IEEE DOI 1910
Dual-attention, dilated residual network, lesion classification, weakly-supervised localization BibRef

Zhou, Y., Sun, Y., Yang, W., Lu, Z., Huang, M., Lu, L., Zhang, Y., Feng, Y., Chen, W., Feng, Q.,
Correlation-Weighted Sparse Representation for Robust Liver DCE-MRI Decomposition Registration,
MedImg(38), No. 10, October 2019, pp. 2352-2363.
IEEE DOI 1910
Liver, Lesions, Strain, Dictionaries, Encoding, Image coding, Principal component analysis, DCE-MRI, registration, sparse representation BibRef

Lu, Z., Shimizu, A., Ho, H.,
Evaluation of a Statistical Shape Model for the Liver,
IVCNZ18(1-4)
IEEE DOI 1902
Liver, Shape, Image segmentation, Training, Indexes, Brain modeling, Computed tomography, Liver, statistical shape, parametric mesh, Jaccard index BibRef

Zhang, Y., Jiang, X., Zhong, C., Zhang, Y., Shi, Z., Li, Z., He, Z.,
SequentialSegNet: Combination with Sequential Feature for Multi-Organ Segmentation,
ICPR18(3947-3952)
IEEE DOI 1812
Feature extraction, Image segmentation, Computed tomography, Liver, Gallbladder BibRef

Lebre, M., Vacavant, A., Grand-Brochier, M., Merveille, O., Chabrot, P., Abergel, A., Magnin, B.,
Automatic 3-D Skeleton-Based Segmentation of Liver Vessels from MRI and CT for Couinaud Representation,
ICIP18(3523-3527)
IEEE DOI 1809
Liver, Magnetic resonance imaging, Image segmentation, Computed tomography, Veins, Biomedical imaging, Surgery, vessels. skeleton BibRef

Küstner, T., Müller, S., Fischer, M., Weiß, J., Nikolaou, K., Bamberg, F., Yang, B., Schick, F., Gatidis, S.,
Semantic Organ Segmentation in 3D Whole-Body MR Images,
ICIP18(3498-3502)
IEEE DOI 1809
Image segmentation, Radio frequency, Liver, Imaging, Semantics, Training data, semantic segmentation BibRef

Rafiei, S., Nasr-Esfahani, E., Najarian, K., Karimi, N., Samavi, S., Soroushmehr, S.M.R.,
Liver Segmentation in CT Images Using Three Dimensional to Two Dimensional Fully Convolutional Network,
ICIP18(2067-2071)
IEEE DOI 1809
Liver, Kernel, Training, Computed tomography, Encoding, conditional random field BibRef

Cinque, L.[Luigi], de Santis, A.[Alberto], di Giamberardino, P.[Paolo], Iacoviello, D.[Daniela], Placidi, G.[Giuseppe], Pompili, S.[Simona], Sferra, R.[Roberta], Spezialetti, M.[Matteo], Vetuschi, A.[Antonella],
Design of a Classification Strategy for Light Microscopy Images of the Human Liver,
CIAP17(I:626-636).
Springer DOI 1711
BibRef

Andersson, T.[Thord], Borga, M.[Magnus], Leinhard, O.D.[Olof Dahlqvist],
Geodesic registration for interactive atlas-based segmentation using learned multi-scale anatomical manifolds,
PRL(112), 2018, pp. 340-345.
Elsevier DOI 1809
BibRef
Earlier: A2, A1, A3:
Semi-supervised learning of anatomical manifolds for atlas-based segmentation of medical images,
ICPR16(3146-3149)
IEEE DOI 1705
Atlas-based segmentation, Image registration, Manifold learning, MRI. Biomedical imaging, Image segmentation, Liver, Magnetic resonance imaging, Manifolds, Prototypes, BibRef

Xu, Y., Lin, L., Hu, H., Wang, D., Liu, Y., Wang, J., Han, X., Chen, Y.W.,
Bag of temporal co-occurrence words for retrieval of focal liver lesions using 3D multiphase contrast-enhanced CT images,
ICPR16(2282-2287)
IEEE DOI 1705
Computed tomography, Feature extraction, Frequency locked loops, Lesions, Liver, Visualization, Vocabulary, Computer-aided diagnosis (CAD) systems, bag of temporal co-occurrence words (BoTCoW), bag of visual words (BoVW), enhancement pattern, multiphase, contrast-enhanced, CT, images BibRef

