Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks

no code implementations • 31 Mar 2021 • Kajetan Grodecki, Aditya Killekar, Andrew Lin, Sebastien Cadet, Priscilla McElhinney, Aryabod Razipour, Cato Chan, Barry D. Pressman, Peter Julien, Judit Simon, Pal Maurovich-Horvat, Nicola Gaibazzi, Udit Thakur, Elisabetta Mancini, Cecilia Agalbato, Jiro Munechika, Hidenari Matsumoto, Roberto Menè, Gianfranco Parati, Franco Cernigliaro, Nitesh Nerlekar, Camilla Torlasco, Gianluca Pontone, Damini Dey, Piotr J. Slomka

Strong agreement between expert manual and automatic segmentation was obtained for lung lesions with a Dice score coefficient of 0. 876 $\pm$ 0. 005; excellent correlations of 0. 978 and 0. 981 for ground-glass opacity and high opacity volumes.

Computed Tomography (CT)

Learning a sparse database for patch-based medical image segmentation

no code implementations • 25 Jun 2019 • Moti Freiman, Hannes Nickisch, Holger Schmitt, Pal Maurovich-Horvat, Patrick Donnelly, Mani Vembar, Liran Goshen

We introduce a functional for the learning of an optimal database for patch-based image segmentation with application to coronary lumen segmentation from coronary computed tomography angiography (CCTA) data.

Image Segmentation Medical Image Segmentation +4