IAS-LAB PUBLICATIONS
Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies
Authors: Bonaldi Lorenza; Pretto Andrea; Pirri Carmelo; Uccheddu Francesca; Fontanella Chiara Giulia; Stecco Carla
Journal: BIOENGINEERING-BASEL
Published: 2023
DOI: 10.3390/bioengineering10020137
By leveraging the recent development of artificial intelligence algorithms, several medical sectors have benefited from using automatic segmentation tools from bioimaging to segment anatomical structures. Segmentation of the musculoskeletal system is key for studying alterations in anatomical tissue and supporting medical interventions. The clinical use of such tools requires an understanding of the proper method for interpreting data and evaluating their performance. The current systematic review aims to present the common bottlenecks for musculoskeletal structures analysis (e.g., small sample size, data inhomogeneity) and the related strategies utilized by different authors. A search was performed using the PUBMED database with the following keywords: deep learning, musculoskeletal system, segmentation. A total of 140 articles published up until February 2022 were obtained and analyzed according to the PRISMA framework in terms of anatomical structures, bioimaging techniques, pre/post-processing operations, training/validation/testing subset creation, network architecture, loss functions, performance indicators and so on. Several common trends emerged from this survey; however, the different methods need to be compared and discussed based on each specific case study (anatomical region, medical imaging acquisition setting, study population, etc.). These findings can be used to guide clinicians (as end users) to better understand the potential benefits and limitations of these tools.
Volume: 10
Keywords: anatomical structures; artificial intelligence; CT; deep learning; medical imaging; MRI; musculoskeletal system; segmentation; ultrasonography; X-ray;
Anatase Nanoparticles for Raman Nanothermometry
Authors: Pretto Thomas; Franca Marina; Zani Veronica; Gross Silvia; Pedron Danilo; Pilot Roberto; Signorini Raffaella
Journal: 21100856785
Published: 2023
The determination of the local temperature is an interesting and intriguing topic in the nanotechnology and nanomedicine world, in terms of tuning the best noninvasive measurement protocol and identification of the more versatile and performing material. In this paper, the Raman technique and titania NPs have been exploited for the realization of a new optical nanotermometer. Biocompatible titania NPs have been properly synthesized, following a combination of sol-gel and solvothermal green synthesis approaches, with the aim of obtaining samples of pure anatase, characterized by crystallite dimensions defined and good control over the final morphology and dispersibility. Powder XRD measurements and room temperature Raman measurements confirmed that the synthesized samples are single-phase anatase. The SEM images clearly showed the nanometric dimension of NPs. Stokes and anti-Stokes Raman measurements, collected with the excitation laser at 514.5 nm (CW Ar/Kr ion laser), substantiate the possibility of evaluating the local temperature, which has been tested in the range of 298 – 313 K, a range of interest for biological applications. The power of the laser has been carefully chosen in order to avoid eventual heating due to the laser irradiation. The data show that TiO2 NPs possess a high sensitivity and low uncertainty in the range of a few degrees as Raman nanothermometer material.
Keywords: Anatase; Green synthesis; Nanoparticles; Nanothermometer; Non-contact technique; Raman; Temperature;
Improving Generalization of Synthetically Trained Sonar Image Descriptors for Underwater Place Recognition
Authors: Donadi Ivano; Olivastri Emilio; Li Wanmeng; Evangelista Daniele; Pretto Alberto; Fusaro Daniel
Journal: COMPUTER VISION SYSTEMS, ICVS 2023
Published: 2023
DOI: 10.1007/978-3-031-44137-0_28
Autonomous navigation in underwater environments presents challenges due to factors such as light absorption and water turbidity, limiting the effectiveness of optical sensors. Sonar systems are commonly used for perception in underwater operations as they are unaffected by these limitations. Traditional computer vision algorithms are less effective when applied to sonar-generated acoustic images, while convolutional neural networks (CNNs) typically require large amounts of labeled training data that are often unavailable or difficult to acquire. To this end, we propose a novel compact deep sonar descriptor pipeline that can generalize to real scenarios while being trained exclusively on synthetic data. Our architecture is based on a ResNet18 back-end and a properly parameterized random Gaussian projection layer, whereas input sonar data is enhanced with standard ad-hoc normalization/prefiltering techniques. A customized synthetic data generation procedure is also presented. The proposed method has been evaluated extensively using both synthetic and publicly available real data, demonstrating its effectiveness compared to state-of-the-art methods.
