Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade oftwo U-nets: training and assessment on multipledatasets using different annotation criteria

no code implementations • 6 May 2021 • Francesca Lizzi, Abramo Agosti, Francesca Brero, Raffaella Fiamma Cabini, Maria Evelina Fantacci, Silvia Figini, Alessandro Lascialfari, Francesco Laruina, Piernicola Oliva, Stefano Piffer, Ian Postuma, Lisa Rinaldi, Cinzia Talamonti, Alessandra Retico

On an independent and publicly available benchmark dataset, the Dice values measured between the masks predicted by LungQuant system and the reference ones were 0. 95$\pm$0. 01 and 0. 66$\pm$0. 13 for the segmentation of lungs and COVID-19 lesions, respectively.

Segmentation

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge

no code implementations • 23 Dec 2016 • Arnaud Arindra Adiyoso Setio, Alberto Traverso, Thomas de Bel, Moira S. N. Berens, Cas van den Bogaard, Piergiorgio Cerello, Hao Chen, Qi Dou, Maria Evelina Fantacci, Bram Geurts, Robbert van der Gugten, Pheng Ann Heng, Bart Jansen, Michael M. J. de Kaste, Valentin Kotov, Jack Yu-Hung Lin, Jeroen T. M. C. Manders, Alexander Sónora-Mengana, Juan Carlos García-Naranjo, Evgenia Papavasileiou, Mathias Prokop, Marco Saletta, Cornelia M Schaefer-Prokop, Ernst T. Scholten, Luuk Scholten, Miranda M. Snoeren, Ernesto Lopez Torres, Jef Vandemeulebroucke, Nicole Walasek, Guido C. A. Zuidhof, Bram van Ginneken, Colin Jacobs

We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set.

Computed Tomography (CT)