8.7.1.2.6 Interactive Medical Image Segmentations

Chapter Contents (Back)
Deformable Curves. Segmentation. Interactive Segmentation. Of course, a lot of medical imagery is 3D:
See also Interactive 3D Segmentations, Depth, Range, Stereo.

Falcao, A.X.[Alexandre X.], Udupa, J.K.[Jayaram K.], Samarasekera, S.[Supun], Sharma, S.[Shoba], Hirsch, B.E.[Bruce Elliot], de Alencar Lotufo, R.[Roberto],
User-Steered Image Segmentation Paradigms: Live Wire and Live Lane,
GMIP(60), No. 4, July 1998, pp. 233-260. BibRef 9807

Xavier Falcão, A.[Alexandre], Udupa, J.K.[Jayaram K.], Miyazawa, F.K.,
An ultra-fast user-steered image segmentation paradigm: Live wire on the fly,
MedImg(19), No. 1, January 2000, pp. 55-62.
IEEE Top Reference. 0110
BibRef

Harders, M., Szekely, G.,
Enhancing human-computer interaction in medical segmentation,
PIEEE(91), No. 9, September 2003, pp. 1430-1442.
IEEE DOI 0309
BibRef

Zhao, F.[Feng], Xie, X.H.[Xiang-Hua],
An Overview of Interactive Medical Image Segmentation,
BMVA(2013), No. 1, 2013, pp. 7, 1-22.
PDF File. 1304
Survey, Segmentation. BibRef

Kurtek, S.[Sebastian], Su, J.Y.[Jing-Yong], Grimm, C.[Cindy], Vaughan, M.[Michelle], Sowell, R.[Ross], Srivastava, A.[Anuj],
Statistical analysis of manual segmentations of structures in medical images,
CVIU(117), No. 9, 2013, pp. 1036-1050.
Elsevier DOI 1307
Medical imaging BibRef

van Opbroek, A.[Annegreet], Ikram, M.A.[M. Arfan], Vernooij, M.W.[Meike W.], de Bruijne, M.[Marleen],
Transfer Learning Improves Supervised Image Segmentation Across Imaging Protocols,
MedImg(34), No. 5, May 2015, pp. 1018-1030.
IEEE DOI 1505
BibRef
Earlier:
Supervised Image Segmentation across Scanner Protocols: A Transfer Learning Approach,
MLMI12(160-167).
Springer DOI 1211
Biomedical imaging BibRef

Mahapatra, D.[Dwarikanath],
Combining multiple expert annotations using semi-supervised learning and graph cuts for medical image segmentation,
CVIU(151), No. 1, 2016, pp. 114-123.
Elsevier DOI 1610
Multiple experts BibRef

Mahapatra, D.[Dwarikanath],
Semi-supervised learning and graph cuts for consensus based medical image segmentation,
PR(63), No. 1, 2017, pp. 700-709.
Elsevier DOI 1612
Multiple experts BibRef

Lin, L., Yang, W., Li, C., Tang, J., Cao, X.,
Inference With Collaborative Model for Interactive Tumor Segmentation in Medical Image Sequences,
Cyber(46), No. 12, December 2016, pp. 2796-2809.
IEEE DOI 1612
Biomedical imaging BibRef

Valvano, G.[Gabriele], Leo, A.[Andrea], Tsaftaris, S.A.[Sotirios A.],
Learning to Segment From Scribbles Using Multi-Scale Adversarial Attention Gates,
MedImg(40), No. 8, August 2021, pp. 1990-2001.
IEEE DOI 2108
Image segmentation, Annotations, Shape, Training, Logic gates, Semantics, Weak supervision, scribbles, shape Priors BibRef

Lu, Y.H.[Yu-Hang], Zheng, K.[Kang], Li, W.J.[Wei-Jian], Wang, Y.R.[Yi-Rui], Harrison, A.P.[Adam P.], Lin, C.H.[Chi-Hung], Wang, S.[Song], Xiao, J.[Jing], Lu, L.[Le], Kuo, C.F.[Chang-Fu], Miao, S.[Shun],
Contour Transformer Network for One-Shot Segmentation of Anatomical Structures,
MedImg(40), No. 10, October 2021, pp. 2672-2684.
IEEE DOI 2110
Image segmentation, Shape, Training, Biomedical imaging, Anatomical structure, X-ray imaging, Task analysis, human-in-the-loop BibRef

Zhang, S.Y.[Shi-Yin], Wei, S.K.[Shi-Kui], Liew, J.H.[Jun Hao], Han, K.Y.[Kun-Yang], Zhao, Y.[Yao], Wei, Y.C.[Yun-Chao],
Interactive Object Segmentation With Inside-Outside Guidance,
PAMI(45), No. 7, July 2023, pp. 8594-8605.
IEEE DOI 2306
BibRef
Earlier: A1, A3, A6, A2, A5, Only: CVPR20(12231-12241)
IEEE DOI 2008
Image segmentation, Annotations, Task analysis, Benchmark testing, Training, Semantic segmentation, Optimization, Deep learning, interactive segmentation. Semantics, Labeling, Heating systems, Task analysis, Biomedical imaging BibRef

