Package: cytounet
cytounet: Deep Learning Based Cell SegmentationBackgroundThis project was largely done as a summer 2020 intern in Dr. MikaelBjorklund'slab, whose guidance, resources, and time I am grateful for.The aim was to automatea549andrpecancer cell segmentation and size determination.Sample data(10 random images each of train, validate, test sets) is provided inoriginal_data.A complete a549 cancer cellsegmentation notebookis also provided.Finally, pre-trainedweightsare provided that can be used for transfer learning.
These were trained on considerably more data and for more epochs. For more pre-trained weights and/or data,
pleasecontactthe author.NoteTo generate masks(labels) provided herea549cancer cells were stained and imaged with fluorescence microscopy. These
fluorescent images were then thresholded withthreshold_imageswith a threshold value of 83. The images were then saved
withsave_images. The original fluorescent images are not provided here mainly due to the already huge size of the
project.This project is not limited to cancer cells. The model can be trained on almost any object, living and non-living.
More examples are given below.InstallationFromPyPI:pip install cytounetFrom source or to use without installing locally:git clone https://github.com/Nelson-Gon/cytounet.git
# proceed with usual source build procedureOr:pip install git+https://github.com/Nelson-Gon/cytounet.gitImportfrom cytounet.model import *
from cytounet.data import *
from cytounet.augmentation import *
from cytounet.post_model import *Detailed Sample UsageScript modepython-mcytounet-t"examples/original_data/a549/train"-i"images"-m"masks"-v"examples/original_data/a549/validation"-l"1e-8"-s512-ep5-se250-b8-tt"examples/original_data/a549/test/"-w"models/a549_test/test_model"-o"Adam"-mt"dice_coef"-ls"dice_coef_loss"-sd2-f0-p0To get help:python-mcytounet-hNotebooksPlease see the following examples:Typical UsagePredicting Embryonic DIC Image Labels with KerasVisually:Sample Object Area Visualization(see the typical usage notebook above for detailed usage)For more examples or to add your own, please see the examplesfolder.Experiments/BenchmarksThis section shows some experimental results based on publicly available data.Comparison of low vs high quality masks on the model's outputThis notebook shows the effects of "filled holes"(outlines whose area is filled with some colour e.g. white)
on the model's quality. The results in general show that filled masks which are also better seen by the human eye
result in better quality output.Thenotebookcan be accessed via thislink.TODO ListA checked box indicates support. You can either add more feature requests here or tackle unchecked boxes and make
a pull request to add such support.Single class segmentationGrayscale imagesModel ValidationDetermining areas of objects in an imageCustom Number of LayersMulti-class segmentationColored image inputCOCO DatasetsCSV Based AnnotationsXML Based AnnotationsFrequently Asked QuestionsPlease read our WikiPagesTo raise an issue or questionIf thewikidoes not answer your question,
please raise a newissue.
You can also open an issue if you have any discussion, criticism, or bug reports.Thank you very much.ReferencesMouse EmbryosDatasetfromBroad Bioimage Benchmark.Red Blood CellImagesprovided by Anne
Carpenter and Roger Wiegand, availablehere.Chinese Hamster OvaryCellsprovided by
Koos et al.(2016)CreditsThis repository started out as a clone ofzhixuhao's  original
unetimplementation.This repository has considerably diverged from the original implementation hence the need
to distribute it separately.This decision was taken in part due to the relative inactivity of the original implementation which would have made
it harder to collaborate.
Please take a look at the list ofchangesfrom the original implementation.The Unet algorithm was introduced by Ronneberger et al. in theirpaper.If you would like to cite this work, please use:Nelson Gonzabato(2020) cytounet: Deep Learning Based Cell Segmentation,https://github.com/Nelson-Gon/cytounetBibTex@misc{Gonzabato2021,
  author = {Gonzabato, N},
  title = {cytounet: Deep Learning Based Cell Segmentation},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Nelson-Gon/cytounet}},
  commit = {58bd951ef4417fc8542f8f3e277071e6cd6980ea}
}