Deep Learning-Based Semantic Segmentation of Microscale Objects

This is the source code for the deep learning model proposed in the paper Deep Learning-Based Semantic Segmentation of Microscale Objects. A condensed version of the paper is published in the Proceedings of the 2019 International Conference on Manipulation, Automation, and Robotics at Small Scales.

Data Annotation

Low contrast, bright-field images of multiple human endothelial cells and silica beads shown in the paper are considered. Segmentation labels for the images are created using LabelMe.

Implementation

This code is tested on a workstation running Windows 10 operating system, equipped with a 3.7GHz 8 Core Intel Xeon W-2145 CPU, GPU ZOTAC GeForce GTX 1080 Ti, and 64 GB RAM.

Implemented with:

• Tensorflow-gpu 1.10.1
• Keras 2.2.4
• OpenCV 3.4.2
• Python 3.5.5

Code

1. Create a 'data' folder in the cell-segmentation-master directory. Create sub-folders 'src' and 'label' in the data folder and place all the dataset images and the corresponding labels in the src and label folders respectively.
2. Run gen_img_aug.py to split the data into training and test sets.
3. Run pre_processing.py to perform pre-processing on the training set.
4. Run train_unet_xception_resnetblock.py to train the model. The model is saved when the validation loss reaches its minimum and is named as unet_xception_resnet_nsgd32_lovaszsoftmax_best.h5.
5. Run predict_lovasz_loss.py to generate segmentation masks for the test images using the saved model.
6. Run get_contour.py to detect the contours of the segmented regions and overlay them on the original image.

Acknowledgement

• The code for the model is built on the code by Siddharta proposed in the TGS Salt Identification Challenge hosted by Kaggle.
• Implementation of the Lovasz Softmax loss is as provided by Maxim Berman.

Citation

Please cite the following paper if the code is useful.

@article{samani2019deep,
  title={Deep Learning-Based Semantic Segmentation of Microscale Objects},
  author={Samani, Ekta U and Guo, Wei and Banerjee, Ashis G},
  journal={arXiv preprint arXiv:1907.03576},
  year={2019}
}