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Automatic annotation of liver computed tomography images based on a
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automatic, CT image, liver annotation, liver segment, vessel skeletonization
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Layer Embedding Analysis in Convolutional Neural Networks for
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2011
Calibration, Liver, Task analysis, Computational modeling,
Convolutional neural networks, Predictive models,
liver tissue classification
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Mahdy, L.N.[Lamia N.],
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Automatic segmentation system for liver tumors based on the
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2011
liver, multilevel thresholding, optimization, Otsu, tumor segmentation
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Wang, S.,
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Ma, K.,
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Conquering Data Variations in Resolution:
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2012
Liver, Tumors, Computed tomography, Decoding, deep learning
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Mourya, G.K.[Gajendra Kumar],
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Empirical greedy machine-based automatic liver segmentation in CT
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Zeng, Q.,
Honarvar, M.,
Schneider, C.,
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Lobo, J.,
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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
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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
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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
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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
Yin, C.[Chong],
Liu, S.Q.[Si-Qi],
Lyu, F.[Fei],
Lu, J.H.[Jia-Hao],
Darkner, S.[Sune],
Wong, V.W.S.[Vincent Wai-Sun],
Yuen, P.C.[Pong C.],
XFibrosis: Explicit Vessel-Fiber Modeling for Fibrosis Staging from
Liver Pathology Images,
CVPR24(11282-11291)
IEEE DOI
2410
Pathology, Convolution, Liver diseases, Computational modeling, Biopsy,
Transforms, liver fibrosis, histological scoring, pathology image analysis
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
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PTVSBB19(31-36).
DOI Link
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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
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CIAP17(I:626-636).
Springer DOI
1711
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Andersson, T.[Thord],
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Leinhard, O.D.[Olof Dahlqvist],
Geodesic registration for interactive atlas-based segmentation using
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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,
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Han, X.H.[Xian-Hua],
Wang, J.[Jian],
Konno, Y.[Yuu],
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Bayesian Saliency Model for Focal Liver Lesion Enhancement and
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MCBMIIA16(III: 32-45).
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1704
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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
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IPTA16(1-6)
IEEE DOI
1703
blood vessels
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Sedlar, J.,
Bajger, M.,
Caon, M.,
Lee, G.,
Model-Guided Segmentation of Liver in CT and PET-CT Images of Child
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DICTA16(1-8)
IEEE DOI
1701
Computational modeling
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Conegliano, A.[Andrew],
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Realistic 3D Modeling of the Liver from MRI Images,
ISVC16(II: 223-232).
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1701
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Al-Kadi, O.S.,
Multiscale Nakagami parametric imaging for improved liver tumor
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ICIP16(3384-3388)
IEEE DOI
1610
Estimation
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Fenwa, O.D.,
Ajala, F.A.,
Aku, A.M.,
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ICCVIA15(1-6)
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1603
image classification
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Kitrungrotsakul, T.[Titinunt],
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Liver segmentation using superpixel-based graph cuts and restricted
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ICIP15(3368-3371)
IEEE DOI
1512
estimated shape constrain
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Chen, B.[Bin],
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Meng, J.Y.[Jing-Yu],
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Luo, L.M.[Li-Min],
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ICIP15(3745-3748)
IEEE DOI
1512
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Marti-Bonmati, L.,
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IPTA12(186-191)
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1503
biomedical MRI
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Goceri, E.,
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Dicle, O.,
An automatic level set based liver segmentation from MRI data sets,
IPTA12(192-197)
IEEE DOI
1503
approximation theory
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Automatic Liver Segmentation Using Statistical Prior Models and
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1501
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ICPR14(3363-3368)
IEEE DOI
1412
Accuracy
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ICPR14(3280-3285)
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1412
Accuracy
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1410
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Automatic Segmentation and Classification of Liver Abnormalities
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ACPR13(937-941)
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computerised tomography
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Ogihara, H.,
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ACPR13(838-841)
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Boolean algebra
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Computer-Aided Diagnosis
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Kidney Disease, Tomography, CAT Analysis, Other Methods .