8.6.3.3 Medical Image Semantic Segmentation

Chapter Contents (Back)
Semantic Segmentation. Medical Images. General topic of medical image segmentation.

Cheng, K.S.[Kuo-Sheng], Lin, J.S.[Jzau-Sheng], Mao, C.W.[Chi-Wu],
The Application of Competitive Hopfield Neural Network to Medical Image Segmentation,
MedImg(15), No. 4, August 1996, pp. 560-567.
IEEE Top Reference. 0203
BibRef

Hansen, M.W.[Michael W.], Higgins, W.E.[William E.],
Relaxation Methods for Supervised Image Segmentation,
PAMI(19), No. 9, September 1997, pp. 949-962.
IEEE DOI 9710
BibRef
Earlier:
Watershed-driven relaxation labeling for image segmentation,
ICIP94(III: 460-464).
IEEE DOI 9411
Watershed driven relaxation labeling. Applied to 3D medical images. Use cues that indicate region shape. BibRef

Gal, Y.[Yaniv], Mehnert, A.[Andrew], Rose, S.[Stephen], Crozier, S.[Stuart],
Mutual information-based binarisation of multiple images of an object: An application in medical imaging,
IET-CV(7), No. 3, 2013, pp. 163-169.
DOI Link 1307
BibRef

Cardoso, M.J.[M. Jorge], Modat, M.[Marc], Wolz, R., Melbourne, A., Cash, D.[David], Rueckert, D., Ourselin, S.[Sebastien],
Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion,
MedImg(34), No. 9, September 2015, pp. 1976-1988.
IEEE DOI 1509
Image segmentation Clinical annotations. BibRef

Korner, M.[Marco], Krishna, M.V.[Mahesh V.], Susse, H.[Herbert], Ortmann, W.[Wolfang], Denzler, J.[Joachim],
Regularized Geometric Hulls for Bio-medical Image Segmentation,
BMVA(2015), No. 4, 2015, pp. 1-12.
PDF File. 1509
BibRef

Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., Rueckert, D.,
DRINet for Medical Image Segmentation,
MedImg(37), No. 11, November 2018, pp. 2453-2462.
IEEE DOI 1811
Image segmentation, Computer architecture, Convolution, Training, Medical diagnostic imaging, Standards, abdominal organ segmentation BibRef

Bi, L.[Lei], Feng, D.[Dagan], Kim, J.M.[Jin-Man],
Dual-Path Adversarial Learning for Fully Convolutional Network (FCN)-Based Medical Image Segmentation,
VC(34), No. 6-8, June 2018, pp. 1043-1052.
WWW Link. 1806
BibRef

Schipaanboord, B., Boukerroui, D., Peressutti, D., van Soest, J., Lustberg, T., Kadir, T., Dekker, A., van Elmpt, W., Gooding, M.,
Can Atlas-Based Auto-Segmentation Ever Be Perfect? Insights From Extreme Value Theory,
MedImg(38), No. 1, January 2019, pp. 99-106.
IEEE DOI 1901
Image segmentation, Databases, Computed tomography, Planning, Head, Neck, Tumors, Radiotherapy, extreme value theory, auto-contouring BibRef

Lu, L., Harrison, A.P.,
Deep Medical Image Computing in Preventive and Precision Medicine,
MultMedMag(25), No. 3, July 2018, pp. 109-113.
IEEE DOI 1901
Biomedical imaging, Tumors, Computed tomography, Image segmentation, Biomarkers BibRef

Karimi, D., Salcudean, S.E.,
Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks,
MedImg(39), No. 2, February 2020, pp. 499-513.
IEEE DOI 2002
Image segmentation, Biomedical imaging, Training, Sensitivity, convolutional neural networks BibRef

Lin, D.Y.[Dong-Yun], Li, Y.Q.[Yi-Qun], Nwe, T.L.[Tin Lay], Dong, S.[Sheng], Oo, Z.M.[Zaw Min],
RefineU-Net: Improved U-Net with progressive global feedbacks and residual attention guided local refinement for medical image segmentation,
PRL(138), 2020, pp. 267-275.
Elsevier DOI 2010
U-Net, Medical image segmentation, Progressive global feedbacks, Local refinement, Residual attention gate BibRef

