COVID-19 opacity segmentation in chest CT via HydraNet: a joint learning multi-decoder network

Sagie, Nimrod; Almog, Shiri; Talby, Ayelet; Greenspan, Hayit

doi:10.1117/12.2581111

Cited by 3 publications

(1 citation statement)

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“…Given the relevance and importance of the problem, many recent studies aim to address lung CT segmentation by estimating the pathological regions. Most of the studies present binary segmentation methods for dividing lung scans into healthy and non-healthy tissues [5][6][7][8][9][10] while only a few present multi-class segmentation techniques for separating between GGO and CON regions [6].…”

Section: Corona Virus Disease 2019 (Covid-19mentioning

confidence: 99%

A Deep Ensemble Learning Approach to Lung CT Segmentation for Covid-19 Severity Assessment

Ben-Haim

Sofer

Ben-Arie

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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We present a novel deep learning approach to categorical segmentation of lung CTs of COVID-19 patients. Specifically, we partition the scans into healthy lung tissues, non-lung regions, and two different, yet visually similar, pathological lung tissues, namely, ground-glass opacity and consolidation. This is accomplished via a unique, end-to-end hierarchical network architecture and ensemble learning, which contribute to the segmentation and provide a measure for segmentation uncertainty.The proposed framework achieves competitive results and outstanding generalization capabilities for three COVID-19 datasets. Our method is ranked second in a public Kaggle competition for COVID-19 CT images segmentation. Moreover, segmentation uncertainty regions are shown to correspond to the disagreements between the manual annotations of two different radiologists. Finally, preliminary promising correspondence results are shown for our private dataset when comparing the patients' COVID-19 severity scores (based on clinical measures), and the segmented lung pathologies. Code and data are available at our repository 1 .

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