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35 results on '"Peressutti, Devis"'

1. Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2

Author

Skakun, Sergii, Wevers, Jan, Brockmann, Carsten, Doxani, Georgia, Aleksandrov, Matej, Batič, Matej, Frantz, David, Gascon, Ferran, Gómez-Chova, Luis, Hagolle, Olivier, López-Puigdollers, Dan, Louis, Jérôme, Lubej, Matic, Mateo-García, Gonzalo, Osman, Julien, Peressutti, Devis, Pflug, Bringfried, Puc, Jernej, Richter, Rudolf, Roger, Jean-Claude, Scaramuzza, Pat, Vermote, Eric, Vesel, Nejc, Zupanc, Anže, and Žust, Lojze

Published
2022
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3. Motion modelling for respiratory motion estimation in minimally invasive cardiac interventions using intraprocedure ultrasound data

Author

Peressutti, Devis, King, Andrew Peter, and Penney, Graeme Patrick

Subjects
610.28
Abstract

Respiratory motion of the heart limits the utility of image-guided cardiac interventions, causing misalignments between the pre-procedure information used for guidance and the intra-procedure moving anatomy and instruments. As a result, the guidance can be misleading, compromising the accuracy and success of the intervention. Respiratory motion models have been proposed to estimate and correct for respiratory motion, but to date their clinical uptake has been very limited due to a lack of accuracy and robustness, and the interruptions that they typically introduce into the clinical work flow. The scope of this project was to devise methods to address these limitations and foster the clinical translation of respiratory motion models. A novel Bayesian respiratory motion model was developed in the first part of the project. The Bayesian framework enables the combination of the robustness of a pre-procedure motion model derived from Magnetic Resonance Imaging with the intraprocedure information provided by 3D echography (echo) images. The main novelties of the approach lie in its probabilistic formulation and its ability to adapt to variable breathing patterns. The Bayesian motion model was further evaluated using live 2D echo images, proving to be accurate using both 2D and 3D echo images. Furthermore, a new motion model-driven echo acquisition framework was developed to acquire 2D echo images that automatically compensates for respiratory motion. The second part of the project addressed the limitations associated with the dynamic calibration scan used to derive the motion model, the acquisition of which causes interruptions to the clinical work flow. A personalisation framework for population based motion models that uses anatomical features to predict cardiac respiratory motion was developed. Results show an average value for the 50th and 95th quantiles of the estimation error of 1:6mm and 4:7mm respectively, without the need for a subject-specific dynamic calibration scan. Finally, the above mentioned parts were combined to produce a personalised Bayesian motion model. The technique is accurate and does not significantly complicate the clinical workflow, thus making it suitable for clinical uptake.

Published
2014

4. Learning Optimal Spatial Scales for Cardiac Strain Analysis Using a Motion Atlas

Author

Sinclair, Matthew, Peressutti, Devis, Puyol-Antón, Esther, Bai, Wenjia, Nordsletten, David, Hadjicharalambous, Myrianthi, Kerfoot, Eric, Jackson, Tom, Claridge, Simon, Rinaldi, C. Aldo, Rueckert, Daniel, King, Andrew P., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Mansi, Tommaso, editor, McLeod, Kristin, editor, Pop, Mihaela, editor, Rhode, Kawal, editor, Sermesant, Maxime, editor, and Young, Alistair, editor

Published
2017
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5. Image-Based Real-Time Motion Gating of 3D Cardiac Ultrasound Images

Author

Panayiotou, Maria, Peressutti, Devis, King, Andrew P., Rhode, Kawal S., James Housden, R., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Mansi, Tommaso, editor, McLeod, Kristin, editor, Pop, Mihaela, editor, Rhode, Kawal, editor, Sermesant, Maxime, editor, and Young, Alistair, editor

Published
2017
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8. Towards Left Ventricular Scar Localisation Using Local Motion Descriptors

Author

Peressutti, Devis, Bai, Wenjia, Shi, Wenzhe, Tobon-Gomez, Catalina, Jackson, Thomas, Sohal, Manav, Rinaldi, Aldo, Rueckert, Daniel, King, Andrew, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Camara, Oscar, editor, Mansi, Tommaso, editor, Pop, Mihaela, editor, Rhode, Kawal, editor, Sermesant, Maxime, editor, and Young, Alistair, editor