Mehrtash, A., Wells, W.M., Tempany, C.M., Abolmaesumi, P., Kapur, T.,
Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation,
MedImg(39), No. 12, December 2020, pp. 3868-3878.
IEEE DOI 2012
Uncertainty, Image segmentation, Calibration, Estimation, Biomedical imaging, Artificial neural networks, Bayes methods, fully convolutional neural networks BibRef

Huang, S.Q.[Shao-Qiong], Huang, M.X.[Meng-Xing], Zhang, Y.[Yu], Chen, J.[Jing], Bhatti, U.[Uzair],
Medical Image Segmentation Using Deep Learning with Feature Enhancement,
IET-IPR(14), No. 14, December 2020, pp. 3324-3332.
DOI Link 2012
BibRef

Eelbode, T., Bertels, J., Berman, M., Vandermeulen, D., Maes, F., Bisschops, R., Blaschko, M.B.,
Optimization for Medical Image Segmentation: Theory and Practice When Evaluating With Dice Score or Jaccard Index,
MedImg(39), No. 11, November 2020, pp. 3679-3690.
IEEE DOI 2011
Indexes, Image segmentation, Biomedical imaging, Measurement, Risk management, Training, Task analysis, Dice, Jaccard, Tversky BibRef

Ren, X., Ahmad, S., Zhang, L., Xiang, L., Nie, D., Yang, F., Wang, Q., Shen, D.,
Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation,
IP(29), 2020, pp. 7497-7510.
IEEE DOI 2007
Semantic segmentation, fully convolutional network, task decomposition, sync-regularization, deep learning BibRef

Wang, D.[Dan], Hu, G.Q.[Guo-Qing], Lyu, C.Z.[Cheng-Zhi],
Multi-path connected network for medical image segmentation,
JVCIR(71), 2020, pp. 102852.
Elsevier DOI 2009
Medical image segmentation, Multi-path connections, Convolutional neural networks, Encoder-decoder structure BibRef

Kim, B.N.[Bach Ngoc], Dolz, J.[Jose], Jodoin, P.M.[Pierre-Marc], Desrosiers, C.[Christian],
Privacy-Net: An Adversarial Approach for Identity-Obfuscated Segmentation of Medical Images,
MedImg(40), No. 7, July 2021, pp. 1737-1749.
IEEE DOI 2107
Image segmentation, Task analysis, Biomedical imaging, Servers, Training, Privacy, Image analysis, Adversarial, deep learning, segmentation BibRef

Huang, H.M.[Hui-Min], Zheng, H.[Han], Lin, L.F.[Lan-Fen], Cai, M.[Ming], Hu, H.J.[Hong-Jie], Zhang, Q.W.[Qiao-Wei], Chen, Q.Q.[Qing-Qing], Iwamoto, Y.[Yutaro], Han, X.H.[Xian-Hua], Chen, Y.W.[Yen-Wei], Tong, R.F.[Ruo-Feng],
Medical Image Segmentation With Deep Atlas Prior,
MedImg(40), No. 12, December 2021, pp. 3519-3530.
IEEE DOI 2112
Image segmentation, Bayes methods, Probabilistic logic, Deep learning, Adaptation models, Task analysis, adaptive bayesian loss BibRef

El Jurdi, R.[Rosana], Petitjean, C.[Caroline], Honeine, P.[Paul], Cheplygina, V.[Veronika], Abdallah, F.[Fahed],
High-level prior-based loss functions for medical image segmentation: A survey,
CVIU(210), 2021, pp. 103248.
Elsevier DOI 2109
Survey, Segmentation. Survey, Medical. Prior-based loss functions, Anatomical constraint losses, Convolutional neural networks, Medical image segmentation, Deep learning BibRef

Yu, Q.[Qian], Gao, Y.[Yang], Zheng, Y.F.[Ye-Feng], Zhu, J.B.[Jian-Bing], Dai, Y.K.[Ya-Kang], Shi, Y.H.[Ying-Huan],
Crossover-Net: Leveraging vertical-horizontal crossover relation for robust medical image segmentation,
PR(113), 2021, pp. 107756.
Elsevier DOI 2103
Code, Segmentation.
WWW Link. Convolutional neural network, Non-elongated tissue, Crossover-Net, Image segmentation, Crossover-patch BibRef

