@conference{Tarroni2014a,

title = {Near-Automated Quantification of Prenatal Aortic Intima-Media Thickness from Ultrasound Images},

author = { G Tarroni and S Visentin and E Cosmi and E Grisan},

url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7043042},

issn = {2325-8861},

year  = {2014},

date = {2014-01-01},

booktitle = {Computing in Cardiology, 2014},

journal = {Computing in Cardiology, 2014},

pages = {313--316},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Tarroni2010,

title = {MRI-based quantification of myocardial perfusion at rest and stress using automated frame-by-frame segmentation and non-rigid registration},

author = { G. Tarroni and A.R. Patel and F. Veronesi and J. Walter and C. Lamberti and R.M. Lang and V. Mor-Avi and C. Corsi},

url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5737884},

year  = {2010},

date = {2010-01-01},

booktitle = {Computing in Cardiology, 2010},

pages = {1--4},

abstract = {We developed a method for automated quantification of myocardial perfusion from cardiac magnetic resonance (CMR) images. Our approach uses region-based and edge-based level set techniques for endocardial and epicardial border detection combined with non-rigid registration achieved by a 2D multi-scale cross-correlation and contour adaptation. This method was tested on 66 short-axis image sequences (Philips 1.5T) obtained in 11 patients at rest and during vasodilator stress at 3 levels of the left ventricle during first pass of a Gadolinium-DTPA bolus. Myocardial ROIs were automatically defined and contrast enhancement curves were constructed throughout the image sequence. Analysis of one sequence required <;1 min and resulted in endo- and epicardial boundaries that were judged accurate. Curves obtained during stress showed the typical pattern of first-pass perfusion with SNR of 19±4, as well as increased contrast inflow rate (0.031±0.013 vs 0.014±0.004 sec-1) and higher peak-to-peak amplitude (0.20±0.05 vs 0.14±0.03) compared to resting curves. Despite the extreme dynamic nature of contrast enhanced image sequences and respiratory motion, fast automated detection of myocardial segments and quantification of tissue contrast results in time curves with excellent noise levels, which reflect the expected effects of stress.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{tarroni_2016_cinc,

title = {A fully automated approach to aortic distensibility quantification from fetal ultrasound images},

author = {G. Tarroni, 

S. Visentin, 

E. Cosmi, 

E. Grisan},

doi = {10.1109/CIC.2015.7411014},

isbn = {23258861},

year  = {2016},

date = {2016-02-16},

booktitle = {Computing in Cardiology},

volume = {42},

number = {7411014},

pages = {729-732},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Tarroni2015b,

title = {A novel approach to aortic intima-media thickness quantification from fetal ultrasound images},

author = { Giacomo Tarroni and Silvia Visentin and Erich Cosmi and Enrico Grisan},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings - IEEE International Symposium on Biomedical Imaging},

journal = {Proceedings - IEEE International Symposium on Biomedical Imaging},

pages = {-},

note = {Conference of 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2015},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Tarroni2015bb,

title = {A Novel Approach to Motion Correction for ASL Images based on Brain Contours},

author = { Giacomo Tarroni and Marco Castellaro and Carlo Boffano and Maria Grazia Bruzzone and Alessandra Bertoldo and Enrico Grisan},

year  = {2015},

date = {2015-01-01},

booktitle = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE Medical Imaging},

journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE Medical Imaging},

volume = {-},

address = {Orlando, FL},

note = {Conference of SPIE Medical Imaging 2015},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Grisan2015,

title = {A Supervised Learning Approach for the Robust Detection of Heart Beat in Plethysmographic Data},

author = { Enrico Grisan and Giorgia Cantisani and Giacomo Tarroni and Michele Rossi},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings - 37th Annual International Conference of the IEEE Engineering in Medicine and Biology},

journal = {Proceedings - 37th Annual International Conference of the IEEE Engineering in Medicine and Biology},

