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Automatic Fetal Brain MRI Quality Assessment

Crop and then score NIFTI files (.nii, .nii.gz) on a scale from 0 to 1 based on quality using machine learning.

Fetal brain MRI quality is negatively affected by image noise and motion artifacts.

Abstract

The aim of this project was to develop a Quality Assessment tool for fetal brain MRIs, which is able to score each volume through a deep learning regression model. Developed using Python3 and Keras/Tensorflow framework.

Our network architecture consists of a non-linear configuration, known as Residual Network (ResNet) architecture: Resnet Architecture Diagram

Given that we are dealing with an unbalanced distribution regarding input dataset, we applied different weights to each input class to compensate for the imbalance in the training sample.

https://youtu.be/H2VNJN-7xZ8?t=494

This repository contains the tool to be used for predications and downstream research. For model training and validation, see https://github.com/ilegorreta/Automatic-Fetal-Brain-Quality-Assessment-Tool

System Requirements

Docker >18.09
NVIDIA Container Toolkit
drivers supporting CUDA version 11.1 (check with nvidia-container-cli info)

Development

DOCKER_BUILDKIT=1 docker build -t fnndsc/pl-fetal-brain-assessment .

<details> <summary>What's BuildKit?</summary> Our <code>Dockerfile</code> leverages advanced features of Docker. <ul> <li>https://github.com/moby/moby/issues/15717#issuecomment-493854811</li> <li>https://docs.docker.com/engine/reference/builder/#buildkit</li> </ul> </details>

Usage

fetal-brain-assessment is a ChRIS plugin.

Below is what the plugin options look like in ChRIS_ui.

Screenshot of ChRIS_ui

Using Docker Run

Run analysis directly on your machine in the command line.

$ ls input
scan-AX-1-3mm_crop.nii
scan-AX-2-3mm_crop.nii
scan-COR-1-3mm_crop.nii
scan-COR-2-3mm_crop.nii
scan-SAG-1-3mm_crop.nii
scan-SAG-2-5mm_crop.nii
scan-SAG-3-3mm_crop.nii

$ mkdir output

$ docker run --rm --gpus all -u $(id -u):$(id -g)                \
    -v /etc/localtime:/etc/localtime:ro                          \
    -v $PWD/input:/incoming:ro -v $PWD/output:/outgoing:rw       \
    fnndsc/pl-fetal-brain-assessment:1.2.0                       \
    fetal_brain_assessment --verbosity 3                         \
    --inputPathFilter '*_crop.nii' --output-file predictions.csv \
    --threshold 0.4 --destination Best_images_drop               \
    /incoming /outgoing

$ cat output/predictions.csv
scan-COR-1-3mm_crop.nii,0.6486295
scan-SAG-1-3mm_crop.nii,0.31944746
scan-AX-1-3mm_crop.nii,0.25669065
scan-SAG-2-5mm_crop.nii,0.26366436
scan-SAG-3-3mm_crop.nii,0.32725734
scan-AX-2-3mm_crop.nii,0.45316696
scan-COR-2-3mm_crop.nii,0.32879326

$ ls output/Best_images_drop
scan-COR-1-3mm_crop.nii
scan-AX-2-3mm_crop.nii

Using Singularity

singularity exec --nv docker://fnndsc/pl-fetal-brain-assessment:1.2.0 fetal_brain_assessment in out