Awesome

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:

• Thanks to Paul, who annotated the bounding box here: Bounding boxes for Type_1. I have made some minor adjustments and added the missing images.

• The amazing Keras UNET implementation by jocicmarko in: ultrasound-nerve-segmentation

• The original U-Net design: U-Net: Convolutional Networks for Biomedical Image Segmentation.

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:

Training loss: