Skip to content

Latest commit

 

History

History

scripts

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
BBBC027
BBBC027
 
 
Cellpose
Cellpose
 
 
DeLTA
DeLTA
 
 
Mask R-CNN
Mask R-CNN
 
 
MiSiC
MiSiC
 
 
Morphometrics
Morphometrics
 
 
PlantSeg
PlantSeg
 
 
StarDist
StarDist
 
 
SuperSegger
SuperSegger
 
 
.DS_Store
.DS_Store
 
 
3D_omnipose.ipynb
3D_omnipose.ipynb
 
 
Diameter_metric.ipynb
Diameter_metric.ipynb
 
 
Figure_1.ipynb
Figure_1.ipynb
 
 
Figure_2A_4AB_CP_OM_Compare.ipynb
Figure_2A_4AB_CP_OM_Compare.ipynb
 
 
Figure_3_S5.ipynb
Figure_3_S5.ipynb
 
 
Figure_5_S6_Tre1.ipynb
Figure_5_S6_Tre1.ipynb
 
 
Figure_6_C_elegans_cyto2.ipynb
Figure_6_C_elegans_cyto2.ipynb
 
 
Figure_6_cyto2_example.ipynb
Figure_6_cyto2_example.ipynb
 
 
Figure_6_fluroescence.ipynb
Figure_6_fluroescence.ipynb
 
 
Figure_6_worm_high_res.ipynb
Figure_6_worm_high_res.ipynb
 
 
Figure_S2.ipynb
Figure_S2.ipynb
 
 
Figure_S3.ipynb
Figure_S3.ipynb
 
 
Figure_S5.ipynb
Figure_S5.ipynb
 
 
Figure_S7.ipynb
Figure_S7.ipynb
 
 
Figure_SX_DeLTA.ipynb
Figure_SX_DeLTA.ipynb
 
 
Figures_3_4_S1.ipynb
Figures_3_4_S1.ipynb
 
 
Ground_truth_export_and_thumbnails.ipynb
Ground_truth_export_and_thumbnails.ipynb
 
 
LICENSE
LICENSE
 
 
README.md
README.md
 
 
export_models.ipynb
export_models.ipynb
 
 
ftsN_comparison.eps
ftsN_comparison.eps
 
 
ftsN_comparison.png
ftsN_comparison.png
 
 
image_cell_counting.ipynb
image_cell_counting.ipynb
 
 
img02.png
img02.png
 
 
scale_bars.ipynb
scale_bars.ipynb
 
 

README.md

Omnipose

All scripts required to reproduce our data and figures in Omnipose: a high-precision, morphology-independent solution for bacterial cell segmentation under /omnipose/figures/. Many algorithms required either a separate conda environment or a standalone application, so I opted to store our segmentation results for each algorithm in an appropriate directory to be read into the notebooks. These directories should be quite clear from thse scripts, but the top levels will need to be replaced according to your own folder structure. Namely, /home/kcutler/DataDrive on our Linux machine might be replaced by /Volumes/<wherever> on a macOS machine. Linux or Windows is recommended for Nvidia GPU support whenever evaluating DNN algorithms. Scripts, screenshots, and README files pertaining to the particulars of each tested algorithm are available in their respective subfolders.