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High quality model for lungs segmentation.

Project description

lungs_segmentation

Automated lung segmentation in chest-x ray

https://habrastorage.org/webt/vk/jv/8r/vkjv8rjd04f1oicbczq5hyadhv0.png

Train pipeline: https://github.com/alimbekovKZ/lungs_segmentation_train

Installation

pip install lungs-segmentation

Example inference

Jupyter notebook with the example: Open In Colab

WebApp

https://lungssegmentation.herokuapp.com/

Models weights

model best dice Mb
resnet34 0.9657 103.4
densenet121 0.9655 62.8

Usage

Code example for resnet34:

from lungs_segmentation.pre_trained_models import create_model
import lungs_segmentation.inference as inference

model = create_model("resnet34")
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)

plt.figure(figsize=(20,40))
plt.subplot(1,1,1)
image, mask = inference.inference(model,'739px-Chest_Xray_PA_3-8-2010.png', 0.2)
plt.imshow(inference.img_with_masks( image, [mask[0], mask[1]], alpha = 0.1))

Results on data from the Internet

resnet34

https://habrastorage.org/webt/e3/mb/kc/e3mbkcxsmos6q4jlw5-tybudzji.png

densenet121

https://habrastorage.org/webt/ef/01/zo/ef01zo2g2qgsux8ses4keg4g8is.png

Authors

Renat Alimbekov, Ivan Vassilenko, Abylaikhan Turlassov

Project details


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