Published
2016
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9. Beyond the AHA 17-Segment Model: Motion-Driven Parcellation of the Left Ventricle

Author

Bai, Wenjia, Peressutti, Devis, Parisot, Sarah, Oktay, Ozan, Rajchl, Martin, O’Regan, Declan, Cook, Stuart, King, Andrew, Rueckert, Daniel, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Camara, Oscar, editor, Mansi, Tommaso, editor, Pop, Mihaela, editor, Rhode, Kawal, editor, Sermesant, Maxime, editor, and Young, Alistair, editor

Published
2016
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11. Prospective Identification of CRT Super Responders Using a Motion Atlas and Random Projection Ensemble Learning

Author

Peressutti, Devis, Bai, Wenjia, Jackson, Thomas, Sohal, Manav, Rinaldi, Aldo, Rueckert, Daniel, King, Andrew, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Navab, Nassir, editor, Hornegger, Joachim, editor, Wells, William M., editor, and Frangi, Alejandro F., editor

Published
2015
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12. Learning a Global Descriptor of Cardiac Motion from a Large Cohort of 1000+ Normal Subjects

Author

Bai, Wenjia, Peressutti, Devis, Oktay, Ozan, Shi, Wenzhe, O’Regan, Declan P., King, Andrew P., Rueckert, Daniel, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, van Assen, Hans, editor, Bovendeerd, Peter, editor, and Delhaas, Tammo, editor

Published
2015
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17. Learning Optimal Spatial Scales for Cardiac Strain Analysis Using a Motion Atlas

Author

Sinclair, Matthew, primary, Peressutti, Devis, additional, Puyol-Antón, Esther, additional, Bai, Wenjia, additional, Nordsletten, David, additional, Hadjicharalambous, Myrianthi, additional, Kerfoot, Eric, additional, Jackson, Tom, additional, Claridge, Simon, additional, Rinaldi, C. Aldo, additional, Rueckert, Daniel, additional, and King, Andrew P., additional

Published
2017
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21. CMIX: Cloud Mask Intercomparison eXercise

Author

Skakun, Sergii, Wevers, Jan, Brockmann, Carsten, Doxani, Geogia, Aleksandrov, Matej, Batič, Matej, Frantz, David, Gascon, Ferran, Gómez-Chova, Luis, Hagolle, Olivier, López-Puigdollers, Dan, Louis, Jerome, Lubej, Matic, Mateo-García, Gonzalo, OSMAN, Julien, Peressutti, Devis, Pflug, Bringfried, Puc, Jernej, Richter, Rudolf, Roger, Jean-Claude, Scaramuzza, Pat, Vermote, Eric, Vesel, Nejc, Zupanc, Anže, and Žust, Lojze

Subjects
Cloud masking
Published
2022

25. AI4Boundaries: an open AI-ready dataset to map field boundaries with Sentinel-2 and aerial photography.

Author

d'Andrimont, Raphaël, Claverie, Martin, Kempeneers, Pieter, Muraro, Davide, Yordanov, Momchil, Peressutti, Devis, Batič, Matej, and Waldner, François

Subjects
FRAGMENTED landscapes, IMAGE analysis, AERIAL photography, REMOTE-sensing images, DEEP learning, AGRICULTURAL productivity, COMPUTER vision, AGRICULTURAL technology
Abstract