pages = {-},

note = {37th Annual International Conference of the IEEE Engineering in Medicine and Biology},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Tarroni2015c,

title = {Fully-Automated Identification and Segmentation of Aortic Lumen from Fetal Ultrasound Images},

author = { Giacomo Tarroni and Silvia Visentin and Erich Cosmi and Enrico Grisan},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings - 37th Annual International Conference of the IEEE Engineering in Medicine and Biology},

journal = {Proceedings - 37th Annual International Conference of the IEEE Engineering in Medicine and Biology},

pages = {-},

note = {37th Annual International Conference of the IEEE Engineering in Medicine and Biology},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Tarroni2014a,

title = {Near-Automated Quantification of Prenatal Aortic Intima-Media Thickness from Ultrasound Images},

author = { G Tarroni and S Visentin and E Cosmi and E Grisan},

url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7043042},

issn = {2325-8861},

year  = {2014},

date = {2014-01-01},

booktitle = {Computing in Cardiology, 2014},

journal = {Computing in Cardiology, 2014},

pages = {313--316},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Tarroni2014,

title = {3D Registration of Background-Suppressed ASL Data based on Brain Contours},

author = { Giacomo Tarroni and Marco Castellaro and Carlo Boffano and Maria Grazia Bruzzone and Alessandra Bertoldo and Enrico Grisan},

year  = {2014},

date = {2014-01-01},

booktitle = {Proc. Int. Soc. for Magn. Reson. in Med. 22th, Milan, Italy},

volume = {50},

number = {6},

pages = {6685},

note = {Proc. Int. Soc. for Magn. Reson. in Med. 22th, Milan, Italy},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Tarroni201433,

title = {Automated estimation of aortic intima-media thickness from fetal ultrasound},

author = { Giacomo Tarroni and Silvia Visentin and Erich Cosmi and Enrico Grisan},

url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84921504690&partnerID=40&md5=2cc600d2dbef7d334098fdba07b9907c},

doi = {10.1007/978-3-319-13909-8_5},

isbn = {978-3-319-13908-1},

year  = {2014},

date = {2014-01-01},

booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {8680},

pages = {33-40},

publisher = {Springer International Publishing},

series = {Lecture Notes in Computer Science},

note = {Clinical Image-Based Procedures. Translational Research in Medical Imaging},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Tarroni2013a,

title = {Near-automated 3D segmentation of left and right ventricles on magnetic resonance images},

author = { G. Tarroni and D. Marsili and F. Veronesi and C. Corsi and C. Lamberti and G. Sanguinetti},

doi = {10.1109/ISPA.2013.6703796},

year  = {2013},

date = {2013-01-01},

booktitle = {8th International Symposium on Image and Signal Processing and Analysis (ISPA), 2013},

pages = {522--527},

abstract = {Quantification of left (LV) and right (RV) ventricular volumes and masses from Cardiac Magnetic Resonance (CMR) images is of prime importance for the clinical assessment of a wide variety of cardiac diseases. Despite over a decade of research aimed at the development of fast and reliable tools for automated endo- and epicardial contours identification, the problem is still open, particularly for the RV as a consequence of its more irregular shape and its higher density of trabeculations. In this study, a novel near-automated technique for the segmentation of LV endo- and epicardial as well as RV endocardial contours is presented. The technique is based on a 3D narrow-band statistical level set and on 2D edge-based level set algorithms. The technique was tested on CMR images acquired at both end-diastolic and end-systolic phases. For performance evaluation, an experienced interpreter manually traced ventricular contours, which were used as reference. A series of quantitative error metrics (e.g. mean absolute distance, MAD) were computed between automatically identified and manually traced contours. The results showed the high accuracy of the proposed technique (MAD: LV Endo = 1.4±0.7 px; RV Endo = 1.6±1.2 px; LV Epi = 1.4±0.6 px), which could thus potentially lead to the implementation of a tool for fast and reliable identification of ventricular contours.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}