Segmentation and classification tool for trans-rectal B-mode ultrasound images.

Submitted as part of the ASMUS2021 Conference for the paper 'Development and evaluation of intraoperative ultrasound segmentation with negative image frames and multiple observer labels'.

This package contains a PyTorch-based implementation of a U-Net based segmentation model, and a DenseNet-based classification model, for the detection and segmentation of prostate in rectal b-mode ultrasound images.

To install from command line, use the following git command:

git clone https://github.com/liamchalcroft/RectAngle/

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 yourself 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.

(The code relevant to different label sampling methods is in sub-branch: label_method.)