Volume: 14253 Pages: 336-349
Keywords: Place Recognition; Sonar Imaging; Underwater Robotics;
SUBLINEAR ALGORITHMS FOR LOCAL GRAPH-CENTRALITY ESTIMATION*
Authors: Bressan Marco; Peserico Enoch; Pretto Luca
Journal: SIAM JOURNAL ON COMPUTING
Published: 2023
DOI: 10.1137/19M1266976
We study the complexity of local graph-centrality estimation, with the goal of approximating the centrality score of a given target node while exploring only a sublinear number of nodes/arcs of the graph and performing a sublinear number of elementary operations. We develop a technique, which we apply to PageRank and Heat Kernel, for constructing a low-variance score estimator through a local exploration of the graph. We obtain an algorithm that, given any node in any graph of n nodes and m arcs, with probability (1-delta) computes a multiplicative (1pmepsilon)-approximation of its score by examining only O~(min(n1/2Delta1/2,n1/2m1/4)) nodes/arcs, where Delta is the maximum outdegree of the graph and poly(epsilon-1) and polylog(delta-1) factors are omitted for readability. A similar bound holds for computational cost. We also prove a lower bound of Omega(min(n1/2Delta1/2, n1/3m1/3)) for both query complexity and computational complexity. Moreover, in the jump-and-crawl graph-access model, our technique yields a O~(min(n1/2Delta1/2,n2/3))-queries algorithm; we show that this algorithm is optimal up to a logarithmic factor-in fact, sublogarithmic in the case of PageRank. These are the first algorithms with sublinear worst-case bounds for general directed graphs and any choice of the target node.
Volume: 52 Pages: 968-1008
Keywords: computational complexity; graph centrality; Heat Kernel; local algorithms; PageRank; query complexity; random walks; sublinear algorithms;
Automatic Segmentation of Stomach of Patients Affected by Obesity
Authors: Pretto Andrea; Toniolo Ilaria; Berardo Alice; Savio Gianpaolo; Perretta Silvana; Carniel Emanuele Luigi; Uccheddu Francesca
Journal: 21100431311
Published: 2023
DOI: 10.1007/978-3-031-15928-2_24
Due to the increasing number of people with severe obesity, the demand for patient-specific modelling in bariatric surgery (BS) is increasing because its potentialities in the improvement of surgical planning, optimization of outcomes and prediction of the mechanical response of the stomach. However, the patient-specific anatomical reconstruction is a pivotal and often time-consuming step due to the lack of efficient and fully automatized tools. Ongoing studies on multi-organ segmentation methods based on neural networks for magnetic resonance images (MRI) are currently available, but they have still several limits, mainly due to both the highly flexible individuals anatomical properties, and convolutional neural networks (CNN) trained only in the detection of physiological stomachs. The aim of this work is to perform a convenient transfer learning from a general-purpose CNN, able to improve the performance in automatically detecting the stomach region of patients with severe obesity. The proposed approach represents the basis for the development of pre- and post-surgical computational models for rapid clinical analysis, especially to boost the mechanical stimulation of gastric receptors. The segmentation masks and the corresponding 3D models were compared with the corresponding manual MRI segmentation as ground truth. Intersection Over Union (IOU) and DICE coefficients (DICE) were used to evaluate 2D masks segmentation, while the Relative Volume Error (RVE), mean surface distance (MSD), standard deviation and the Normalised Hausdorff distance (NHD) were applied to assess the obtained 3D results.
Pages: 276-285
Keywords: Bariatric surgery; Image segmentation; MRI; Stomach;
Euclid preparation – XXIII. Derivation of galaxy physical properties with deep machine learning using mock fluxes and H-band images
