257 related articles for article (PubMed ID: 33242074)
1. Advances and opportunities in image analysis of bacterial cells and communities.
Jeckel H; Drescher K
FEMS Microbiol Rev; 2021 Aug; 45(4):. PubMed ID: 33242074
[TBL] [Abstract][Full Text] [Related]
2. BCM3D 2.0: accurate segmentation of single bacterial cells in dense biofilms using computationally generated intermediate image representations.
Zhang J; Wang Y; Donarski ED; Toma TT; Miles MT; Acton ST; Gahlmann A
NPJ Biofilms Microbiomes; 2022 Dec; 8(1):99. PubMed ID: 36529755
[TBL] [Abstract][Full Text] [Related]
3. Application of convolutional neural networks towards nuclei segmentation in localization-based super-resolution fluorescence microscopy images.
Mela CA; Liu Y
BMC Bioinformatics; 2021 Jun; 22(1):325. PubMed ID: 34130628
[TBL] [Abstract][Full Text] [Related]
4. Analytics and visualization tools to characterize single-cell stochasticity using bacterial single-cell movie cytometry data.
Balomenos AD; Stefanou V; Manolakos ES
BMC Bioinformatics; 2021 Oct; 22(1):531. PubMed ID: 34715773
[TBL] [Abstract][Full Text] [Related]
5. Convolutional Neural Networks for Classifying Chromatin Morphology in Live-Cell Imaging.
Ulicna K; Ho LTL; Soelistyo CJ; Day NJ; Lowe AR
Methods Mol Biol; 2022; 2476():17-30. PubMed ID: 35635694
[TBL] [Abstract][Full Text] [Related]
6. Image analysis driven single-cell analytics for systems microbiology.
Balomenos AD; Tsakanikas P; Aspridou Z; Tampakaki AP; Koutsoumanis KP; Manolakos ES
BMC Syst Biol; 2017 Apr; 11(1):43. PubMed ID: 28376782
[TBL] [Abstract][Full Text] [Related]
7. Quantitative image analysis of microbial communities with BiofilmQ.
Hartmann R; Jeckel H; Jelli E; Singh PK; Vaidya S; Bayer M; Rode DKH; Vidakovic L; Díaz-Pascual F; Fong JCN; Dragoš A; Lamprecht O; Thöming JG; Netter N; Häussler S; Nadell CD; Sourjik V; Kovács ÁT; Yildiz FH; Drescher K
Nat Microbiol; 2021 Feb; 6(2):151-156. PubMed ID: 33398098
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Learning image-based spatial transformations via convolutional neural networks: A review.
Tustison NJ; Avants BB; Gee JC
Magn Reson Imaging; 2019 Dec; 64():142-153. PubMed ID: 31200026
[TBL] [Abstract][Full Text] [Related]
10. Computer Vision-Based Image Analysis of Bacteria.
Danielsen J; Nordenfelt P
Methods Mol Biol; 2017; 1535():161-172. PubMed ID: 27914078
[TBL] [Abstract][Full Text] [Related]
11. An analytical tool that quantifies cellular morphology changes from three-dimensional fluorescence images.
Haass-Koffler CL; Naeemuddin M; Bartlett SE
J Vis Exp; 2012 Aug; (66):e4233. PubMed ID: 22951512
[TBL] [Abstract][Full Text] [Related]
12. Automatic identification of crossovers in cryo-EM images of murine amyloid protein A fibrils with machine learning.
Weber M; Bäuerle A; Schmidt M; Neumann M; Fändrich M; Ropinski T; Schmidt V
J Microsc; 2020 Jan; 277(1):12-22. PubMed ID: 31859366
[TBL] [Abstract][Full Text] [Related]
13. White blood cells detection and classification based on regional convolutional neural networks.
Kutlu H; Avci E; Özyurt F
Med Hypotheses; 2020 Feb; 135():109472. PubMed ID: 31760248
[TBL] [Abstract][Full Text] [Related]
14. Machine learning and image analysis in vascular surgery.
Tomihama RT; Dass S; Chen S; Kiang SC
Semin Vasc Surg; 2023 Sep; 36(3):413-418. PubMed ID: 37863613
[TBL] [Abstract][Full Text] [Related]
15. Synthetic Micrographs of Bacteria (SyMBac) allows accurate segmentation of bacterial cells using deep neural networks.
Hardo G; Noka M; Bakshi S
BMC Biol; 2022 Nov; 20(1):263. PubMed ID: 36447211
[TBL] [Abstract][Full Text] [Related]
16. A convolutional neural network for segmentation of yeast cells without manual training annotations.
Kruitbosch HT; Mzayek Y; Omlor S; Guerra P; Milias-Argeitis A
Bioinformatics; 2022 Feb; 38(5):1427-1433. PubMed ID: 34893817
[TBL] [Abstract][Full Text] [Related]
17. Learning fuzzy clustering for SPECT/CT segmentation via convolutional neural networks.
Chen J; Li Y; Luna LP; Chung HW; Rowe SP; Du Y; Solnes LB; Frey EC
Med Phys; 2021 Jul; 48(7):3860-3877. PubMed ID: 33905560
[TBL] [Abstract][Full Text] [Related]
18. Software for segmenting and quantifying calcium signals using multi-scale generative adversarial networks.
Moghnieh H; Kamran SA; Hossain KF; Kuol N; Riar S; Bartlett A; Tavakkoli A; Baker SA
STAR Protoc; 2022 Dec; 3(4):101852. PubMed ID: 36595928
[TBL] [Abstract][Full Text] [Related]
19. Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images.
Alaskar H; Hussain A; Al-Aseem N; Liatsis P; Al-Jumeily D
Sensors (Basel); 2019 Mar; 19(6):. PubMed ID: 30871162
[TBL] [Abstract][Full Text] [Related]
20. Training a deep learning model for single-cell segmentation without manual annotation.
Din NU; Yu J
Sci Rep; 2021 Dec; 11(1):23995. PubMed ID: 34907213
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
