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kidney-segmentation-code

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Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks

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Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks-TensorFlow
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This is an implementation of the proposed model in TensorFlow for kidney ultrasound image segmentation.

Model Description：
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We first use deep neural networks pre-trained for classification of natural images to extract high-level image features from US images. 
These features are used as input to learn kidney boundary distance maps using a boundary distance regression network and the predicted boundary distance maps are classified as kidney pixels or non-kidney pixels using a pixelwise classification network in an end-to-end learning fashion. We also adopted a data augmentation method based on kidney shape registration to generate enriched training data from a small number of US images with manually segmented kidney labels.

We refer to the boundary distance regression network followed by post-processing for segmenting kidneys as a boundary detection network (Bnet).

For more details on the underlying model please refer to the following paper:
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@article{yin2020automatic, 
title={Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks}, 
author={Yin, Shi and Peng, Qinmu and Li, Hongming and Zhang, Zhengqiang and You, Xinge and Fischer, Katherine and Furth, Susan L and Tasian, Gregory E and Fan, Yong}, 
journal={Medical Image Analysis}, 
volume={60}, 
pages={101602}, 
year={2020}}

Requirements：
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The proposed networks were implemented based on Python 3.7.0 and TensorFlow r1.11

Training：
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We initialized the network from the VGG_16.npy 
Training the end-to-end learning of subsequent segmentation networks: train_end_to_end.py 
Training the Bnet: train_Bnet.py

Evaluation:
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Evaluating the end-to-end learning of subsequent segmentation networks: evaluate_end_to_end.py 
Evaluating the Bnet: evaluate_Bnet.py

Data augmentation:
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data augmentaion/main_preprocessing.m 
The dataaugmentation code was implemented based on Matlab R2015b

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