Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding techniques

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding technique

The Control Unit of the KM3NeT Data Acquisition System

The KM3NeT Collaboration runs a multi-site neutrino observatory in the Mediterranean Sea. Water Cherenkov particle detectors, deep in the sea and far off the coasts of France and Italy, are already taking data while incremental construction progresses. Data Acquisition Control software is operating off-shore detectors as well as testing and qualification stations for their components. The software, named Control Unit, is highly modular. It can undergo upgrades and reconfiguration with the acquisition running. Interplay with the central database of the Collaboration is obtained in a way that allows for data taking even if Internet links fail. In order to simplify the management of computing resources in the long term, and to cope with possible hardware failures of one or more computers, the KM3NeT Control Unit software features a custom dynamic resource provisioning and failover technology, which is especially important for ensuring continuity in case of rare transient events in multi-messenger astronomy. The software architecture relies on ubiquitous tools and broadly adopted technologies and has been successfully tested on several operating systems

Dependence of atmospheric muon flux on seawater depth measured with the first KM3NeT detection units: The KM3NeT Collaboration

KM3NeT is a research infrastructure located in the Mediterranean Sea, that will consist of two deep-sea Cherenkov neutrino detectors. With one detector (ARCA), the KM3NeT Collaboration aims at identifying and studying TeV–PeV astrophysical neutrino sources. With the other detector (ORCA), the neutrino mass ordering will be determined by studying GeV-scale atmospheric neutrino oscillations. The first KM3NeT detection units were deployed at the Italian and French sites between 2015 and 2017. In this paper, a description of the detector is presented, together with a summary of the procedures used to calibrate the detector in-situ. Finally, the measurement of the atmospheric muon flux between 2232–3386 m seawater depth is obtained

KM3NeT broadcast optical data transport system

The optical data transport system of the KM3NeT neutrino telescope at the bottom of the Mediterranean Sea will provide more than 6000 optical modules in the detector arrays with a point-to-point optical connection to the control stations onshore. The ARCA and ORCA detectors of KM3NeT are being installed at a depth of about 3500 m and 2500 m, respectively and their distance to the control stations is about 100 kilometers and 40 kilometers. In particular, the two detectors are optimised for the detection of cosmic neutrinos with energies above about 1 TeV (ARCA) and for the detection of atmospheric neutrinos with energies in the range 1 GeV-1 TeV (ORCA). The expected maximum data rate is 200 Mbps per optical module. The implemented optical data transport system matches the layouts of the networks of electro-optical cables and junction boxes in the deep sea. For efficient use of the fibres in the system the technology of Dense Wavelength Division Multiplexing is applied. The performance of the optical system in terms of measured bit error rates, optical budget are presented. The next steps in the implementation of the system are also discussed

Probing invisible neutrino decay with KM3NeT-ORCA

In the era of precision measurements of the neutrino oscillation parameters, upcoming neutrino experiments will also be sensitive to physics beyond the Standard Model. KM3NeT/ORCA is a neutrino detector optimised for measuring atmospheric neutrinos from a few GeV to around 100 GeV. In this paper, the sensitivity of the KM3NeT/ORCA detector to neutrino decay has been explored. A three-flavour neutrino oscillation scenario, where the third neutrino mass state $\nu_3$ decays into an invisible state, e.g. a sterile neutrino, is considered. We find that KM3NeT/ORCA would be sensitive to invisible neutrino decays with $1/\alpha_3=\tau_3/m_3 < 180$~$\mathrm{ps/eV}$ at $90\%$ confidence level, assuming true normal ordering. Finally, the impact of neutrino decay on the precision of KM3NeT/ORCA measurements for $\theta_{23}$, $\Delta m^2_{31}$ and mass ordering have been studied. No significant effect of neutrino decay on the sensitivity to these measurements has been found.Comment: 27 pages, 14 figures, bibliography updated, typos correcte

Characterisation of the Hamamatsu photomultipliers for the KM3NeT Neutrino Telescope

