S
Sara Moccia
Researcher at Sant'Anna School of Advanced Studies
Publications - 110
Citations - 1419
Sara Moccia is an academic researcher from Sant'Anna School of Advanced Studies. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 12, co-authored 74 publications receiving 705 citations. Previous affiliations of Sara Moccia include Polytechnic University of Milan & Istituto Italiano di Tecnologia.
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Journal ArticleDOI
Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics
TL;DR: No single segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the vessel segmentation algorithms so that the most suitable methods can be chosen according to the specific task.
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Deep Learning Based Robotic Tool Detection and Articulation Estimation With Spatio-Temporal Layers
TL;DR: A novel encoder–decoder architecture for surgical instrument joint detection and localization that uses three-dimensional convolutional layers to exploit spatio-temporal features from laparoscopic videos is proposed and appears to be particularly useful when processing images with unseen backgrounds during the training phase.
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Development and testing of a deep learning-based strategy for scar segmentation on CMR-LGE images
Sara Moccia,Sara Moccia,Riccardo Banali,Chiara Martini,Giuseppe Muscogiuri,Gianluca Pontone,Mauro Pepi,Enrico G. Caiani +7 more
TL;DR: The findings of this paper represent an encouraging starting point for the use of fully convolutional neural networks for the segmentation of nonviable scar tissue from CMR-LGE images.
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Confident texture-based laryngeal tissue classification for early stage diagnosis support.
Sara Moccia,Sara Moccia,Elena De Momi,Marco Guarnaschelli,Matteo Savazzi,Andrea Laborai,Luca Guastini,Giorgio Peretti,Leonardo S. Mattos +8 more
TL;DR: The use of texture-based machine-learning algorithms for early stage cancerous laryngeal tissue classification is investigated and the results are a promising step toward a helpful endoscope-integrated processing system to support early stage diagnosis.
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Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy
Sara Moccia,Sebastian J. Wirkert,Hannes Kenngott,Anant Vemuri,Martin Apitz,Benjamin F. B. Mayer,Elena De Momi,Leonardo S. Mattos,Lena Maier-Hein +8 more
TL;DR: This paper significantly enhances the state of art in automatic labeling of endoscopic videos by introducing the use of the confidence metric, and by being the first study to use MI data for in vivo laparoscopic tissue classification.