Segmentation and classification tool for trans-rectal B-mode ultrasound images.
Submitted as coursework for MPHY0041: Machine Learning in Medical Imaging.
This package contains PyTorch-based implementations of a U-Net based segmentation model, and a DenseNet-based classification model, for the simultaneous detection and segmentation of prostate in rectal b-mode ultrasound images.
To install from command line, use the following git command:
A conda environment file is provided in the ./conda/ folder, which may be used as such:
cd RectAngle
conda env create --file ./conda/rectangle.yml
conda activate rectangle
Once this is activated, the package may be installed using the setup.py file:
pip install .
Following this, training/inference may be performed using objects in the train module.
To familiarise with the code used, an interactive notebook used for experiments in the associated report is available below. Please note that data used is proprietary and so has been withheld from the published repository.
