B16BL6: A public time-lapse microscopy dataset and data-centric annotation pipeline for cell and division detection
Authors:
- Mokhaled N.A. Al-HamadaniMokhaled N.A. Al-Hamadani
- Péter SzilágyiPSPéter Szilágyi
- Gabor Szeman-NagyGSGabor Szeman-Nagy
- Andras HajduAHAndras Hajdu
- Balazs HarangiBHBalazs Harangi
Open Access
Abstract
No abstract availableFunding Information
- Debreceni Egyetem
This publication has 21 references indexed in Scilit:
- The multimodality cell segmentation challenge: toward universal solutionsNature Methods, 2024
- Visual detection on posture transformation characteristics of sows in late gestation based on Libra R-CNNBiosystems Engineering, 2022
- Deep learning applied in fish reproduction for counting larvae in images captured by smartphoneAquacultural Engineering, 2022
- LIVECell—A large-scale dataset for label-free live cell segmentationNature Methods, 2021
- Detection of cervical cells based on improved SSD networkMultimedia Tools and Applications, 2021
- A blood cell dataset for lymphoma classification using faster R-CNNBiotechnology & Biotechnological Equipment, 2020
- Nucleus segmentation across imaging experiments: the 2018 Data Science BowlNature Methods, 2019
- Deep learning for cellular image analysisNature Methods, 2019
- Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotationsScientific Data, 2018
- An objective comparison of cell-tracking algorithmsNature Methods, 2017