Nath, V.[Vishwesh], Yang, D.[Dong], Landman, B.A.[Bennett A.], Xu, D.G.[Da-Guang], Roth, H.R.[Holger R.],
Diminishing Uncertainty Within the Training Pool: Active Learning for Medical Image Segmentation,
MedImg(40), No. 10, October 2021, pp. 2534-2547.
IEEE DOI 2110
Image segmentation, Biomedical imaging, Deep learning, Training, Task analysis, Mutual information, Hippocampus, Deep Learning, SVGD BibRef

Feng, R.[Ruiwei], Zheng, X.S.[Xiang-Shang], Gao, T.X.[Tian-Xiang], Chen, J.[Jintai], Wang, W.Z.[Wen-Zhe], Chen, D.Z.[Danny Z.], Wu, J.[Jian],
Interactive Few-Shot Learning: Limited Supervision, Better Medical Image Segmentation,
MedImg(40), No. 10, October 2021, pp. 2575-2588.
IEEE DOI 2110
Image segmentation, Task analysis, Biomedical imaging, Training, Annotations, Deep learning, Optimization, limited supervision BibRef

Cui, H.J.[Heng-Ji], Wei, D.[Dong], Ma, K.[Kai], Gu, S.[Shi], Zheng, Y.F.[Ye-Feng],
A Unified Framework for Generalized Low-Shot Medical Image Segmentation With Scarce Data,
MedImg(40), No. 10, October 2021, pp. 2656-2671.
IEEE DOI 2110
Image segmentation, Biomedical imaging, Training, Annotations, Task analysis, Diseases, adaptive mixing coefficients BibRef

Wang, L.[Lu], Guo, D.[Dong], Wang, G.[Guotai], Zhang, S.T.[Shao-Ting],
Annotation-Efficient Learning for Medical Image Segmentation Based on Noisy Pseudo Labels and Adversarial Learning,
MedImg(40), No. 10, October 2021, pp. 2795-2807.
IEEE DOI 2110
Image segmentation, Training, Annotations, Shape, Biomedical imaging, Noise measurement, Deep learning, Segmentation, deep learning, noisy labels BibRef

Qu, L.[Lei], Wang, M.[Meng], Guo, K.X.[Kai-Xuan], Wan, W.[Wan], Liu, Y.[Yu], Tang, J.[Jun], Wu, J.[Jun], Duan, P.[Peng],
Biomedical image segmentation based on full-Resolution network,
PRL(153), 2022, pp. 232-238.
Elsevier DOI 2201
Image Segmentation, Biomedical Image, Full-resolution, Convolutional Neural Network BibRef

Wang, R.S.[Ri-Sheng], Lei, T.[Tao], Cui, R.X.[Rui-Xia], Zhang, B.T.[Bing-Tao], Meng, H.Y.[Hong-Ying], Nandi, A.K.[Asoke K.],
Medical image segmentation using deep learning: A survey,
IET-IPR(16), No. 5, 2022, pp. 1243-1267.
DOI Link 2203
Survey, Medical Images. BibRef

Minaee, S.[Shervin], Boykov, Y.Y.[Yuri Y.], Porikli, F.[Fatih], Plaza, A.[Antonio], Kehtarnavaz, N.[Nasser], Terzopoulos, D.[Demetri],
Image Segmentation Using Deep Learning: A Survey,
PAMI(44), No. 7, July 2022, pp. 3523-3542.
IEEE DOI 2206
Image segmentation, Computer architecture, Semantics, Deep learning, Computational modeling, medical image segmentation BibRef

Zhang, Z.X.[Zhen-Xi], Tian, C.[Chunna], Gao, X.B.[Xin-Bo], Li, J.[Jie], Jiao, Z.C.[Zhi-Cheng], Wang, C.[Cui], Zhong, Z.[Zhusi],
Collaborative boundary-aware context encoding networks for error map prediction,
PR(125), 2022, pp. 108515.
Elsevier DOI 2203
Segmentation quality assessment, Error map prediction, Medical image segmentation BibRef

