133 related articles for article (PubMed ID: 28834185)
1. Automated analysis of individual sperm cells using stain-free interferometric phase microscopy and machine learning.
Mirsky SK; Barnea I; Levi M; Greenspan H; Shaked NT
Cytometry A; 2017 Sep; 91(9):893-900. PubMed ID: 28834185
[TBL] [Abstract][Full Text] [Related]
2. Partially spatially coherent digital holographic microscopy and machine learning for quantitative analysis of human spermatozoa under oxidative stress condition.
Dubey V; Popova D; Ahmad A; Acharya G; Basnet P; Mehta DS; Ahluwalia BS
Sci Rep; 2019 Mar; 9(1):3564. PubMed ID: 30837490
[TBL] [Abstract][Full Text] [Related]
3. Individual sperm selection by microfluidics integrated with interferometric phase microscopy.
Eravuchira PJ; Mirsky SK; Barnea I; Levi M; Balberg M; Shaked NT
Methods; 2018 Mar; 136():152-159. PubMed ID: 28958952
[TBL] [Abstract][Full Text] [Related]
4. Stain-free interferometric phase microscopy correlation with DNA fragmentation stain in human spermatozoa.
Barnea I; Karako L; Mirsky SK; Levi M; Balberg M; Shaked NT
J Biophotonics; 2018 Nov; 11(11):e201800137. PubMed ID: 29877620
[TBL] [Abstract][Full Text] [Related]
5. Machine learning predicts live-birth occurrence before in-vitro fertilization treatment.
Goyal A; Kuchana M; Ayyagari KPR
Sci Rep; 2020 Dec; 10(1):20925. PubMed ID: 33262383
[TBL] [Abstract][Full Text] [Related]
6. Interferometric phase microscopy for label-free morphological evaluation of sperm cells.
Haifler M; Girshovitz P; Band G; Dardikman G; Madjar I; Shaked NT
Fertil Steril; 2015 Jul; 104(1):43-7.e2. PubMed ID: 26003272
[TBL] [Abstract][Full Text] [Related]
7. A dictionary learning approach for human sperm heads classification.
Shaker F; Monadjemi SA; Alirezaie J; Naghsh-Nilchi AR
Comput Biol Med; 2017 Dec; 91():181-190. PubMed ID: 29100112
[TBL] [Abstract][Full Text] [Related]
8. Automatic segmentation of Sperm's parts in microscopic images of human semen smears using concatenated learning approaches.
Movahed RA; Mohammadi E; Orooji M
Comput Biol Med; 2019 Jun; 109():242-253. PubMed ID: 31096088
[TBL] [Abstract][Full Text] [Related]
9. Holographic virtual staining of individual biological cells.
Nygate YN; Levi M; Mirsky SK; Turko NA; Rubin M; Barnea I; Dardikman-Yoffe G; Haifler M; Shalev A; Shaked NT
Proc Natl Acad Sci U S A; 2020 Apr; 117(17):9223-9231. PubMed ID: 32284403
[TBL] [Abstract][Full Text] [Related]
10. Morphometric dimensions of the human sperm head depend on the staining method used.
Maree L; du Plessis SS; Menkveld R; van der Horst G
Hum Reprod; 2010 Jun; 25(6):1369-82. PubMed ID: 20400771
[TBL] [Abstract][Full Text] [Related]
11. Comparison and optimization of machine learning methods for automated classification of circulating tumor cells.
Lannin TB; Thege FI; Kirby BJ
Cytometry A; 2016 Oct; 89(10):922-931. PubMed ID: 27754580
[TBL] [Abstract][Full Text] [Related]
12. Real-Time Stain-Free Classification of Cancer Cells and Blood Cells Using Interferometric Phase Microscopy and Machine Learning.
Nissim N; Dudaie M; Barnea I; Shaked NT
Cytometry A; 2021 May; 99(5):511-523. PubMed ID: 32910546
[TBL] [Abstract][Full Text] [Related]
13. Localized measurements of physical parameters within human sperm cells obtained with wide-field interferometry.
Balberg M; Levi M; Kalinowski K; Barnea I; Mirsky SK; Shaked NT
J Biophotonics; 2017 Oct; 10(10):1305-1314. PubMed ID: 28079304
[TBL] [Abstract][Full Text] [Related]
14. Quantitative phase microscopy spatial signatures of cancer cells.
Roitshtain D; Wolbromsky L; Bal E; Greenspan H; Satterwhite LL; Shaked NT
Cytometry A; 2017 May; 91(5):482-493. PubMed ID: 28426133
[TBL] [Abstract][Full Text] [Related]
15. Label-free, high-throughput holographic imaging to evaluate mammalian gametes and embryos†.
Wheeler MB; Rabel RAC; Rubessa M; Popescu G
Biol Reprod; 2024 Jun; 110(6):1125-1134. PubMed ID: 38733568
[TBL] [Abstract][Full Text] [Related]
16. Predictive Modeling of Implantation Outcome in an In Vitro Fertilization Setting: An Application of Machine Learning Methods.
Uyar A; Bener A; Ciray HN
Med Decis Making; 2015 Aug; 35(6):714-25. PubMed ID: 24842951
[TBL] [Abstract][Full Text] [Related]
17. Sperm Inspection for In Vitro Fertilization via Self-Assembled Microdroplet Formation and Quantitative Phase Microscopy.
Atzitz Y; Dudaie M; Barnea I; Shaked NT
Cells; 2021 Nov; 10(12):. PubMed ID: 34943823
[TBL] [Abstract][Full Text] [Related]
18. Motile sperm organelle morphology examination (MSOME) can predict outcomes of conventional in vitro fertilization: A prospective pilot diagnostic study.
Gao Y; Zhang X; Xiong S; Han W; Liu J; Huang G
Hum Fertil (Camb); 2015; 18(4):258-64. PubMed ID: 26646391
[TBL] [Abstract][Full Text] [Related]
19. Reactive oxygen species, total antioxidant concentration of seminal plasma and their effect on sperm parameters and outcome of IVF/ICSI patients.
Hammadeh ME; Al Hasani S; Rosenbaum P; Schmidt W; Fischer Hammadeh C
Arch Gynecol Obstet; 2008 Jun; 277(6):515-26. PubMed ID: 18026972
[TBL] [Abstract][Full Text] [Related]
20. Gold-standard for computer-assisted morphological sperm analysis.
Chang V; Garcia A; Hitschfeld N; Härtel S
Comput Biol Med; 2017 Apr; 83():143-150. PubMed ID: 28279863
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