Authors: AREA MIN. 02 - Scienze fisiche; MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY###0035-8711; HNI-9120-2023; IRB-5227-2023; I-6985-2013; O-9495-2015; J-2774-2019; B-4650-2017; EQT-2114-2022; M-4118-2013; GFK-2340-2022; GDK-6541-2022; ABC-8644-2021; H-4394-2019; EDW-5764-2022; EJM-8740-2022; CDR-2303-2022; C-4378-2014; HMR-4007-2023; EKV-4052-2022; DVB-2560-2022; L-8385-2017; M-2616-2015; AAG-7753-2020; GBO-0318-2022; E-2727-2014; L-8237-2014; E-8021-2017; IVA-4275-2023; GBS-0220-2022; H-8587-2015; DVC-6323-2022; B-4928-2015; GBN-8818-2022; PGG-2427-2026; C-1574-2008; A-2693-2010; PCA-2324-2025; GBB-1963-2022; EUO-2530-2022; ETL-7525-2022; EUK-3820-2022; J-3686-2012; AAR-6622-2021; CQF-5798-2022; DXA-1952-2022; ITP-9423-2023; DVG-8690-2022; GAZ-3876-2022; DWB-6758-2022; GQH-6424-2022; ABF-7029-2021; DWK-1716-2022; CTE-6775-2022; CSK-3817-2022; CUA-0149-2022; FBF-5584-2022; FBV-0790-2022; CTZ-4163-2022; GBH-2365-2022; DWQ-9372-2022; OOP-8239-2025; DXH-0671-2022; FFG-2233-2022; GEK-4486-2022; DAV-9216-2022; DUU-4676-2022; B-8502-2016; GFM-0308-2022; A-2699-2012; FIV-3763-2022; DWC-8789-2022; DWZ-6747-2022; DFQ-7859-2022; AAH-9937-2020; AAX-3485-2021; D-1300-2016; FNO-5530-2022; GNG-7078-2022; AAB-4321-2020; DVP-3997-2022; FNA-5485-2022; AAW-4410-2021; DWT-4779-2022; DZP-5216-2022; FNB-0821-2022; C-3218-2017; FQP-7090-2022; DWD-4131-2022; DLB-6897-2022; GBD-7573-2022; IVG-7504-2023; FSY-2184-2022; DMX-5934-2022; DNY-0415-2022; K-4114-2015; OON-3882-2025; AFE-8548-2022; HTB-0114-2023; DXL-4304-2022; GCA-5113-2022; FVF-5606-2022; GCT-2940-2022; DXO-8435-2022; H-1761-2016; DXM-5348-2022; DPD-7597-2022; PHJ-7571-2026; IZJ-2041-2023; GBV-4959-2022; I-8498-2012; J-5067-2012; EAA-4768-2022; L-8068-2014; CFK-7257-2022; MQB-6975-2025; Q-2220-2015; T-7378-2018; AAB-2503-2019; GCB-5227-2022; DYT-7473-2022; HNI-8187-2023; GCA-5567-2022; Q-6715-2019; DZU-8266-2022; EAZ-0566-2022; EIA-6036-2022; FYJ-9637-2022; O-9369-2015; GDW-2905-2022; HTG-8587-2023; HTA-5649-2023; GBY-6621-2022; GAU-7672-2022; FLK-4707-2022; FNC-4379-2022; FVK-3262-2022; GFA-3443-2022; P-2194-2018; O-9396-2015; B-3004-2019; AAZ-4907-2020; AAO-6325-2021; CHT-0596-2022; DTO-7937-2022; EPI-1133-2022; O-9391-2015; KDL-3231-2024; JFJ-3489-2023; OZD-6988-2025; KUC-6512-2024; CMH-0439-2022; AGZ-3259-2022; PCU-7129-2025; PDR-7717-2025; ERD-3189-2022; CNT-5485-2022; IDQ-0489-2023; ISC-4027-2023; AAW-1061-2020; CPC-6980-2022; KLD-3528-2024; CRZ-8120-2022; C-2920-2017; PDR-5897-2025; DWN-8747-2022; ABB-8257-2020; JZW-7667-2024; S-8590-2017; GBV-7145-2022; ITW-2356-2023; DWS-1040-2022; HRX-7202-2023; IZP-8032-2023; FZY-7746-2022; DWL-3001-2022; FLM-0394-2022; HUJ-7899-2023; U-7309-2018; DZP-0372-2022; D-1237-2017; PHJ-0952-2026; AAI-1245-2021; AAN-7016-2021; LXV-7382-2024; DYG-8551-2022; FTV-6637-2022; GCY-0967-2022; AAY-8788-2021; GGM-6223-2022; GEF-7978-2022; FXG-6905-2022; FXS-9180-2022; GBG-9412-2022; IYS-3498-2023; GCB-1754-2022; DXU-7894-2022; LXA-1722-2024; ECF-2024-2022; A-9058-2016; DYK-4428-2022; 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Journal: MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Published: 2023
Next-generation telescopes, like Euclid, Rubin/LSST, and Roman, will open new windows on the Universe, allowing us to infer physical properties for tens of millions of galaxies. Machine-learning methods are increasingly becoming the most efficient tools to handle this enormous amount of data, because they are often faster and more accurate than traditional methods. We investigate how well redshifts, stellar masses, and star-formation rates (SFRs) can be measured with deep-learning algorithms for observed galaxies within data mimicking the Euclid and Rubin/LSST surveys. We find that deep-learning neural networks and convolutional neural networks (CNNs), which are dependent on the parameter space of the training sample, perform well in measuring the properties of these galaxies and have a better accuracy than methods based on spectral energy distribution fitting. CNNs allow the processing of multiband magnitudes together with HE-band images. We find that the estimates of stellar masses improve with the use of an image, but those of redshift and SFR do not. Our best results are deriving (i) the redshift within a normalized error of 3 in the HE band; (ii) the stellar mass within a factor of two (∼0.3 dex) for 99.5 per cent of the considered galaxies; and (iii) the SFR within a factor of two (∼0.3 dex) for ∼70 per cent of the sample. We discuss the implications of our work for application to surveys as well as how measurements of these galaxy parameters can be improved with deep learning.