Shi, Y.H.[Ying-Huan], Zhang, J.[Jian], Ling, T.[Tong], Lu, J.W.[Ji-Wen], Zheng, Y.F.[Ye-Feng], Yu, Q.[Qian], Qi, L.[Lei], Gao, Y.[Yang],
Inconsistency-Aware Uncertainty Estimation for Semi-Supervised Medical Image Segmentation,
MedImg(41), No. 3, March 2022, pp. 608-620.
IEEE DOI 2203
Uncertainty, Image segmentation, Estimation, Training, Biomedical imaging, Computational modeling, Task analysis, conservative-radical networks BibRef

Gut, D.[Daniel], Tabor, Z.[Zbislaw], Szymkowski, M.[Mateusz], Rozynek, M.[Milosz], Kucybala, I.[Iwona], Wojciechowski, W.[Wadim],
Benchmarking of Deep Architectures for Segmentation of Medical Images,
MedImg(41), No. 11, November 2022, pp. 3231-3241.
IEEE DOI 2211
Image segmentation, Task analysis, Training, Biomedical imaging, Computed tomography, Magnetic resonance imaging, Benchmark, segmentation BibRef

Zhang, Y.C.[Yun-Chu], Dong, J.F.[Jian-Fei],
2K-Fold-Net and feature enhanced 4-Fold-Net for medical image segmentation,
PR(127), 2022, pp. 108625.
Elsevier DOI 2205
2K-Fold-Net, EF-Net, U-Net, AFE, Image segmentation BibRef

Wang, K.[Kun], Zhang, X.H.[Xiao-Hong], Zhang, X.B.[Xiang-Bo], Lu, Y.T.[Yu-Ting], Huang, S.[Sheng], Yang, D.[Dan],
EANet: Iterative edge attention network for medical image segmentation,
PR(127), 2022, pp. 108636.
Elsevier DOI 2205
Medical image segmentation, Dynamic scale-aware context, Edge attention preservation, Multi-level pairwise regression, Computer-aided diagnosis (CAD) BibRef

Liu, Z.H.[Zi-Hao], Li, Z.W.[Zhuo-Wei], Hu, Z.Q.[Zhi-Qiang], Xia, Q.[Qing], Xiong, R.Q.[Rui-Qin], Zhang, S.T.[Shao-Ting], Jiang, T.T.[Ting-Ting],
Contrastive and Selective Hidden Embeddings for Medical Image Segmentation,
MedImg(41), No. 11, November 2022, pp. 3398-3410.
IEEE DOI 2211
Code, Segmentation.
WWW Link. Uncertainty, Image segmentation, Training, Task analysis, Medical diagnostic imaging, Decoding, neural network BibRef

Song, J.H.[Jia-Huan], Chen, X.J.[Xin-Jian], Zhu, Q.[Qianlong], Shi, F.[Fei], Xiang, D.[Dehui], Chen, Z.Y.[Zhong-Yue], Fan, Y.[Ying], Pan, L.J.[Ling-Jiao], Zhu, W.F.[Wei-Fang],
Global and Local Feature Reconstruction for Medical Image Segmentation,
MedImg(41), No. 9, September 2022, pp. 2273-2284.
IEEE DOI 2209
Feature extraction, Image reconstruction, Semantics, Image segmentation, Convolution, Biomedical imaging, Task analysis, local feature reconstruction module BibRef

Nan, Y.[Yang], Tang, P.[Peng], Zhang, G.[Guyue], Zeng, C.H.[Cai-Hong], Liu, Z.H.[Zhi-Hong], Gao, Z.[Zhifan], Zhang, H.[Heye], Yang, G.[Guang],
Unsupervised Tissue Segmentation via Deep Constrained Gaussian Network,
MedImg(41), No. 12, December 2022, pp. 3799-3811.
IEEE DOI 2212
Image segmentation, Pathology, Training, Manuals, Annotations, Deep learning, Unsupervised learning, Semantic segmentation, tissue segmentation BibRef

Zheng, R.F.[Rui-Feng], Zhong, Y.[Ying], Yan, S.[Senxiang], Sun, H.C.[Hong-Cheng], Shen, H.B.[Hai-Bin], Huang, K.[Kejie],
MsVRL: Self-Supervised Multiscale Visual Representation Learning via Cross-Level Consistency for Medical Image Segmentation,
MedImg(42), No. 1, January 2023, pp. 91-102.
IEEE DOI 2301
Image segmentation, Task analysis, Visualization, Self-supervised learning, Medical diagnostic imaging, abdomen BibRef