Fan, S.Q.[Si-Qi], Zhu, F.H.[Feng-Hua], Feng, Z.L.[Zun-Lei], Lv, Y.S.[Yi-Sheng], Song, M.L.[Ming-Li], Wang, F.Y.[Fei-Yue],
Conservative-Progressive Collaborative Learning for Semi-Supervised Semantic Segmentation,
IP(32), 2023, pp. 6183-6194.
IEEE DOI 2311
BibRef

Li, Z.[Zihan], Zheng, Y.[Yuan], Shan, D.D.[Dan-Dan], Yang, S.Z.[Shu-Zhou], Li, Q.[Qingde], Wang, B.Z.[Bei-Zhan], Zhang, Y.T.[Yuan-Ting], Hong, Q.Q.[Qing-Qi], Shen, D.G.[Ding-Gang],
ScribFormer: Transformer Makes CNN Work Better for Scribble-Based Medical Image Segmentation,
MedImg(43), No. 6, June 2024, pp. 2254-2265.
IEEE DOI Code:
WWW Link. 2406
Image segmentation, Transformers, Convolutional neural networks, Annotations, Training, Decoding, Medical diagnostic imaging, scribble-supervised learning BibRef

Shi, Q.X.[Qing-Xuan], Li, Y.H.[Yi-Hang], Di, H.J.[Hui-Jun], Wu, E.[Enyi],
Self-Supervised Interactive Image Segmentation,
CirSysVideo(34), No. 8, August 2024, pp. 6797-6808.
IEEE DOI Code:
WWW Link. 2408
Image segmentation, Feature extraction, Task analysis, Medical diagnostic imaging, Training, Adaptation models, generalization BibRef

Marinov, Z.[Zdravko], Jäger, P.F.[Paul F.], Egger, J.[Jan], Kleesiek, J.[Jens], Stiefelhagen, R.[Rainer],
Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy,
PAMI(46), No. 12, December 2024, pp. 10998-11018.
IEEE DOI 2411
Image segmentation, Training, Taxonomy, Predictive models, Robots, Annotations, Deep learning, Deep learning, systematic review BibRef

Chen, W.T.[Wen-Ting], Liu, J.[Jie], Liu, T.M.[Tian-Ming], Yuan, Y.X.[Yi-Xuan],
Bi-VLGM: Bi-Level Class-Severity-Aware Vision-Language Graph Matching for Text Guided Medical Image Segmentation,
IJCV(133), No. 3, March 2025, pp. 1375-1391.
Springer DOI 2502
BibRef


Wong, H.E.[Hallee E.], Rakic, M.[Marianne], Guttag, J.[John], Dalca, A.V.[Adrian V.],
Scribbleprompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image,
ECCV24(XL: 207-229).
Springer DOI 2412
BibRef

Rao, A.[Adrit], Fisher, A.[Andrea], Chang, K.[Ken], Panagides, J.C.[John Christopher], McNamara, K.[Katherine], Lee, J.Y.[Joon-Young], Aalami, O.[Oliver],
IMIL: Interactive Medical Image Learning Framework,
DEF-AI-MIA24(5241-5250)
IEEE DOI 2410
Training, Image analysis, Accuracy, Computational modeling, Radiology, Data augmentation, Interactive learning, Data augmentation BibRef

Jin, J.Q.[Jia-Qi], Tong, X.F.[Xu-Feng],
An improved algorithm for interactive medical image segmentation based on Intelligent scissors,
CVIDL23(175-179)
IEEE DOI 2403
Image segmentation, Pathology, Machine learning algorithms, Image edge detection, Intelligent scissors algorithm BibRef

Zhou, Y.[Yi], He, X.D.[Xiao-Dong], Huang, L.[Lei], Liu, L.[Li], Zhu, F.[Fan], Cui, S.S.[Shan-Shan], Shao, L.[Ling],
Collaborative Learning of Semi-Supervised Segmentation and Classification for Medical Images,
CVPR19(2074-2083).
IEEE DOI 2002
BibRef

Leon, L.M.C.[Leissi Margarita Castañeda], de Miranda, P.A.V.[Paulo André Vechiatto],
Efficient Interactive Multi-object Segmentation in Medical Images,
WiCV-E18(IV:705-710).
Springer DOI 1905
BibRef

Herve, N.[Nicolas], Servais, A.[Aude], Thervet, E.[Eric], Zhu, Y.X.[Ying-Xuan], Cheng, S.[Samuel], Goel, A.[Amrit],
Interactive segmentation of medical images using belief propagation with level sets,
ICIP10(4113-4116).
IEEE DOI 1009
BibRef

Jagadeesh, V.[Vignesh], Manjunath, B.S.,
Interactive graph cut segmentation of touching neuronal structures from electron micrographs,
ICIP10(3625-3628).
IEEE DOI 1009
BibRef

Vu, N.[Nhat], Manjunath, B.S.,
Graph cut segmentation of neuronal structures from transmission electron micrographs,
ICIP08(725-728).
IEEE DOI 0810
BibRef

Garibotto, G.[Giovanni], Garibotto, V.[Valentina],
Edge Tracking of subjective contours in Biomedical Imaging,
CIAP07(737-742).
IEEE DOI 0709
BibRef

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Interactive 3D Segmentations, Depth, Range, Stereo .


Last update:Mar 17, 2025 at 20:02:03