Han, X.H.[Xian-Hua], Wang, J.[Jian], Konno, Y.[Yuu], Chen, Y.W.[Yen-Wei],
Bayesian Saliency Model for Focal Liver Lesion Enhancement and Detection,
MCBMIIA16(III: 32-45).
Springer DOI 1704
BibRef

Gueziri, H.E.[Houssem-Eddine], Tremblay, S.[Sebastien], Laporte, C.[Catherine], Brooks, R.[Rupert],
Graph-Based 3D-Ultrasound Reconstruction of the Liver in the Presence of Respiratory Motion,
RAMBO16(48-57).
Springer DOI 1703
BibRef

Batool, N.,
Detection and spatial analysis of hepatic steatosis in histopathology images using sparse linear models,
IPTA16(1-6)
IEEE DOI 1703
blood vessels BibRef

Sedlar, J., Bajger, M., Caon, M., Lee, G.,
Model-Guided Segmentation of Liver in CT and PET-CT Images of Child Patients Based on Statistical Region Merging,
DICTA16(1-8)
IEEE DOI 1701
Computational modeling BibRef

Conegliano, A.[Andrew], Schulze, J.P.[Jürgen P.],
Realistic 3D Modeling of the Liver from MRI Images,
ISVC16(II: 223-232).
Springer DOI 1701
BibRef

Al-Kadi, O.S.,
Multiscale Nakagami parametric imaging for improved liver tumor localization,
ICIP16(3384-3388)
IEEE DOI 1610
Estimation BibRef

Fenwa, O.D., Ajala, F.A., Aku, A.M.,
Performance evaluation of support vector machine and artificial neural network in the classification of liver cirhosis and hemachromatosis,
ICCVIA15(1-6)
IEEE DOI 1603
image classification BibRef

Kitrungrotsakul, T.[Titinunt], Han, X.H.[Xian-Hua], Chen, Y.W.[Yen-Wei],
Liver segmentation using superpixel-based graph cuts and restricted regions of shape constrains,
ICIP15(3368-3371)
IEEE DOI 1512
estimated shape constrain BibRef

Chen, B.[Bin], Chen, Y.[Yang], Yang, G.[Guanyu], Meng, J.Y.[Jing-Yu], Zeng, R.[Rui], Luo, L.M.[Li-Min],
Segmentation of liver tumor via nonlocal active contours,
ICIP15(3745-3748)
IEEE DOI 1512
BibRef

Conze, P.H.[Pierre-Henri], Rousseau, F.[François], Noblet, V.[Vincent], Heitz, F.[Fabrice], Memeo, R.[Riccardo], Pessaux, P.[Patrick],
Semi-automatic Liver Tumor Segmentation in Dynamic Contrast-Enhanced CT Scans Using Random Forests and Supervoxels,
MLMI15(212-219).
Springer DOI 1511
BibRef

Domingo, J.[Juan], Dura, E.[Esther], Ayala, G.[Guillermo], Ruiz-España, S.[Silvia],
Means of 2D and 3D Shapes and Their Application in Anatomical Atlas Building,
CAIP15(I:522-533).
Springer DOI 1511
BibRef

Dura, E.[Esther], Domingo, J.[Juan], Rojas-Arboleda, A.F., Marti-Bonmati, L.,
Mean sets for building 3D probabilistic liver atlas from perfusion MR images,
IPTA12(186-191)
IEEE DOI 1503
biomedical MRI BibRef

Goceri, E., Unlu, M.Z., Guzelis, C., Dicle, O.,
An automatic level set based liver segmentation from MRI data sets,
IPTA12(192-197)
IEEE DOI 1503
approximation theory BibRef