Volume: 520 Pages: 3529-3548
Keywords: galaxies: evolution; galaxies: general; galaxies: photometry; galaxies: star formation;
Stellar metallicity from optical and UV spectral indices: Test case for WEAVE-StePS
Authors: Ditrani F. R.; Longhetti M.; La Barbera F.; Iovino A.; Costantin L.; Zibetti S.; Gallazzi A.; Fossati M.; Angthopo J.; Ascasibar Y.; Poggianti B.; Sanchez-Blazquez P.; Balcells M.; Bianconi M.; Bolzonella M.; Cassara L. P.; Cucciati O.; Dalton G.; Ferre-Mateu A.; Garcia-Benito R.; Granett B.; Gullieuszik M.; Ikhsanova A.; Jin S.; Knapen J. H.; McGee S.; Mercurio A.; Morelli L.; Moretti A.; Murphy D.; Pizzella A.; Pozzetti L.; Spiniello C.; Tortora C.; Trager S. C.; Vazdekis A.; Vergani D.; Vulcani B.; Ditrani F.R.; Sánchez-Blázquez P.; Cassarà L.P.; Ferré-Mateu A.; García-Benito R.; Knapen J.H.; Trager S.C.
Journal: ASTRONOMY & ASTROPHYSICS
Published: 2023
DOI: 10.1051/0004-6361/202346563
Context. The upcoming generation of optical spectrographs on four meter-class telescopes, with their huge multiplexing capabilities, excellent spectral resolution, and unprecedented wavelength coverage, will provide high-quality spectra for thousands of galaxies. These data will allow us to examine of the stellar population properties at intermediate redshift, an epoch that remains unexplored by large and deep surveys. Aims. We assess our capability to retrieve the mean stellar metallicity in galaxies at different redshifts and signal-to-noise ratios (S/N), while simultaneously exploiting the ultraviolet (UV) and optical rest-frame wavelength coverage. Methods. The work is based on a comprehensive library of spectral templates of stellar populations, covering a wide range of age and metallicity values and built assuming various star formation histories, to cover an observable parameter space with diverse chemical enrichment histories and dust attenuation. We took into account possible observational errors, simulating realistic observations of a large sample of galaxies carried out with WEAVE at the William Herschel Telescope at different redshifts and S/N values. We measured all the available and reliable indices on the simulated spectra and on the comparison library. We then adopted a Bayesian approach to compare the two sets of measurements in order to obtain the probability distribution of stellar metallicity with an accurate estimate of the uncertainties. Results. The analysis of the spectral indices has shown how some mid-UV indices, such as BL3580 and Fe3619, can provide reliable constraints on stellar metallicity, along with optical indicators. The analysis of the mock observations has shown that even at S/N = 10, the metallicity can be derived within 0.3 dex, in particular, for stellar populations older than 2 Gyr. The S/N value plays a crucial role in the uncertainty of the estimated metallicity and so, the differences between S/N = 10 and S/N = 30 are quite large, with uncertainties of ~0.15 dex in the latter case. On the contrary, moving from S/N = 30 to S/N = 50, the improvement on the uncertainty of the metallicity measurements is almost negligible. Our results are in good agreement with other theoretical and observational works in the literature and show how the UV indicators, coupled with classic optical ones, can be advantageous in constraining metallicities. Conclusions. We demonstrate that a good accuracy can be reached on the spectroscopic measurements of the stellar metallicity of galaxies at intermediate redshift, even at low S/N, when a large number of indices can be employed, including some UV indices. This is very promising for the upcoming surveys carried out with new, highly multiplexed, large-field spectrographs, such as StePS at the WEAVE and 4MOST, which will provide spectra of thousands of galaxies covering large spectral ranges (between 3600 and 9000 Å in the observed frame) at relatively high S/N (>10 Å -1).
