Learning to Map 2D Ultrasound Images into 3D Space with Minimal Human Annotation

This repository contains the codes (in PyTorch) for the framework introduced in the following paper:

Learning to Map 2D Ultrasound Images into 3D Space with Minimal Human Annotation [Paper] [Project Page]

@article{yeung2021learning,
    title = {Learning to map 2D ultrasound images into 3D space with minimal human annotation},
    author = {Yeung, Pak-Hei and Aliasi, Moska and Papageorghiou, Aris T and Haak, Monique and Xie, Weidi and Namburete, Ana IL},
    journal = {Medical Image Analysis},
    volume = {70},
    pages = {101998},
    year = {2021},
    publisher = {Elsevier}
}

Contents

1. Dependencies
2. Train
3. Inference on Video Data

Dependencies

• Python (3.6), other versions should also work
• PyTorch (1.6), other versions should also work
• scipy
• cv2
• skimage
• imgaug

Train

1. Modify the train_github.py for importing your dataset according to the instructions in the file.
2. Use the following command to train:

   python train_github.py

   Inference on Video Data

   Post (predict after the whole video is acquired)

3. Modify the inference_github.py according to the instructions in the file.
4. Use the following command to train:

   python inference_github.py

   Real-time

   To do, but it would be an easy modification based on the inference_github.py.