123 related articles for article (PubMed ID: 37066636)
1. Single-cell segmentation in bacterial biofilms with an optimized deep learning method enables tracking of cell lineages and measurements of growth rates.
Jelli E; Ohmura T; Netter N; Abt M; Jiménez-Siebert E; Neuhaus K; Rode DKH; Nadell CD; Drescher K
Mol Microbiol; 2023 Jun; 119(6):659-676. PubMed ID: 37066636
[TBL] [Abstract][Full Text] [Related]
2. Non-invasive single-cell morphometry in living bacterial biofilms.
Zhang M; Zhang J; Wang Y; Wang J; Achimovich AM; Acton ST; Gahlmann A
Nat Commun; 2020 Dec; 11(1):6151. PubMed ID: 33262347
[TBL] [Abstract][Full Text] [Related]
3. 3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images.
Wen C; Miura T; Voleti V; Yamaguchi K; Tsutsumi M; Yamamoto K; Otomo K; Fujie Y; Teramoto T; Ishihara T; Aoki K; Nemoto T; Hillman EM; Kimura KD
Elife; 2021 Mar; 10():. PubMed ID: 33781383
[TBL] [Abstract][Full Text] [Related]
4. Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets.
Kar A; Petit M; Refahi Y; Cerutti G; Godin C; Traas J
PLoS Comput Biol; 2022 Apr; 18(4):e1009879. PubMed ID: 35421081
[TBL] [Abstract][Full Text] [Related]
5. SC-Track: a robust cell-tracking algorithm for generating accurate single-cell lineages from diverse cell segmentations.
Li C; Xie SS; Wang J; Sharvia S; Chan KY
Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38704671
[TBL] [Abstract][Full Text] [Related]
6. A method for the evaluation of thousands of automated 3D stem cell segmentations.
Bajcsy P; Simon M; Florczyk SJ; Simon CG; Juba D; Brady MC
J Microsc; 2015 Dec; 260(3):363-76. PubMed ID: 26268699
[TBL] [Abstract][Full Text] [Related]
7. A robust transformer-based pipeline of 3D cell alignment, denoise and instance segmentation on electron microscopy sequence images.
Liu J; Zheng Y; Lin L; Guo J; Lv Y; Yuan J; Zhai H; Chen X; Shen L; Li L; Bai S; Han H
J Plant Physiol; 2024 Jun; 297():154236. PubMed ID: 38621330
[TBL] [Abstract][Full Text] [Related]
8. CellSNAP: a fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging.
Raj P; Paidi SK; Conway L; Chatterjee A; Barman I
J Biomed Opt; 2024 Jun; 29(Suppl 2):S22706. PubMed ID: 38638450
[TBL] [Abstract][Full Text] [Related]
9. Automated segmentation and recognition of C. elegans whole-body cells.
Li Y; Lai C; Wang M; Wu J; Li Y; Peng H; Qu L
Bioinformatics; 2024 May; 40(5):. PubMed ID: 38775410
[TBL] [Abstract][Full Text] [Related]
10. Tracking cell lineages in 3D by incremental deep learning.
Sugawara K; Çevrim Ç; Averof M
Elife; 2022 Jan; 11():. PubMed ID: 34989675
[TBL] [Abstract][Full Text] [Related]
11. Confocal DNA cytometry: a contour-based segmentation algorithm for automated three-dimensional image segmentation.
Beliën JA; van Ginkel HA; Tekola P; Ploeger LS; Poulin NM; Baak JP; van Diest PJ
Cytometry; 2002 Sep; 49(1):12-21. PubMed ID: 12210606
[TBL] [Abstract][Full Text] [Related]
12. Quantitative Analysis of Differentiation Activity for Mouse Embryonic Stem Cells by Deep Learning for Cell Center Detection using Three-Dimensional Confocal Fluorescence Microscopy Images.
Chu SL; Yokota H; Hsieh HL; Abe K; Cho D; Tsai MD
Annu Int Conf IEEE Eng Med Biol Soc; 2023 Jul; 2023():1-4. PubMed ID: 38083144
[TBL] [Abstract][Full Text] [Related]
13. DeepSeeded: Volumetric Segmentation of Dense Cell Populations with a Cascade of Deep Neural Networks in Bacterial Biofilm Applications.
Toma TT; Wang Y; Gahlmann A; Acton ST
Expert Syst Appl; 2024 Mar; 238(Pt D):. PubMed ID: 38646063
[TBL] [Abstract][Full Text] [Related]
14. 3D Optical Coherence Tomography image processing in BISCAP: characterization of biofilm structure and properties.
Narciso DAC; Pereira A; Dias NO; Monteiro M; Melo LF; Martins FG
Bioinformatics; 2024 Feb; 40(2):. PubMed ID: 38265243
[TBL] [Abstract][Full Text] [Related]
15. A photometric stereo-based 3D imaging system using computer vision and deep learning for tracking plant growth.
Bernotas G; Scorza LCT; Hansen MF; Hales IJ; Halliday KJ; Smith LN; Smith ML; McCormick AJ
Gigascience; 2019 May; 8(5):. PubMed ID: 31127811
[TBL] [Abstract][Full Text] [Related]
16. CEM500K, a large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning.
Conrad R; Narayan K
Elife; 2021 Apr; 10():. PubMed ID: 33830015
[TBL] [Abstract][Full Text] [Related]
17. Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images.
Caicedo JC; Roth J; Goodman A; Becker T; Karhohs KW; Broisin M; Molnar C; McQuin C; Singh S; Theis FJ; Carpenter AE
Cytometry A; 2019 Sep; 95(9):952-965. PubMed ID: 31313519
[TBL] [Abstract][Full Text] [Related]
18. Segmentor: a tool for manual refinement of 3D microscopy annotations.
Borland D; McCormick CM; Patel NK; Krupa O; Mory JT; Beltran AA; Farah TM; Escobar-Tomlienovich CF; Olson SS; Kim M; Wu G; Stein JL
BMC Bioinformatics; 2021 May; 22(1):260. PubMed ID: 34022787
[TBL] [Abstract][Full Text] [Related]
19. C1M2: a universal algorithm for 3D instance segmentation, annotation, and quantification of irregular cells.
Zheng H; Huang S; Zhang J; Zhang R; Wang J; Yuan J; Li A; Yang X; Zhang Z
Sci China Life Sci; 2023 Oct; 66(10):2415-2428. PubMed ID: 37243949
[TBL] [Abstract][Full Text] [Related]
20. 3D matting: A benchmark study on soft segmentation method for pulmonary nodules applied in computed tomography.
Wang L; Ye X; Zhang D; He W; Ju L; Luo Y; Luo H; Wang X; Feng W; Song K; Zhao X; Ge Z
Comput Biol Med; 2022 Nov; 150():106153. PubMed ID: 36228464
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
