Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound

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 SlounElisa Ricci, Libertario Demi (Corresponding author)

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

464 Citaten (Scopus)

Samenvatting

Deep learning (DL) has proved successful in medical 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 analysis of 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.
Originele taal-2Engels
Artikelnummer9093068
Pagina's (van-tot)2676-2687
Aantal pagina's12
TijdschriftIEEE Transactions on Medical Imaging
Volume39
Nummer van het tijdschrift8
DOI's
StatusGepubliceerd - aug. 2020

Bibliografische nota

Fondazione VRT
Caritro Deep Learning Lab

Vingerafdruk

Duik in de onderzoeksthema's van 'Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound'. Samen vormen ze een unieke vingerafdruk.

Citeer dit