DLCA

left: input (3D volume)

right: output (3D volume. Blue boxes：predictions, red box：GT label）

Introduction
Prerequisites
Installation
Usage
Results

Introduction

This repository contains the source code and the trained model for our work Deep Learning for Detecting Cerebral Aneurysms on CT Angiography.

Prerequisites

Ubuntu
Python 3
Pytorch 0.4.1
NVIDIA GPU + CUDA CuDNN

This repository has been tested on NVIDIA TITAN Xp. Configurations (e.g batch size, image patch size) may need to be changed on different platforms.

Installation

Clone this repo:

git clone https://github.com/CTA-detection/DLCA.git
cd DLCA

Install dependencies:

pip install -r requirements.txt

Usage

1. Preprocess

Run command as below.

python ./utils/pre_process.py --input="./raw_data/" --output="./train_data/"

2. Train

Run command as below.

python train.py -j=16 -b=12 --input="train_data/" --output="./checkpoint/"

3. Inference

Click the checkpoint link(GoogleDrive) to download trained model into "./checkpoint".
Click the data link(GoogleDrive) to download test image "brain_CTA.nii.gz"
Run command as below.

# an example with the image named "brain_CTA.nii.gz"
python inference.py -j=1 -b=1 --resume="./checkpoint/trained_model.ckpt" --input="./test_image/brain_CTA" --output="./prediction/brain_CTA"

Results

Sensitivity	False Positive per case
97.5%	13.8

daisy0x00/DLCA