Hu, S.S.[Shi-Shuai], Liao, Z.[Zehui], Zhang, J.P.[Jian-Peng], Xia, Y.[Yong],
Domain and Content Adaptive Convolution Based Multi-Source Domain Generalization for Medical Image Segmentation,
MedImg(42), No. 1, January 2023, pp. 233-244.
IEEE DOI 2301
Image segmentation, Adaptation models, Head, Biomedical imaging, Convolution, Training, Data models, Domain generalization, deep learning BibRef

Valverde, J.M.[Juan Miguel], Tohka, J.[Jussi],
Region-wise loss for biomedical image segmentation,
PR(136), 2023, pp. 109208.
Elsevier DOI 2301
Deep learning, Segmentation, Medical imaging, Loss function BibRef

Yuan, F.N.[Fei-Niu], Zhang, Z.X.[Zheng-Xiao], Fang, Z.J.[Zhi-Jun],
An effective CNN and Transformer complementary network for medical image segmentation,
PR(136), 2023, pp. 109228.
Elsevier DOI 2301
Transformer, Medical image segmentation, Feature complementary module, Cross-domain fusion, Convolutional Neural Network BibRef


Guo, P.F.[Peng-Fei], Yang, D.[Dong], Hatamizadeh, A.[Ali], Xu, A.[An], Xu, Z.[Ziyue], Li, W.Q.[Wen-Qi], Zhao, C.[Can], Xu, D.[Daguang], Harmon, S.[Stephanie], Turkbey, E.[Evrim], Turkbey, B.[Baris], Wood, B.[Bradford], Patella, F.[Francesca], Stellato, E.[Elvira], Carrafiello, G.[Gianpaolo], Patel, V.M.[Vishal M.], Roth, H.R.[Holger R.],
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation,
ECCV22(XXI:437-455).
Springer DOI 2211
BibRef

Wang, J.C.[Jia-Cheng], Jin, Y.M.[Yue-Ming], Wang, L.S.[Lian-Sheng],
Personalizing Federated Medical Image Segmentation via Local Calibration,
ECCV22(XXI:456-472).
Springer DOI 2211
BibRef

Wang, W.X.[Wen-Xuan], Chen, C.[Chen], Wang, J.[Jing], Zha, S.[Sen], Zhang, Y.[Yan], Li, J.Y.[Jiang-Yun],
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation,
ECCV22(XXI:506-522).
Springer DOI 2211
BibRef

Zhou, Z.Q.[Zi-Qi], Qi, L.[Lei], Shi, Y.[Yinghuan],
Generalizable Medical Image Segmentation via Random Amplitude Mixup and Domain-Specific Image Restoration,
ECCV22(XXI:420-436).
Springer DOI 2211
BibRef

Gupta, S.[Saumya], Hu, X.L.[Xiao-Ling], Kaan, J.[James], Jin, M.[Michael], Mpoy, M.[Mutshipay], Chung, K.[Katherine], Singh, G.[Gagandeep], Saltz, M.[Mary], Kurc, T.[Tahsin], Saltz, J.[Joel], Tassiopoulos, A.[Apostolos], Prasanna, P.[Prateek], Chen, C.[Chao],
Learning Topological Interactions for Multi-Class Medical Image Segmentation,
ECCV22(XXIX:701-718).
Springer DOI 2211
BibRef

Liu, L.[Libo], Fan, X.X.[Xin-Xin], Zhang, X.D.[Xiao-Dong], Hu, Q.M.[Qing-Mao],
Lightweight Dual-Domain Network for Real-Time Medical Image Segmentation,
ICIP22(396-400)
IEEE DOI 2211
Image segmentation, Convolution, Frequency-domain analysis, Semantics, Surgery, Stroke (medical condition), Real-time systems, lightweight dual-domain network BibRef

Cheng, J.L.[Jun-Long], Gao, C.[Chengrui], Li, C.[Changlin], Ming, Z.Q.[Zhang-Qiang], Yang, Y.[Yong], Wang, F.J.[Feng-Jie], Zhu, M.[Min],
F2RNET: A Full-Resolution Representation Network for Biomedical Image Segmentation,
ICIP22(2406-2410)
IEEE DOI 2211
Deep learning, Image segmentation, Image resolution, Convolution, Multilayer perceptrons, Feature extraction, Transformers, Biomedical image segmentation BibRef