Volume: 677
Keywords: Galaxies: evolution; Galaxies: formation; Galaxies: stellar content;
Euclid preparation: XXV. the Euclid Morphology Challenge: Towards model-fitting photometry for billions of galaxies
Authors: AREA MIN. 02 - Scienze fisiche; ASTRONOMY & ASTROPHYSICS###0004-6361; DVP-3997-2022; IVA-4275-2023; CIW-4665-2022; GBV-7145-2022; GWV-3568-2022; EDC-3693-2022; K-9464-2019; HHF-0466-2022; B-4348-2013; CIE-6835-2022; AFS-1680-2022; JQO-3317-2023; DTP-1685-2022; GWA-7849-2022; JMX-5918-2023; EXH-2410-2022; GRO-8426-2022; B-9902-2008; ETY-6882-2022; FZT-3187-2022; ABC-3716-2021; DWU-8294-2022; CUC-4357-2022; JWP-3376-2024; H-5733-2014; DQT-2192-2022; H-4394-2019; EJM-8740-2022; FZO-1254-2022; GAK-9927-2022; CDR-2303-2022; C-4378-2014; GBF-1843-2022; EKV-4052-2022; L-8385-2017; J-2774-2019; AAG-7753-2020; GBO-0318-2022; E-2727-2014; L-8237-2014; E-8021-2017; B-4650-2017; EQA-1830-2022; GLW-3038-2022; H-8587-2015; DVC-6323-2022; B-4928-2015; GBN-8818-2022; LEQ-1557-2024; FZL-7353-2022; A-2693-2010; PCA-2324-2025; HKB-2933-2023; GBB-1963-2022; EUO-2530-2022; ETL-7525-2022; EUK-3820-2022; J-3686-2012; AAR-6622-2021; CQF-5798-2022; DXA-1952-2022; ITP-9423-2023; DVG-8690-2022; GBB-1832-2022; DWB-6758-2022; ABF-7029-2021; DWK-1716-2022; KKH-4411-2024; CSK-3817-2022; FBF-5584-2022; HRW-8595-2023; FBV-0790-2022; CTZ-4163-2022; GBH-2365-2022; EYY-4006-2022; DWQ-9372-2022; DXH-0671-2022; FFG-2233-2022; FGD-1080-2022; DAV-9216-2022; DUU-4676-2022; B-8502-2016; GFM-0308-2022; A-2699-2012; FIV-3763-2022; DWZ-6747-2022; DFQ-7859-2022; AAH-9937-2020; AAX-3485-2021; D-1300-2016; AAA-1489-2019; GNG-7078-2022; DFC-8070-2022; AAB-4321-2020; KEK-6332-2024; AAW-4410-2021; DWT-4779-2022; DZP-5216-2022; FNB-0821-2022; C-3218-2017; HTJ-4919-2023; DWD-4131-2022; DLB-6897-2022; HTM-1531-2023; IVG-7504-2023; FSY-2184-2022; DMX-5934-2022; ABC-8644-2021; DNY-0415-2022; AAR-4345-2020; K-4114-2015; OON-3882-2025; AFE-8548-2022; DXL-4304-2022; GCT-2940-2022; DXO-8435-2022; H-1761-2016; DXM-5348-2022; DPD-7597-2022; IZJ-2041-2023; GBV-4959-2022; I-8498-2012; J-5067-2012; NES-1075-2025; JAE-9097-2023; L-8068-2014; CFK-4637-2022; Q-2220-2015; T-7378-2018; GCB-5227-2022; HNI-8187-2023; GCA-5567-2022; Q-6715-2019; ABD-6783-2021; DZU-8266-2022; EAZ-0566-2022; EIA-6036-2022; FYJ-9637-2022; O-9369-2015; GDW-2905-2022; HTG-8587-2023; CMV-6954-2022; GBY-6621-2022; GAU-7672-2022; B-8712-2017; FNC-4379-2022; DJO-8166-2022; FVK-3262-2022; AAQ-4773-2020; O-9396-2015; CEY-5520-2022; DVH-0096-2022; AAZ-4907-2020; IUS-1317-2023; AAO-6325-2021; KTI-3074-2024; B-3004-2019; DTO-7937-2022; EPI-1133-2022; O-9391-2015; KDL-3231-2024; JFJ-3489-2023; OZD-6988-2025; KUC-6512-2024; CMH-0439-2022; AGZ-3259-2022; EQT-2114-2022; PDR-7717-2025; ERD-3189-2022; CNT-5485-2022; ISC-4027-2023; AAW-1061-2020; CPC-6980-2022; CRZ-8120-2022; C-2920-2017; PDR-5897-2025; ABB-8257-2020; JOZ-7589-2023; S-8590-2017; AAK-4578-2020; FYO-6165-2022; GZL-0460-2022; ITW-2356-2023; DWS-1040-2022; HRX-7202-2023; IZP-8032-2023; FZY-7746-2022; DWL-3001-2022; FLM-0394-2022; ABA-3922-2020; HUJ-7899-2023; U-7309-2018; DZP-0372-2022; LRV-2049-2024; FKW-0156-2022; D-1237-2017; Z-3406-2019; AAI-1245-2021; AAN-7016-2021; LXV-7382-2024; DYG-8551-2022; C-3470-2011; JCV-3612-2023; GCY-0967-2022; AAY-8788-2021; GGM-6223-2022; GEF-7978-2022; FXG-6905-2022; FXS-9180-2022; GBG-9412-2022; GCB-1754-2022; DXU-7894-2022; LXA-1722-2024; ECF-2024-2022; A-9058-2016; EDW-5764-2022; AAC-2083-2020; DYK-4428-2022; DXW-9679-2022; MDK-9017-2025; 14832846900; 24439181000; 57225065454; 23050749700; 55868798300; 57194413215; 36195346900; 35402086700; 7003910265; 57211860571; 