[EN] The Hamamatsu R12199-02 3-inch photomultiplier tube is the photodetector chosen for the first phase of the KM3NeT neutrino telescope. About 7000 photomultipliers have been characterised for dark count rate, timing spread and spurious pulses. The quantum eÿciency, the gain and the peak-to-valley ratio have also been measured for a sub-sample in order to determine parameter values needed as input to numerical simulations of the detector.The authors acknowledge the financial support of the funding agencies: Agence Nationale de la Recherche (contract ANR-15-CE31-0020), Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund and Marie Curie Program), Institut Universitaire de France (IUF), IdEx program and UnivEarthS Labex program at Sorbonne Paris Cite (ANR-10-LABX-0023 and ANR-11-IDEX-0005-02), France; 'Helmholtz Alliance for Astroparticle Physics' funded by the Initiative and Networking Fund of the Helmholtz Association, Germany; The General Secretariat of Research and Technology (GSRT), Greece; Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Istruzione, dell'Universita e della Ricerca (MIUR), Italy; Agence de l'Oriental and CNRST, Morocco; Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; National Authority for Scientific Research (ANCS), Romania; Plan Estatal de Investigacion (refs. FPA2015-65150-C3-1-P, -2-P and -3-P, (MINECO/FEDER)), Severo Ochoa Centre of Excellence and MultiDark Consolider (MINECO), and Prometeo and Grisolia programs (Generalitat Valenciana), Spain.Aiello, S.; Akrame, SE.; Ameli, F.; Anassontzis, EG.; Andre, M.; Androulakis, G.; Anghinolfi, M.... (2018). Characterisation of the Hamamatsu photomultipliers for the KM3NeT Neutrino Telescope. Journal of Instrumentation. 13:1-17. https://doi.org/10.1088/1748-0221/13/05/P05035S11713Adrián-Martínez, S., Ageron, M., Aharonian, F., Aiello, S., Albert, A., Ameli, F., … Anghinolfi, M. (2016). Letter of intent for KM3NeT 2.0. Journal of Physics G: Nuclear and Particle Physics, 43(8), 084001. doi:10.1088/0954-3899/43/8/084001Adrián-Martínez, S., Ageron, M., Aharonian, F., Aiello, S., Albert, A., Ameli, F., … Anvar, S. (2014). Deep sea tests of a prototype of the KM3NeT digital optical module. The European Physical Journal C, 74(9). doi:10.1140/epjc/s10052-014-3056-3Adrián-Martínez, S., Ageron, M., Aharonian, F., Aiello, S., Albert, A., Ameli, F., … Anton, G. (2016). The prototype detection unit of the KM3NeT detector. The European Physical Journal C, 76(2). doi:10.1140/epjc/s10052-015-3868-9Herold, B., Kalekin, O., & Reubelt, J. (2011). PMT characterisation for the KM3NeT project. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 639(1), 70-72. doi:10.1016/j.nima.2010.09.018Timmer, P., Heine, E., & Peek, H. (2010). Very low power, high voltage base for a Photo Multiplier Tube for the KM3NeT deep sea neutrino telescope. Journal of Instrumentation, 5(12), C12049-C12049. doi:10.1088/1748-0221/5/12/c12049Mollo, C. M., Bozza, C., Chiarusi, T., Costa, M., Capua, F. D., Kulikovskiy, V., … Vivolo, D. (2016). A new instrument for high statistics measurement of photomultiplier characteristics. Journal of Instrumentation, 11(08), T08002-T08002. doi:10.1088/1748-0221/11/08/t08002Adrián-Martínez, S., Ageron, M., Aiello, S., Albert, A., Ameli, F., Anassontzis, E. G., … Anton, G. (2016). A method to stabilise the performance of negatively fed KM3NeT photomultipliers. Journal of Instrumentation, 11(12), P12014-P12014. doi:10.1088/1748-0221/11/12/p12014Lubsandorzhiev, B. K., Vasiliev, R. V., Vyatchin, Y. E., & Shaibonov, B. A. J. (2006). Photoelectron backscattering in vacuum phototubes. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 567(1), 12-16. doi:10.1016/j.nima.2006.05.04

The Power Board of the KM3NeT Digital Optical Module: design, upgrade, and production