Field boundaries are at the core of many agricultural applications and are a key enabler for operational monitoring of agricultural production to support food security. Recent scientific progress in deep learning methods has highlighted the capacity to extract field boundaries from satellite and aerial images with a clear improvement from object-based image analysis (e.g., multiresolution segmentation) or conventional filters (e.g., Sobel filters). However, these methods need labels to be trained on. So far, no standard data set exists to easily and robustly benchmark models and progress the state of the art. The absence of such benchmark data further impedes proper comparison against existing methods. Besides, there is no consensus on which evaluation metrics should be reported (both at the pixel and field levels). As a result, it is currently impossible to compare and benchmark new and existing methods. To fill these gaps, we introduce AI4Boundaries, a data set of images and labels readily usable to train and compare models on the task of field boundary detection. AI4Boundaries includes two specific data sets: (i) a 10-m Sentinel-2 monthly composites for large-scale analyses in retrospect, (ii) a 1-m orthophoto data set for regional-scale analyses such as the automatic extraction of Geospatial Aid Application (GSAA). All labels have been sourced from GSAA data that have been made openly available (Austria, Catalonia, France, Luxembourg, the Netherlands, Slovenia, and Sweden) for 2019 representing 14.8 M parcels covering 376 K km². Data were selected following a stratified random sampling drawn based on two landscape fragmentation metrics, the perimeter/area ratio and the area covered by parcels, thus taking into account the diversity of the agricultural landscapes. The resulting "AI4Boundaries" dataset consists of 7,831 samples of 256 by 256 pixels for the 10-m Sentinel-2 dataset and of 512 by 512 pixels for the 1-m aerial orthophoto. Both datasets are provided with the corresponding vector ground-truth parcel delineation (2.5 M parcels covering 47,105 km²) and with a raster version already pre-processed and ready to use. Besides providing this open dataset to foster computer vision developments of parcel delineation methods, we discuss perspectives and limitations of the dataset for various types of applications in the agriculture domain and consider possible further improvements. [ABSTRACT FROM AUTHOR]

Published
2022
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27. Can Atlas-Based Auto-Segmentation Ever Be Perfect? Insights From Extreme Value Theory

Author

Schipaanboord, Bas, Schipaanboord, Bas, Boukerroui, Djamal, Peressutti, Devis, van Soest, Johan, Lustberg, Tim, Kadir, Timor, Dekker, Andre, van Elmpt, Wouter, Gooding, Mark, Schipaanboord, Bas, Schipaanboord, Bas, Boukerroui, Djamal, Peressutti, Devis, van Soest, Johan, Lustberg, Tim, Kadir, Timor, Dekker, Andre, van Elmpt, Wouter, and Gooding, Mark

Abstract

Atlas-based segmentation is used in radiotherapy planning to accelerate the delineation of organs at risk (OARs). Atlas selection has been proposed to improve the performance of segmentation, assuming that the more similar the atlas is to the patient, the better the result. It follows that the larger the database of atlases from which to select, the better the results should be. This paper seeks to estimate a clinically achievable expected performance under this assumption. Assuming a perfect atlas selection, an extreme value theory has been applied to estimate the accuracy of single-atlas andmulti-atlas segmentation given a large database of atlases. For this purpose, clinical contours of most common OARs on computed tomography of the head and neck (N = 316) and thoracic (N = 280) cases were used. This paper found that while for most organs, perfect segmentation cannot be reasonably expected, auto-contouring performance of a level corresponding to clinical quality could be consistently expected given a database of 5000 atlases under the assumption of perfect atlas selection.

Published
2019

30. Comparative evaluation of autocontouring in clinical practice: A practical method using the Turing test

Author

Gooding, Mark J., Gooding, Mark J., Smith, Annamarie J., Tariq, Maira, Aljabar, Paul, Peressutti, Devis, van der Stoep, Judith, Reymen, Bart, Emans, Daisy, Hattu, Djoya, van Loon, Judith, de Rooy, Maud, Wanders, Rinus, Peeters, Stephanie, Lustberg, Tim, van Soest, Johan, Dekker, Andre, van Elmpt, Wouter, Gooding, Mark J., Gooding, Mark J., Smith, Annamarie J., Tariq, Maira, Aljabar, Paul, Peressutti, Devis, van der Stoep, Judith, Reymen, Bart, Emans, Daisy, Hattu, Djoya, van Loon, Judith, de Rooy, Maud, Wanders, Rinus, Peeters, Stephanie, Lustberg, Tim, van Soest, Johan, Dekker, Andre, and van Elmpt, Wouter