Li, X.[Xuhui], Huang, C.[Cheng], Jia, F.C.[Fu-Cang], Li, Z.M.[Zong-Min], Fang, C.H.[Chi-Hua], Fan, Y.F.[Ying-Fang],
Automatic Liver Segmentation Using Statistical Prior Models and Free-form Deformation,
MCV14(181-188).
Springer DOI 1501
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Deng, J.P.[Jun-Ping], Han, X.H.[Xian-Hua], Xu, G.[Gang], Chen, Y.W.[Yen-Wei],
Sparse and Low Rank Matrix Decomposition Based Local Morphological Analysis and Its Application to Diagnosis of Cirrhosis Livers,
ICPR14(3363-3368)
IEEE DOI 1412
Accuracy BibRef

Yan, Z.N.[Zhen-Nan], Tan, C.W.[Chao-Wei], Zhang, S.T.[Shao-Ting], Zhou, Y.[Yan], Belaroussi, B.[Boubakeur], Yu, H.J.[Hui Jing], Miller, C.[Colin], Metaxas, D.N.[Dimitris N.],
Automatic Liver Segmentation and Hepatic Fat Fraction Assessment in MRI,
ICPR14(3280-3285)
IEEE DOI 1412
Accuracy BibRef

Garnier, M.[Mickaël], Ali, M.A.[Maya Alsheh], Seguin, J.[Johanne], Mignet, N.[Nathalie], Hurtut, T.[Thomas], Wendling, L.[Laurent],
Grading Cancer from Liver Histology Images Using Inter and Intra Region Spatial Relations,
ICIAR14(II: 247-254).
Springer DOI 1410
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Anter, A.M., Hassanien, A.E., Schaefer, G.,
Automatic Segmentation and Classification of Liver Abnormalities Using Fractal Dimension,
ACPR13(937-941)
IEEE DOI 1408
computerised tomography BibRef

Ogihara, H., Fujita, Y., Hamamoto, Y., Iizuka, N., Oka, M.,
Classification Based on Boolean Algebra and Its Application to the Prediction of Recurrence of Liver Cancer,
ACPR13(838-841)
IEEE DOI 1408
Boolean algebra BibRef

Luo, J.[Jie], Chen, Y.W.[Yen-Wei], Han, X.H.[Xian-Hua], Tateyama, T.[Tomoko], Furukawa, A.[Akira], Kanasaki, S.[Shuzo],
Pilot study of applying shape analysis to liver cirrhosis diagnosis,
ICIP13(3537-3541)
IEEE DOI 1402
Computer-Aided Diagnosis BibRef