Wu, H.[Huisi], Xiao, F.Y.[Fang-Yan], Liang, C.X.[Chong-Xin],
Dual Contrastive Learning with Anatomical Auxiliary Supervision for Few-Shot Medical Image Segmentation,
ECCV22(XX:417-434).
Springer DOI 2211
BibRef

Zhao, Z.Y.[Zi-Yuan], Zhu, A.D.[An-Dong], Zeng, Z.[Zeng], Veeravalli, B.[Bharadwaj], Guan, C.T.[Cun-Tai],
ACT-NET: Asymmetric Co-Teacher Network for Semi-Supervised Memory-Efficient Medical Image Segmentation,
ICIP22(1426-1430)
IEEE DOI 2211
Knowledge engineering, Image segmentation, Limiting, Computational modeling, Employment, Data models, medical image segmentation BibRef

Khan, T.M.[Tariq M.], Robles-Kelly, A.[Antonio], Naqvi, S.S.[Syed S.],
T-Net: A Resource-Constrained Tiny Convolutional Neural Network for Medical Image Segmentation,
WACV22(1799-1808)
IEEE DOI 2202
Image segmentation, Skin, Retinal vessels, Mobile handsets, Lesions, Convolutional neural networks, Image Processing BibRef

Hatamizadeh, A.[Ali], Tang, Y.C.[Yu-Cheng], Nath, V.[Vishwesh], Yang, D.[Dong], Myronenko, A.[Andriy], Landman, B.[Bennett], Roth, H.R.[Holger R.], Xu, D.G.[Da-Guang],
UNETR: Transformers for 3D Medical Image Segmentation,
WACV22(1748-1758)
IEEE DOI 2202
Image segmentation, Semantics, Computer architecture, Transformers, Natural language processing, Medical Imaging/Imaging for Bioinformatics/Biological and Cell Microscopy BibRef

Shi, D.C.[Da-Chuan], Liu, R.Y.[Rui-Yang], Tao, L.M.[Lin-Mi], He, Z.X.[Zuo-Xiang], Huo, L.[Li],
Multi-Encoder Parse-Decoder Network for Sequential Medical Image Segmentation,
ICIP21(31-35)
IEEE DOI 2201
Training, Manifolds, Image segmentation, Neural networks, Feature extraction, Decoding, Data mining, Convolutional neural networks BibRef

Koker, T.[Teddy], Mireshghallah, F.[Fatemehsadat], Titcombe, T.[Tom], Kaissis, G.[Georgios],
U-Noise: Learnable Noise Masks for Interpretable Image Segmentation,
ICIP21(394-398)
IEEE DOI 2201
Deep learning, Image segmentation, Sensitivity, Computed tomography, Decision making, Distance measurement, Medical Imaging BibRef

Bhide, S., Mikut, R., Leptin, M., Stegmaier, J.,
Semi-Automatic Generation Of Tight Binary Masks And Non-Convex Isosurfaces For Quantitative Analysis of 3D Biological Samples,
ICIP20(2820-2824)
IEEE DOI 2011
Image segmentation, Embryo, Shape, Isosurfaces, GUI BibRef

Chang, Q., Qu, H., Zhang, Y., Sabuncu, M., Chen, C., Zhang, T., Metaxas, D.N.,
Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data,
CVPR20(13853-13863)
IEEE DOI 2008
Biomedical imaging, Generators, Data privacy, Task analysis, Image segmentation, Data models BibRef

Smith, T.J.[Thomas J.], Valstar, M.[Michel], Sharkey, D.[Don], Crowe, J.[John],
Clinical Scene Segmentation with Tiny Datasets,
CVPM19(1637-1645)
IEEE DOI 2004
convolutional neural nets, graph theory, image representation, image segmentation, learning (artificial intelligence), End to end BibRef

Xu, X., Lu, Q., Yang, L., Hu, S., Chen, D., Hu, Y., Shi, Y.,
Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation,
CVPR18(8300-8308)
IEEE DOI 1812
Quantization (signal), Training, Biomedical imaging, Image segmentation, Uncertainty, Memory management, Neural networks BibRef