56925206200; 9334587700; 55420010100; 56216916000; 58149234200; 24279354600; 57221803833; 7005656061; 6602631317; 8719314400; 57217712130; 7801607411; 57215682687; 57216343493; 15754453800; 57194419532; 55929371000; 6701390827; 14629998500; 57194514014; 8651648800; 24482926400; 7102960752; 6506892241; 6701447926; 7004168457; 35117442400; 56592859600; 8316050500; 57193414472; 7004614794; 54924573500; 56260193000; 55959642400; 37121732700; 6507398813; 55757270100; 56018146100; 8856476200; 7004529134; 26663174300; 6603213706; 57212263363; 6701702242; 6506323808; 55978579700; 6601991850; 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7003963996; 57217789465; 7404952697
Journal: ASTRONOMY & ASTROPHYSICS
Published: 2023
DOI: 10.1051/0004-6361/202245041
The European Space Agency’s Euclid mission will provide high-quality imaging for about 1.5 billion galaxies. A software pipeline to automatically process and analyse such a huge amount of data in real time is being developed by the Science Ground Segment of the Euclid Consortium; this pipeline will include a model-fitting algorithm, which will provide photometric and morphological estimates of paramount importance for the core science goals of the mission and for legacy science. The Euclid Morphology Challenge is a comparative investigation of the performance of five model-fitting software packages on simulated Euclid data, aimed at providing the baseline to identify the best-suited algorithm to be implemented in the pipeline. In this paper we describe the simulated dataset, and we discuss the photometry results. A companion paper is focussed on the structural and morphological estimates. We created mock Euclid images simulating five fields of view of 0.48 deg2 each in the IE band of the VIS instrument, containing a total of about one and a half million galaxies (of which 350 000 have a nominal signal-to-noise ratio above 5), each with three realisations of galaxy profiles (single and double Sérsic, and ‘realistic’ profiles obtained with a neural network); for one of the fields in the double Sérsic realisation, we also simulated images for the three near-infrared YE, JE, and HE bands of the NISP-P instrument, and five Rubin/LSST optical complementary bands (u, g, r, i, and z), which together form a typical dataset for an Euclid observation. The images were simulated at the expected Euclid Wide Survey depths. To analyse the results, we created diagnostic plots and defined metrics to take into account the completeness of the provided catalogues, as well as the median biases, dispersions, and outlier fractions of their measured flux distributions. Five model-fitting software packages (DeepLeGATo, Galapagos-2, Morfometryka, ProFit, and SourceXtractor++) were compared, all typically providing good results. Of the differences among them, some were at least partly due to the distinct strategies adopted to perform the measurements. In the best-case scenario, the median bias of the measured fluxes in the analytical profile realisations is below 1% at a signal-to-noise ratio above 5 in IE, and above 10 in all the other bands; the dispersion of the distribution is typically comparable to the theoretically expected one, with a small fraction of catastrophic outliers. However, we can expect that real observations will prove to be more demanding, since the results were found to be less accurate for the most realistic realisation. We conclude that existing model-fitting software can provide accurate photometric measurements on Euclid datasets. The results of the challenge are fully available and reproducible through an online plotting tool.