Abstract

Purpose Methods Automated techniques for estimating the contours of organs and structures in medical images have become more widespread and a variety of measures are available for assessing their quality. Quantitative measures of geometric agreement, for example, overlap with a gold-standard delineation, are popular but may not predict the level of clinical acceptance for the contouring method. Therefore, surrogate measures that relate more directly to the clinical judgment of contours, and to the way they are used in routine workflows, need to be developed. The purpose of this study is to propose a method (inspired by the Turing Test) for providing contour quality measures that directly draw upon practitioners' assessments of manual and automatic contours. This approach assumes that an inability to distinguish automatically produced contours from those of clinical experts would indicate that the contours are of sufficient quality for clinical use. In turn, it is anticipated that such contours would receive less manual editing prior to being accepted for clinical use. In this study, an initial assessment of this approach is performed with radiation oncologists and therapists. Eight clinical observers were presented with thoracic organ-at-risk contours through a web interface and were asked to determine if they were automatically generated or manually delineated. The accuracy of the visual determination was assessed, and the proportion of contours for which the source was misclassified recorded. Contours of six different organs in a clinical workflow were for 20 patient cases. The time required to edit autocontours to a clinically acceptable standard was also measured, as a gold standard of clinical utility. Established quantitative measures of autocontouring performance, such as Dice similarity coefficient with respect to the original clinical contour and the misclassification rate accessed with the proposed framework, were evaluated as surrogates of the editing

Published
2018

31. Registration of multiview echocardiography sequences using a subspace error metric

Author

Peressutti, Devis, Gomez, Alberto, Penney, Graeme P., and King, Andrew P.

Subjects
Computer Science::Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, echocardiography, multiview registration, Dimensionality reduction, principal component analysis (PCA) error metric
Abstract

Objective: 3-D+t echocardiography (3DtE) is widely employed for the assessment of left ventricular anatomy and function. However, the information derived from 3DtE images can be affected by the poor image quality and the limited field of view. Registration of multiview 3DtE sequences has been proposed to compound images from different acoustic windows, therefore improving both image quality and coverage. We propose a novel subspace error metric for an automatic and robust registration of multiview intrasubject 3DtE sequences. Methods: The proposed metric employs linear dimensionality reduction to exploit the similarity in the temporal variation of multiview 3DtE sequences. The use of a low-dimensional subspace for the computation of the error metric reduces the influence of image artefacts and noise on the registration optimization, resulting in fast and robust registrations that do not require a starting estimate. Results: The accuracy, robustness, and execution time of the proposed registration were thoroughly validated. Results on 48 pairwise multiview 3DtE registrations show the proposed error metric to outperform a state-of-the-art phase-based error metric, with improvements in median/75th percentile of the target registration error of 21%/31% and an improvement in mean execution time of 45%. Conclusion: The proposed subspace error metric outperforms sum-of-squared differences and phase-based error metrics for the registration of multiview 3DtE sequences in terms of accuracy, robustness, and execution time. Significance: The use of the proposed subspace error metric has the potential to replace standard image error metrics for a robust and automatic registration of multiview 3DtE sequences.

Published
2017
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32. Comparative evaluation of autocontouring in clinical practice: A practical method using the Turing test

Author

Gooding, Mark J., primary, Smith, Annamarie J., additional, Tariq, Maira, additional, Aljabar, Paul, additional, Peressutti, Devis, additional, van der Stoep, Judith, additional, Reymen, Bart, additional, Emans, Daisy, additional, Hattu, Djoya, additional, van Loon, Judith, additional, de Rooy, Maud, additional, Wanders, Rinus, additional, Peeters, Stephanie, additional, Lustberg, Tim, additional, van Soest, Johan, additional, Dekker, Andre, additional, and van Elmpt, Wouter, additional

Published
2018
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35. Non-invasive Model-Based Assessment of Passive Left-Ventricular Myocardial Stiffness in Healthy Subjects and in Patients with Non-ischemic Dilated Cardiomyopathy

Author

Hadjicharalambous, Myrianthi, primary, Asner, Liya, additional, Chabiniok, Radomir, additional, Sammut, Eva, additional, Wong, James, additional, Peressutti, Devis, additional, Kerfoot, Eric, additional, King, Andrew, additional, Lee, Jack, additional, Razavi, Reza, additional, Smith, Nicolas, additional, Carr-White, Gerald, additional, and Nordsletten, David, additional

Published
2016
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