Cervix ROI Segmentation Using U-NET

Related tags

Deep Learningcervix-roi-segmentation-by-unet
Overview

Cervix ROI Segmentation Using U-NET

Overview

This code illustrate how to segment the ROI in cervical images using U-NET.

The ROI here meant to include the: Os + transformation zone + nearby tissue.

The localized ROI is supposed to improve the classification of cervical types, which is the challenge in the Kaggle competition:Intel and MobileODT Cervical Cancer Screening

Compare to other UNET examples, in this one we got:

  • the input images in RGB
  • the input images and masks are augmented in pairs using izip ImageDataGenerators
  • support both Tensorflow and Theano backend, and is using Keras 2

Dependencies:

  • Keras 2
  • Tensorflow or Theano
  • cv2

Other references:

Usage

Data preparation:

  • Download the data from Kaggle.
  • Unzip trian.7z and test.7z into input folder. You may unzip additional_Type_*_v2.7z as well, if you want to segment them, its optional.
  • The input folders should look like this:
    • input/test/
    • input/train/Type_1
    • input/train/Type_2
    • input/train/Type_3
    • input/additional/Type_1 (optional)
    • input/additional/Type_2 (optional)
    • input/additional/Type_3 (optional)
  • Run prepare_data.py
  • Run split_data.py
  • Note:
    • The bbox annotations were converted to Sloth json format and is included under input/*.json.
    • The additional data is NOT used in this training.

Training:

  • Run train.py
  • The best epoch weight file will be save under src/unet_xxxxxx/weights.h5. Note when train.py starts, it will look for previous weight file (if any) and resume from there if weight file exits

Segmentation:

  • Run predict.py
  • The output segmentations are under:
    • input/test_roi/
    • input/train_roi/
    • input/additional_roi/

Configurations:

  • Customize configurations.py

Results

On a GTX 1070, the training of 400 epochs took ~2 hours to complete. The best DICE coefficient is ~0.78.

Apply this model to the 512 unseen test images, the result looks satisfactory in 96% of images.

Sample outputs: img/preview.jpg

Training loss: img/loss_history.jpg

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Comments
  • Where is the manual labels?

    Where is the manual labels?

    Thanks for your sharing. After downloading the training and testing dataset, I notice that I cannot find the manual labels. Where are they? Don't we need them?

    Thanks,

    opened by lowkeygit 1
  • Doubt in predict.py

    Doubt in predict.py

    Hi Scotty - Tks for the amazing code, helped me a lot , had a doubt in predict.py . Is it possible to explain what was done in predict.py at a high level, any references to any literature on these methods would be great Thanks -

    opened by ronroc 1
Releases(v1.0)
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Scotty Kwok
Scotty Kwok
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