Volume: 671
Keywords: Galaxies: photometry; Methods: data analysis; Surveys; Techniques: photometric;
Towards a muon collider
Authors: Non assegn; AREA MIN. 02 - Scienze fisiche; AREA MIN. 09 - Ingegneria industriale e dell'informazione; THE EUROPEAN PHYSICAL JOURNAL. C, PARTICLES AND FIELDS###1434-6044; FYK-9474-2022; JXH-3132-2024; CBT-5100-2022; CBO-5484-2022; GXG-4131-2022; HDN-9630-2022; GAF-5089-2022; CBM-6264-2022; EJZ-8437-2022; ELP-6741-2022; ELT-7023-2022; CBV-3628-2022; HWS-2912-2023; EJO-6831-2022; AAD-1249-2019; N-2616-2016; EJP-9173-2022; KFI-9671-2024; OPO-1688-2025; OUW-4280-2025; ABF-2326-2020; ELQ-1684-2022; OFI-4992-2025; HSI-4895-2023; E-8371-2014; AAZ-7440-2021; GBJ-0556-2022; LND-6444-2024; HPR-0543-2023; EMV-7074-2022; CGM-2065-2022; DUA-7658-2022; CHD-5253-2022; FZC-0419-2022; CFN-0061-2022; MQS-0901-2025; GDW-7839-2022; KHA-8040-2024; ENH-6825-2022; CHK-2982-2022; OFB-9067-2025; GCN-2143-2022; EOX-2064-2022; GOJ-9505-2022; ABH-1850-2021; GCN-3824-2022; EOQ-2444-2022; FZT-9239-2022; CJG-1785-2022; EQO-1488-2022; L-3623-2018; CIC-4585-2022; HSS-4497-2023; DUK-8749-2022; GPX-8560-2022; ABF-5841-2020; DUZ-4694-2022; KRQ-4175-2024; KKS-4321-2024; FYB-3471-2022; ITZ-9738-2023; DVN-0598-2022; KGK-4347-2024; CJH-9758-2022; AAY-3232-2020; JTS-3830-2023; CKK-5051-2022; C-5670-2012; LGT-0834-2024; ERD-0592-2022; ERZ-9626-2022; GEW-9612-2022; KAX-3097-2024; AAA-7177-2020; CMK-2328-2022; CLR-2238-2022; MTM-5239-2025; CNH-9435-2022; FXV-8765-2022; ESD-2839-2022; CNO-5551-2022; CNP-2629-2022; DVD-2246-2022; ETU-2780-2022; CNO-7527-2022; N-1001-2016; ESQ-7551-2022; PDF-1988-2025; ESM-8648-2022; COI-3179-2022; AAY-6673-2020; CPL-4805-2022; MUX-1732-2025; AAD-7755-2019; GEP-1969-2022; AAG-9056-2019; KGW-8980-2024; DSK-9477-2022; DWV-9160-2022; HKE-5979-2023; CQK-1614-2022; DVK-5456-2022; DWV-6294-2022; GKK-4996-2022; J-3028-2012; FZX-7892-2022; CQD-7457-2022; CQF-4975-2022; DVF-7013-2022; JKK-4692-2023; EYC-2565-2022; JNA-2474-2023; OVY-4226-2025; MKK-6147-2025; F-1674-2015; HRF-5560-2023; GFG-2021-2022; EZP-8140-2022; GBZ-2211-2022; LWP-7470-2024; LFC-7706-2024; CVD-6013-2022; LQK-1439-2024; DXL-4135-2022; KKZ-9641-2024; CUU-8008-2022; AAU-1638-2020; GLT-5738-2022; AAS-6657-2021; EZW-7171-2022; CWL-8626-2022; CWN-4108-2022; JOQ-5785-2023; HVD-5171-2023; MLN-7196-2025; JNT-9826-2023; GXM-8803-2022; DVS-6196-2022; FFR-5442-2022; DTP-9281-2022; HZZ-3425-2023; DXX-5838-2022; IFJ-3836-2023; AAC-4015-2021; DCC-1557-2022; AAU-6790-2021; LNW-5957-2024; P-5265-2014; FJI-7907-2022; H-4121-2019; KGZ-2480-2024; IEC-9911-2023; JHW-5806-2023; FZU-4399-2022; DXP-8049-2022; DTV-2203-2022; FHW-2051-2022; MQN-8435-2025; JDM-7550-2023; FJF-7561-2022; GEZ-2602-2022; GYA-2528-2022; FIO-2556-2022; NAZ-0784-2025; DET-5763-2022; GSD-6351-2022; HPN-8412-2023; GBV-0788-2022; JMU-4886-2023; GCW-4690-2022; ABC-6479-2021; GAX-5411-2022; DVQ-7540-2022; GDT-6804-2022; C-9292-2015; DXZ-1813-2022; NPJ-2611-2025; GDS-3478-2022; KGV-8151-2024; FME-5262-2022; IEN-7067-2023; FRF-9581-2022; DII-0652-2022; OSO-4797-2025; DXB-3939-2022; FNQ-0369-2022; FPZ-3381-2022; JAJ-5712-2023; JKH-5719-2023; IUR-7139-2023; OSM-4812-2025; DLC-3167-2022; J-8413-2017; GFM-7428-2022; DZX-1700-2022; A-8220-2018; FSU-7131-2022; FPG-6809-2022; AAJ-8429-2020; JRZ-0528-2023; KZV-0289-2024; FPX-2774-2022; DYK-2911-2022; OUW-3912-2025; AAD-5618-2022; FTB-5406-2022; DNG-9858-2022; I-1033-2014; GCW-1241-2022; Q-1115-2018; FXO-9182-2022; DYO-9039-2022; HSV-1795-2023; GHB-8331-2022; GGX-8856-2022; GQG-6646-2022; HNX-0204-2023; FSD-3179-2022; AAK-8280-2020; LCD-3236-2024; DQU-8879-2022; LRV-0707-2024; FWL-9354-2022; KFO-7165-2024; AAI-7939-2020; H-1886-2012; R-5298-2017; DYU-9872-2022; FZN-6230-2022; GEJ-1360-2022; D-9109-2016; KUD-1535-2024; H-5504-2015; DYC-6161-2022; DSC-6309-2022; OVP-5661-2025; JAC-5093-2023; E-4025-2014; FVP-2009-2022; IQR-8987-2023; HMG-0035-2023; DXP-1400-2022; DTQ-2611-2022; HDM-3686-2022; DTF-0825-2022; HQX-3405-2023; GCE-8025-2022; NXB-7807-2025; PDQ-8422-2025; CBX-6356-2022; DXK-6747-2022; AAL-7450-2020; DYV-7358-2022; GBU-9924-2022; EAA-7831-2022; JED-2068-2023; DYW-5324-2022; FZQ-0369-2022; KAR-7175-2024; GCB-6898-2022; KKR-0020-2024; GBZ-6722-2022; GBI-6421-2022; IYS-7842-2023; G-6251-2010; AAC-4162-2021; DWS-1143-2022; NES-9267-2025; GER-8065-2022; N-9558-2014; GMV-9678-2022; M-3246-2019; GDZ-8430-2022; AAT-5836-2020; GEX-6430-2022; IYS-8904-2023; DYG-0871-2022; IAO-4780-2023; GBE-8524-2022; IVH-5947-2023; DXF-1347-2022; AAA-8819-2019; ISU-0855-2023; GBX-4051-2022; MFB-3011-2025; EFL-5534-2022; AAG-4163-2020; KKB-6851-2024; GJO-4491-2022; HGD-9283-2022; LCV-2259-2024; AAI-6963-2021; OVJ-7539-2025; GLB-4212-2022; EIE-9246-2022; HOL-7761-2023; IXX-4640-2023; GKC-3212-2022; LWK-1941-2024; MTB-6503-2025; 57204199382; 56119018800; 57892704100; 57203181693; 57220389726; 35285501100; 57204197233; 24758083600; 56978805100; 15752667000; 35274438700; 6603246277; 6506606859; 57195564403; 55135134500; 12797409100; 57202159934; 57222598988; 55194865900; 57203666333; 51763191300; 8627402500; 56604132500; 57208122370; 55667537000; 21933344900; 6507465732; 35226997300; 57207983072; 6507576030; 7004429063; 7005687798; 8583776900; 57219471620; 7006777765; 6603734062; 7005171207; 57894157800; 55841127700; 57204040430; 7102657549; 57201508539; 50061032000; 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Journal: EUROPEAN PHYSICAL JOURNAL C
Published: 2023
DOI: 10.1140/epjc/s10052-023-11889-x
A muon collider would enable the big jump ahead in energy reach that is needed for a fruitful exploration of fundamental interactions. The challenges of producing muon collisions at high luminosity and 10 TeV centre of mass energy are being investigated by the recently-formed International Muon Collider Collaboration. This Review summarises the status and the recent advances on muon colliders design, physics and detector studies. The aim is to provide a global perspective of the field and to outline directions for future work.
Volume: 83
Personalised management of patients with hepatocellular carcinoma: a multiparametric therapeutic hierarchy concept
Authors: Vitale Alessandro; Cabibbo Giuseppe; Iavarone Massimo; Vigano Luca; Pinato DavidJ; Ponziani Francesca Romana; Lai Quirino; Casadei-Gardini Andrea; Celsa Ciro; Galati Giovanni; Gambato Martina; Crocetti Laura; Renzulli Matteo; Giannini Edoardo G.; Farinati Fabio; Trevisani Franco; Cillo Umberto
Journal: LANCET ONCOLOGY
Published: 2023
DOI: 10.1016/S1470-2045(23)00186-9
Advances in the surgical and systemic therapeutic landscape of hepatocellular carcinoma have increased the complexity of patient management. A dynamic adaptation of the available staging-based algorithms is required to allow flexible therapeutic allocation. In particular, real-world hepatocellular carcinoma management increasingly relies on factors independent of oncological staging, including patients’ frailty, comorbid burden, critical tumour location, multiple liver functional parameters, and specific technical contraindications impacting the delivery of treatment and resource availability. In this Policy Review we critically appraise how treatment allocation strictly based on pretreatment staging features has shifted towards a more personalised treatment approach, in which expert tumour boards assume a central role. We propose an evidence-based framework for hepatocellular carcinoma treatment based on the novel concept of multiparametric therapeutic hierarchy, in which different therapeutic options are ordered according to their survival benefit (ie, from surgery to systemic therapy). Moreover, we introduce the concept of converse therapeutic hierarchy, in which therapies are ordered according to their conversion abilities or adjuvant abilities (ie, from systemic therapy to surgery).
Volume: 24 Pages: E312-E322