Contents

• Example notebooks
• Segmenting the C. elegans embryo
• Generating synthetic images of embryos with a GAN
• Predicting populations of cells within the C. elegans embryo
• Contributing to DevoLearn
• Links to datasets
• Links to Huggingface spaces
• Contact us

Installation

pip install devolearn

Example notebooks

• Extracting centroid maps and making 3d centroid models

Segmenting the Cell Membrane in C. elegans embryo

• Importing the model

  
  from devolearn import cell_membrane_segmentor
  segmentor = cell_membrane_segmentor()

* Running the model on an image and viewing the prediction
```python
seg_pred = segmentor.predict(image_path = "sample_data/images/seg_sample.jpg")
plt.imshow(seg_pred)
plt.show()

• Running the model on a video and saving the predictions into a folder

  filenames = segmentor.predict_from_video(video_path = "sample_data/videos/seg_sample.mov", centroid_mode = False, save_folder = "preds")

• Finding the centroids of the segmented features

  seg_pred, centroids = segmentor.predict(image_path = "sample_data/images/seg_sample.jpg", centroid_mode = True)
  plt.imshow(seg_pred)
  plt.show()

• Saving the centroids from each frame into a CSV

df = segmentor.predict_from_video(video_path = "sample_data/videos/seg_sample.mov", centroid_mode = True, save_folder = "preds")
df.to_csv("centroids.csv")

Segmenting the Cell Nucleus in C. elegans embryo

• Importing the model

  
  from devolearn import cell_nucleus_segmentor
  segmentor = cell_nucleus_segmentor()

* Running the model on an image and viewing the prediction
```python
seg_pred = segmentor.predict(image_path = "sample_data/images/nucleus_seg_sample.jpg")
plt.imshow(seg_pred)
plt.show()

Generating synthetic images of embryos with a Pre-trained GAN

• Importing the model

  
  from devolearn import Generator, embryo_generator_model
  generator = embryo_generator_model()

* Generating a picture and viewing it with [matplotlib](https://matplotlib.org/)
```python
gen_image = generator.generate()  
plt.imshow(gen_image)
plt.show()

• Generating n images and saving them into foldername with a custom size

generator.generate_n_images(n = 5, foldername= "generated_images", image_size= (700,500))

Predicting populations of cells within the C. elegans embryo

• Importing the population model for inferences

  from devolearn import lineage_population_model

• Loading a model instance to be used to estimate lineage populations of embryos from videos/photos.

  model = lineage_population_model(device = "cpu")

• Making a prediction from an image

  print(model.predict(image_path = "sample_data/images/embryo_sample.png"))

• Making predictions from a video and saving the predictions into a CSV file

  results = model.predict_from_video(video_path = "sample_data/videos/embryo_timelapse.mov", save_csv = True, csv_name = "video_preds.csv", ignore_first_n_frames= 10, ignore_last_n_frames= 10, postprocess = False)

• Plotting the model's predictions from a video

  plot = model.create_population_plot_from_video(video_path = "sample_data/videos/embryo_timelapse.mov", save_plot= True, plot_name= "plot.png", ignore_last_n_frames= 0, postprocess = False)
  plot.show()

Links to Datasets

Links to HuggingFace spaces

Authors/maintainers:

• Mayukh Deb
• Ujjwal Singh
• Dr. Bradly Alicea

Feel free to join our Slack workspace!