Deep learning for classification and localization of COVID-19 markers in point-of-care lungultrasound
Figure 1. Overview of the different tasks considered in this work.
Deep learning for classification and localization of COVID-19 markers in point-of-care lungultrasound
Subhankar Roy, Willi Menapace, Sebastiaan Oei, Ben Luijten, Enrico Fini, Cristiano Saltori, Iris Huijben, Nishith Chennakeshava, Federico Mento, Alessandro Sentelli, Emanuele Peschiera, Riccardo Trevisan, Giovanni Maschietto, Elena Torri, Riccardo Inchingolo, Andrea Smargiassi, Gino Soldati, Paolo Rota, Andrea Passerini, Ruud J.G. van Sloun, Elisa Ricci, Libertario Demi
In IEEE Transactions on Medical Imaging.
Paper: IEEE link
Abstract: Deep learning (DL) has proved successful inmedical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysisof lung ultrasonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, video-level, and pixel-level (segmentation masks). Leveraging these data, we introduce several deep models that address relevant tasks for the automatic analysis of LUS images. In particular, we present a novel deep network, derived from Spatial Transformer Networks, which simultaneously predicts the disease severity score associated to a input frame and provides localization of pathological artefacts in a weakly-supervised way. Furthermore, we introduce a new method based on uninorms for effective frame score aggregation at a video-level. Finally, we benchmark state of the art deep models for estimating pixel-level segmentations of COVID-19 imaging biomarkers. Experiments on the proposed dataset demonstrate satisfactory results on all the considered tasks, paving the way to future research on DL for the assisted diagnosis of COVID-19 from LUS data.
Our method foresees two main components:
- A frame-based predictor exploiting a novel deep architecture based on STN [1] that provides the disease severity score and a weakly-supervised localization of pathological artefacts.
- A video-based score predictor based on uninorms that performs aggregation of the frame-based scores.
Figure 2. Illustration of the proposed Reg-STN architecture for frame-based score prediction.
Table 1. F1 scores (%) for the frame-based classification under different evaluation settings explained in the paper.
Best and second best F1 scores (%) are in bold and underlines, respectively.
Figure 3. Examples of weakly-supervised localization results of pathological artefacts produced by the Reg-STN network.
Video 1. Examples of frame-based predictions produced by our proposed Reg-STN model.
Table 2. Mean and standard deviation of weighted F1 score, precision and recall for the proposed video-based classification method and baselines.
Results coming soon
The project requires Python 3.7+. To install the dependencies, run:
python -m pip install -r requirements.txt
The ICLUS dataset is available here. For dataset related queries, please drop an email to [email protected], in case the dataset that was used for the experiments are not available on the iclus website.
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To use the exact train-test split used in our paper, please download the
framesfolder containing the extracted frames from this link and place the folder under the dataset/ folder. If unavailable, please drop an email to [email protected] -
The frame-based score predictor can be trained by running the following commands from the root folder:
- for fixed scaling and trainable translation model in the paper
python frame-score-predictor/train.py --fixed_scale
- for trainable scaling, rotation and translation model in the paper
python frame-score-predictor/train.py
The video-based score predictor can be trained by running the following command inside the video_score_predictor directory
python aggregator.py --use_sord --setting=kfolds --lr=0.01 --tnorm=product --zero_score_gap=0.5 --loss=ce --epoch=30 --earlystop=last --init_neutral=0. --lr_gamma=1 --off_diagonal=min --testfile '' --expname=<experiment_name> 'data/frame_predictions.pkl' 'data/video_annotations.xlsx' 'data/video_annotations_to_video_names.xlsx' <output_path>
To reproduce the experiments pertaining to the frame-based semantic segmentation please refer to the following GitHub repo. https://github.com/TUEindhoven-BMd-AI/localisation_of_cov_19_markers
Please cite our paper if you find the work useful:
@article{DL4covidUltrasound,
author={S. Roy and W. Menapace and S. Oei and B. Luijten and E. Fini and C. Saltori and I. Huijben and N. Chennakeshava and F. Mento and A. Sentelli and E. Peschiera and R. Trevisan and G. Maschietto and E. Torri and R. Inchingolo and A. Smargiassi and G. Soldati and P. Rota and A. Passerini and R. J. G. Van Sloun and E. Ricci and L. Demi},
journal={IEEE Transactions on Medical Imaging},
title={Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound},
year={2020}
}
We thank the Caritro Deep Learning Lab of ProM Facility who made available their GPUs for the current work. We also thank Fondazione VRT for financial support [COVID-19 CALL 2020 Grant #1]
This work is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License