Kromp, F., Ambros, I., Weiss, T., Bogen, D., Dodig, H., Berneder, M., Gerber, T., Taschner-Mandl, S., Ambros, P., Hanbury, A.,
Machine learning framework incorporating expert knowledge in tissue image annotation,
ICPR16(343-348)
IEEE DOI 1705
Algorithm design and analysis, Biological tissues, Image segmentation, Machine learning algorithms, Morphology, Prediction algorithms, image annotation, machine learning, online, training BibRef

Mesbah, S., Shalaby, A., Willhite, A., Harkema, S., Rejc, E., El-baz, A.,
Automatic 3-D muscle and fat segmentation of thigh magnetic resonance images in individuals with spinal cord injury,
ICIP17(3280-3284)
IEEE DOI 1803
Markov processes, biomedical MRI, diseases, image registration, image segmentation, injuries, medical disorders, SCI BibRef

Jarrar, M., Kerkeni, A., Abdallah, A.B., Bedoui, M.H.,
MLP Neural Network Classifier for Medical Image Segmentation,
CGiV16(88-93)
IEEE DOI 1608
image classification BibRef

Zhu, H.[Hong], Xu, J.H.[Jin-Hui], Hu, J.F.[Jun-Feng], Chen, J.[Jing],
Medical Image Segmentation Using Improved Affinity Propagation,
CompIMAGE16(208-215).
Springer DOI 1704
Affinity Propagation (AP) vs. Nearest Neighbor classification. BibRef

Masci, J.[Jonathan], Giusti, A.[Alessandro], Ciresan, D.C.[Dan C.], Fricout, G.[Gabriel], Schmidhuber, J.[Jurgen],
A fast learning algorithm for image segmentation with max-pooling convolutional networks,
ICIP13(2713-2717)
IEEE DOI 1402
Convolutional Network BibRef

Giusti, A.[Alessandro], Ciresan, D.C.[Dan C.], Masci, J.[Jonathan], Gambardella, L.M.[Luca M.], Schmidhuber, J.[Jurgen],
Fast image scanning with deep max-pooling convolutional neural networks,
ICIP13(4034-4038)
IEEE DOI 1402
Biomedical Imaging BibRef

Ding, J.J.[Jian-Jiun], Wang, Y.H.[Yu-Hsiang], Hu, L.L.[Lee-Lin], Chao, W.L.[Wei-Lun], Shau, Y.W.[Yio-Wha],
Muscle injury determination by image segmentation,
VCIP11(1-4).
IEEE DOI 1201
BibRef

Kamarainen, J.K.[Joni-Kristian], Lensu, L.[Lasse], Kauppi, T.[Tomi],
Combining Multiple Image Segmentations by Maximizing Expert Agreement,
MLMI12(193-200).
Springer DOI 1211
BibRef

Pham, T.D.[Tuan D.], Eisenblatter, U.[Uwe], Golledge, J.[Jonathan], Baune, B.T.[Bernhard T.], Berger, K.[Klaus],
Segmentation of medical images using geo-theoretic distance matrix in fuzzy clustering,
ICIP09(3369-3372).
IEEE DOI 0911
BibRef

Luong, H.V.[Hyunh Van], Kim, J.M.[Jong Myon],
A New Parallel Approach to Fuzzy Clustering for Medical Image Segmentation,
ISVC08(I: 1092-1101).
Springer DOI 0812
BibRef

Vannier, M.W.[Michael W.], Haller, J.W.,
Biomedical image segmentation,
ICIP98(II: 20-24).
IEEE DOI 9810
BibRef

Wegner, S., Harms, T., Oswald, H., Fleck, E.,
The watershed transformation on graphs for the segmentation of CT images,
ICPR96(III: 498-502).
IEEE DOI 0509
BibRef
Earlier:
Medical image segmentation using the watershed transformation on graphs,
ICIP96(III: 37-40).
IEEE DOI 9610
Image Segmentation for a Hyperthermia Planning Environment BibRef

Wegner, S., Harms, T., Builtjes, J.H., Oswald, H., Fleck, E.,
The watershed transformation for multiresolution image segmentation,
CIAP95(31-36).
Springer DOI 9509
BibRef

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Weakly Supervised, Self Supervised Semantic Segmentation .


Last update:Jan 29, 2023 at 20:54:24