• HuBMAP - Hacking the Human Vasculature (BackEnd implementation using Flask)
  • Description
  • APIs
    • /predict/unet
    • /predict/unet_scratch
    • /predict/linknet
    • /predict/fcn
    • /predict/ensemble
  • Installation
    • Additional Setup
  • Sample Data
  • UI

The goal is to segment instances of microvascular structures, including capillaries, arterioles, and venules, to automate the segmentation of microvasculature structures to improve researchers' understanding of how the blood vessels are arranged in human tissues.

• Description: API for predicting using the UNet model.
• Input:
  • image: Kidney tissue image of shape 512x512x3.
  • mask: Corresponding mask of shape 512x512x1. Note: The mask's pixel values must be 0 or 1 for proper functioning.
• Output:
  • Paths:
    • image: Path to the input image.
    • overlaid_image_true: Path to the image overlaid with the true mask.
    • overlaid_image_pred: Path to the image overlaid with the predicted mask.
    • true_mask: Path to the true mask.
    • predicted_mask: Path to the predicted mask.
    • overlaid_mask: Path to the overlaid mask.
  • Score Metrics:
    • IOU: Intersection over Union score indicating the model's confidence.
    • Dice: The dice coefficient score indicates the model's confidence.
  • Threshold:
    • threshold: The threshold at which the predicted_mask was binarized.

• Description: API for predicting using defined UNet model layers.
• Input:
  • image: Kidney tissue image of shape 512x512x3.
  • mask: Corresponding mask of shape 512x512x1. Note: The mask's pixel values must be 0 or 1 for proper functioning.
• Output:
  • Paths:
    • image: Path to the input image.
    • overlaid_image_true: Path to the image overlaid with the true mask.
    • overlaid_image_pred: Path to the image overlaid with the predicted mask.
    • true_mask: Path to the true mask.
    • predicted_mask: Path to the predicted mask.
    • overlaid_mask: Path to the overlaid mask.
  • Score Metrics:
    • IOU: Intersection over Union score indicating the model's confidence.
    • Dice: The dice coefficient score indicates the model's confidence.
  • Threshold:
    • threshold: The threshold at which the predicted_mask was binarized.

• Description: API for predicting using LinkNet model.
• Input:
  • image: Kidney tissue image of shape 512x512x3.
  • mask: Corresponding mask of shape 512x512x1. Note: The mask's pixel values must be 0 or 1 for proper functioning.
• Output:
  • Paths:
    • image: Path to the input image.
    • overlaid_image_true: Path to the image overlaid with the true mask.
    • overlaid_image_pred: Path to the image overlaid with the predicted mask.
    • true_mask: Path to the true mask.
    • predicted_mask: Path to the predicted mask.
    • overlaid_mask: Path to the overlaid mask.
  • Score Metrics:
    • IOU: Intersection over Union score indicating the model's confidence.
    • Dice: The dice coefficient score indicates the model's confidence.
  • Threshold:
    • threshold: The threshold at which the predicted_mask was binarized.

• Description: API for predicting using FCN model.
• Input:
  • image: Kidney tissue image of shape 512x512x3.
  • mask: Corresponding mask of shape 512x512x1. Note: The mask's pixel values must be 0 or 1 for proper functioning.
• Output:
  • Paths:
    • image: Path to the input image.
    • overlaid_image_true: Path to the image overlaid with the true mask.
    • overlaid_image_pred: Path to the image overlaid with the predicted mask.
    • true_mask: Path to the true mask.
    • predicted_mask: Path to the predicted mask.
    • overlaid_mask: Path to the overlaid mask.
  • Score Metrics:
    • IOU: Intersection over Union score indicating the model's confidence.
    • Dice: The dice coefficient score indicates the model's confidence.
  • Threshold:
    • threshold: The threshold at which the predicted_mask was binarized.

• Description: API for predicting using an ensemble of models.
• Input:
  • image: Kidney tissue image of shape 512x512x3.
  • mask: Corresponding mask of shape 512x512x1. Note: The mask's pixel values must be 0 or 1 for proper functioning.
• Output:
  • Paths:
    • image: Path to the input image.
    • overlaid_image_true: Path to the image overlaid with the true mask.
    • overlaid_image_pred: Path to the image overlaid with the predicted mask.
    • true_mask: Path to the true mask.
    • predicted_mask: Path to the predicted mask.
    • overlaid_mask: Path to the overlaid mask.
  • Score Metrics:
    • IOU: Intersection over Union score indicating the model's confidence.
    • Dice: The dice coefficient score indicates the model's confidence.
  • Threshold:
    • threshold: The threshold at which the predicted_mask was binarized.

To install the project dependencies, run the following command:

pip install -r requirements.txt

This command will install all the necessary packages listed in the requirements.txt file.

To ensure the project runs smoothly, please follow these steps:

1. Weights Directory: Create a directory named weights within the model directory. This directory should contain the weights of the four models (UNet, UNet Scratch, LinkNet, FCN). These weights are essential for the proper functioning of the project. Adjust the model weights file name to match the one in the model_weights.json file.

   • UNet Model Weights: Download the UNet model weights from here.
   • UNet Scratch Model Weights: Download the LinkNet model weights from here.
   • LinkNet Model Weights: Download the LinkNet model weights from here.
   • FCN Model Weights: Download the FCN model weights from here.

2. Images Directory in Static: Create a directory named images within the static directory. This directory will contain the images generated by the server to display them later.

These directories must be created before running the project.

You can test the app with the sample data.

Here you can find the implemented User Interface.