LoFiHippSeg: Bilateral Hippocampi Segmentation in Low Field MRIs Using Mutual Feature Learning via Dual-Views

First runner-up entry for segmentation Task in Low field pediatric brain magnetic resonance Image Segmentation and quality Assurance - LISA Challenge 2024. Link to Challenge

Abstract

Accurate hippocampus segmentation in brain MRI is critical for studying cognitive and memory functions and diagnosing neurodevelopmental disorders. While high-field MRIs provide detailed imaging, low-field MRIs are more accessible and cost-effective, which eliminates the need for sedation in children, though they often suffer from lower image quality. In this paper, we present a novel deep-learning approach for the automatic segmentation of bilateral hippocampi in low-field MRIs. Extending recent advancements in infant brain segmentation to underserved communities through the use of low-field MRIs ensures broader access to essential diagnostic tools, thereby supporting better healthcare outcomes for all children. Inspired by our previous work, Co-BioNet, the proposed model employs a dual-view structure to enable mutual feature learning via high-frequency masking, enhancing segmentation accuracy by leveraging complementary information from different perspectives. Extensive experiments demonstrate that our method provides reliable segmentation outcomes for hippocampal analysis in low-resource settings.

Link to full paper:

Paper Link Pre-print : Link

System requirements

Under this section, we provide details on the environmental setup and dependencies required to train/test the LoFiHippSeg model. This software was originally designed and run on a system running Ubuntu.
All the experiments are conducted on Ubuntu 20.04 Focal version with Python 3.8.
To train LoFiHippSeg with the given settings, the system requires 1 GPU with at least 80GB. All the experiments are conducted on Nvidia A100 GPUs. (Not required any non-standard hardware)
To test the model's performance on unseen test data, the system requires a GPU with at least 24 GB.

Create a virtual environment

• Download anaconda: wget https://repo.anaconda.com/archive/Anaconda3-2022.10-Linux-x86_64.sh

• Install anaconda: bash Anaconda3-2022.10-Linux-x86_64.sh

conda create -n your_env_name python=3.8
conda activate your_env_name

Installation guide

• Install torch :

conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia

• Install other dependencies :

pip install -r requirements.txt

Typical Install Time

This depends on the internet connection speed. It would take around 15-30 minutes to create environment and install all the dependencies required.

Dataset Preparation

The experiments are conducted on LISA 2024 Challenge dataset.

Pretrained Model

The pretrained models can be downloaded from here: https://drive.google.com/drive/folders/1PajSZf3T6xC8laeensI3gFcNX0OekqIH?usp=sharing

Train Model

CUDA_VISIBLE_DEVICES=0 python train.py &> experiment.out &

Test Model

cd code
CUDA_VISIBLE_DEVICES=0 python validation.py  &> experiment_evaluation.out &