Chen, Y.W.[Yen-Wei], Luo, J.[Jie], Han, X.H.[Xian-Hua], Tateyama, T.[Tomoko], Furukawa, A.[Akira], Kanasaki, S.[Shuzo],
A Morphologic Analysis of Cirrhotic Liver in CT Images,
ICIAR13(494-501).
Springer DOI 1307
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Ribeiro, R.[Ricardo], Marinho, R.T.[Rui Tato], Sanches, J.M.[João Miguel],
Cirrhosis Prognostic Quantification with Ultrasound: An Approximation to Model for End-Stage Liver Disease,
IbPRIA13(551-558).
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Wu, D.[Dijia], Liu, D.[David], Suehling, M.[Michael], Zhou, K.S.[Kevin S.], Tietjen, C.[Christian],
A Cascade Learning Method for Liver Lesion Detection in CT Images,
MCVM12(206-214).
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Weon, C.J.[Chi Jun], Nam, W.H.[Woo Hyun], Lee, D.[Duhgoon], Hwang, Y.[Youngkyoo], Kim, J.B.[Jung-Bae], Bang, W.C.[Won-Chul], Ra, J.B.[Jong Beom],
Position estimation of moving liver lesion based on registration between 2D ultrasound and 4D MR images,
ICIP12(1677-1680).
IEEE DOI 1302
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Gloger, O.[Oliver], Toennies, K.[Klaus], Kuehn, J.P.[Jens-Peter],
Fully Automatic Liver Volumetry Using 3D Level Set Segmentation for Differentiated Liver Tissue Types in Multiple Contrast MR Datasets,
SCIA11(512-523).
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Kohara, S.[Shinya], Tateyama, T.[Tomoko], Foruzan, A.H.[Amir Hossein], Furukawa, A.[Akira], Kanasaki, S.[Shuzo], Wakamiya, M.[Makoto], Wei, X.[Xiong], Chen, Y.W.[Yen-Wei],
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ICIP11(2921-2924).
IEEE DOI 1201
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Masuda, Y.[Yu], Tateyama, T.[Tomoko], Xiong, W.[Wei], Zhou, J.[Jiayin], Wakamiya, M.[Makoto], Kanasaki, S.[Syuzo], Furukawa, A.[Akira], Chen, Y.W.[Yen Wei],
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Lee, J.G.[June-Goo], Cai, W.L.[Wen-Li], Singh, A.[Anand], Yoshida, H.[Hiroyuki],
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Li, C.Y.[Chang-Yang], Wang, X.Y.[Xiu-Ying], Eberl, S.[Stefan], Fulham, M.J.[Michael J.], Yin, Y.[Yong], Feng, D.D.[David Dagan],
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Militzer, A.[Arne], Hager, T.[Tobias], Jager, F.[Florian], Tietjen, C.[Christian], Hornegger, J.[Joachim],
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IASP10(248-253).
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Jha, A.K.[Abhinav K.], Rodriguez, J.J.[Jeffrey J.],
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Southwest12(21-24).
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Jha, A.K.[Abhinav K.], Rodriguez, J.J.[Jeffrey J.], Stephen, R.M.[Renu M.], Stopeck, A.T.[Alison T.],
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Fujita, Y.[Yusuke], Hamamoto, Y.[Yoshihiko], Segawa, M.[Makoto], Terai, S.[Shuji], Sakaida, I.[Isao],
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Ribeiro, R.[Ricardo], Marinho, R.[Rui], Velosa, J.[José], Ramalho, F.[Fernando], Sanches, J.M.[João Miguel],
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Ribeiro, R.[Ricardo], Sanches, J.M.[João M.],
Fatty Liver Characterization and Classification by Ultrasound,
IbPRIA09(354-361).
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Xiong, W.[Wei], Ong, S.H., Tian, Q.[Qi], Xu, G.Z.[Guo-Zhen], Zhou, J.Y.[Jia-Yin], Liu, J.[Jiang], Venkatash, S.K.,
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Liu, J.H.[Jian-Hua], Zhang, R.[Rui], Wang, J.W.[Jian-Wei],
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Chen, Y.W.[Yen-Wei], Tsubokawa, K.[Katsumi], Xu, R.[Rui], Morikawa, S.[Shigehiro], Kurumi, Y.[Yoshimasa],
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Ling, H.B.[Hai-Bin], Zhou, S.K.[S. Kevin], Zheng, Y.F.[Ye-Feng], Georgescu, B.[Bogdan], Suehling, M.[Michael], Comaniciu, D.[Dorin],
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Garamendi, J.F., Malpica, N., Martel, J., Schiavi, E.,
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IbPRIA07(I: 161-168).
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Evans, A., Lambrou, T., Linney, A.D., Todd-Pokropek, A.,
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Li, Y.Z.[Yuan-Zhong], Hara, S.[Shoji], Shimura, K.[Kazuo],
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Mekada, Y.[Yoshito], Wakida, Y.[Yuki], Hayashi, Y.[Yuichiro], Ide, I.[Ichiro], Murase, H.[Hiroshi],
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Hiransakolwong, N.[Nualsawat],
Automated Liver Detection in Ultrasound Images,
CIVR05(619-628).
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Kissi, A., Cormier, S., Pourcelot, L., Bleuzen, A., Tranquart, F.,
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Krishnamurthy, C., Rodriguez, J.J., Gillies, R.J.,
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Saitoh, T., Tamura, Y., Kaneko, T.,
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ICPR02(I: 735-738).
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Agrafiotis, D., Jones, M., Nikolov, S.G., Halliwell, M., Bull, D.R., Canagarajah, C.N.,
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ICIP01(II: 331-334).
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Boes, J.L., Meyer, C.R., Weymouth, T.E.,
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Kovalev, V.A.[Vassili A.],
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CAIP95(496-502).
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Kidney Disease, Tomography, CAT Analysis, Other Methods .

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