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

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

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

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))

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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

Model	Data source
Segmenting the cell membrane in C. elegans embryo	3DMMS: robust 3D Membrane Morphological Segmentation of C. elegans embryo
Segmenting the nucleus in C. elegans embryo	C. elegans Cell-Tracking-Challenge dataset
Cell lineage population prediction + embryo GAN	EPIC dataset

Links to HuggingFace spaces

Model	Huggingface
Segmenting the cell membrane in C. elegans embryo	Cell Membrane segmentor
Segmenting the nucleus in C. elegans embryo	C. elegans Nucleus segmentor
Cell lineage population prediction	Lineage population

Authors/maintainers:

• Mayukh Deb
• Ujjwal Singh
• Dr. Bradly Alicea

Feel free to join our Slack workspace!