Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound

This repository contains all code to support the paper "Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound", published in IEEE Journal of Biomedical and Health Informatics. [arXiv][JBHI][Supplementary material]

        

 

        

Data and Network Parameters

As part of this work, we curated the "Boston Emergency Department Lung UltraSound (BEDLUS)" dataset. This dataset comprises 1,419 videos from 113 patients, along with expert annotations. All videos are labeled as positive or negative for the presence of B-lines. For the positive videos, a total of 15,755 B-lines are annotated on 10,371 frames.

The BEDLUS dataset and network parameter files for the trained models are available at Harvard Dataverse.

Project Setup

1. Install all required libraries (Python version 3.7):

pip install -r requirements.txt

2. Create the project folder structure after specifying the root folder in config.py:

python utils/config.py

3. After requesting access, download and unzip the dataset (data.zip) and network parameter files (networks_clip-level.zip, networks_frame-level.zip, networks_pixel-level.zip).
4. Copy all contents from data/LUS_videos to project_root/raw. The raw-folder should contain a subfolder for each patient (e.g. Case-001) with LUS videos as mp4-files inside.
5. Copy all pkl-files with dataset information supplementary_files/*.pkl (in this repository) to project_root/intermediate/data/info.
6. Copy all csv-files with annotation information supplementary_files/*.csv (in this repository) to project_root/intermediate/data/annotations.
7. Copy all contents (or only the network parameter files for the models of interest) from clip-level, frame-level, pixel-level to project_root/intermediate/models.

Data Preparation

Extract and preprocess the frames from the lung ultrasound videos:

python data_preparation/5_get_processed_frames.py

A new folder intermediate/data/images/processed_frames is created with inside all processed frames. All other python files in the data_preparation folder were used to extract the processing information (e.g. for cropping and rescaling), which is already available in the supplementary pkl-files.

Dataset Generation

From the preprocessed frames, datasets with labels or annotations can be created for training deep neural networks at the level of multi-frame clips, single frames, and individual pixels (i.e., segmentation). The dataset split is already provided in one of the supplementary files.

1. Specify the dataset type in 2_create_dataset.py:
   • frames = 1 for frame-level and pixel-level
   • frames = 16 for clip-level
2. Create the dataset:

python dataset_generation/2_create_dataset.py

3. Specify the labelset type in 3_create_dataset.py:
   • task = 'segmentation' with folder = 'frames_1' for the pixel-level
   • task = 'classification' with folder = 'frames_1' for the frame-level
   • task = 'classification' with folder = 'frames_16' for the clip-level
4. Create the labelset:

python dataset_generation/3_create_labelset.py

Network Training & Evaluation

Network training details can be configured for a single training run in single_training_run.py, which is inside the network_training folder. Alternatively, the training details can be specified in a csv-file (e.g., see network_training/example_training_schedule.csv) when running multiple consecutive experiments using scheduled_training_runs.py in the network_training folder.

The evaluation folder contains all code that was used for the evaluation, separated in subfolders for each level.

Citing

If you found our work useful in your research, please consider citing our paper:

@article{lucassen2023deep,
  title={Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound},
  author={Lucassen, Ruben T and Jafari, Mohammad H and Duggan, Nicole M and Jowkar, Nick and Mehrtash, Alireza and Fischetti, Chanel and Bernier, Denie and Prentice, Kira and Duhaime, Erik P and Jin, Mike and others},
  journal={IEEE Journal of Biomedical and Health Informatics},
  year={2023},
  publisher={IEEE}
}