The KM3NeT Collaboration is building an underwater neutrino observatory at the bottom of the Mediterranean Sea consisting of two neutrino telescopes, both composed of a three-dimensional array of light detectors, known as digital optical modules. Each digital optical module contains a set of 31 three inch photomultiplier tubes distributed over the surface of a 0.44 m diameter pressure-resistant glass sphere. The module includes also calibration instruments and electronics for power, readout and data acquisition. The power board was developed to supply power to all the elements of the digital optical module. The design of the power board began in 2013, and several prototypes were produced and tested. After an exhaustive validation process in various laboratories within the KM3NeT Collaboration, a mass production batch began, resulting in the construction of over 1200 power boards so far. These boards were integrated in the digital optical modules that have already been produced and deployed, 828 until October 2023. In 2017, an upgrade of the power board, to increase reliability and efficiency, was initiated. After the validation of a pre-production series, a production batch of 800 upgraded boards is currently underway. This paper describes the design, architecture, upgrade, validation, and production of the power board, including the reliability studies and tests conducted to ensure the safe operation at the bottom of the Mediterranean Sea throughout the observatory's lifespa

Implementation and first results of the KM3NeT real-time core-collapse supernova neutrino search

The authors acknowledge the financial support of the funding agencies: Agence Nationale de la Recherche (contract ANR-15-CE31-0020), Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund and Marie Curie Program), Institut Universitaire de France (IUF), LabEx UnivEarthS (ANR-10-LABX-0023 and ANR-18-IDEX-0001), Paris ile-de-France Region, France; Shota Rustaveli National Science Foundation of Georgia (SRNSFG, FR-18-1268), Georgia; Deutsche Forschungsgemeinschaft (DFG), Germany; The General Secretariat of Research and Technology (GSRT), Greece; Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Universita e della Ricerca (MIUR), PRIN 2017 program (Grant NAT-NET 2017W4HA7S) Italy; Ministry of Higher Education Scientific Research and Professional Training, ICTP through Grant AF-13, Morocco; Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; The National Science Centre, Poland (2015/18/E/ST2/00758); National Authority for Scientific Research (ANCS), Romania; Ministerio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento (refs. PGC2018-096663-B-C41, -A-C42, -B-C43, -B-C44) (MCIU/FEDER), Generalitat Valenciana: Prometeo (PROMETEO/2020/019), Grisolia (ref. GRISOLIA/2018/119) and GenT (refs. CIDEGENT/2018/034, /2019/043, /2020/049) programs, Junta de Andalucia (ref. A-FQM-053-UGR18), La Caixa Foundation (ref. LCF/BQ/IN17/11620019), EU: MSC program (ref. 101025085), Spain.The KM3NeT research infrastructure is unconstruction in the Mediterranean Sea. KM3NeT will study atmospheric and astrophysical neutrinos with two multipurpose neutrino detectors, ARCA and ORCA, primarily aimed at GeV–PeV neutrinos. Thanks to the multiphotomultiplier tube design of the digital optical modules, KM3NeT is capable of detecting the neutrino burst from a Galactic or near-Galactic core-collapse supernova. This potential is already exploitable with the first detection units deployed in the sea. This paper describes the real-time implementation of the supernova neutrino search, operating on the two KM3NeT detectors since the first months of 2019. A quasi-online astronomy analysis is introduced to study the time profile of the detected neutrinos for especially significant events. Themechanism of generation and distribution of alerts, aswell as the integration into theSNEWSandSNEWS 2.0 global alert systems, are described. The approach for the follow-up of external alerts with a search for a neutrino excess in the archival data is defined. Finally, an overviewof the current detector capabilities and a report after the first two years of operation are given.French National Research Agency (ANR)European Commission ANR-15-CE31-0020Centre National de la Recherche Scientifique (CNRS)Commission EuropeenneInstitut Universitaire de France (IUF)LabEx UnivEarthS ANR-10-LABX-0023 ANR-18-IDEX-0001Shota Rustaveli National Science Foundation of Georgia (SRNSFG), Georgia FR-18-1268German Research Foundation (DFG)Greek Ministry of Development-GSRTIstituto Nazionale di Fisica Nucleare (INFN)Ministry of Education, Universities and Research (MIUR)PRIN 2017 program, Italy NAT-NET 2017W4HA7SMinistry of Higher Education Scientific Research and Professional Training, ICTP, Morocco AF-13Netherlands Organization for Scientific Research (NWO) Netherlands GovernmentNational Science Centre, Poland 2015/18/E/ST2/00758National Authority for Scientific Research (ANCS), RomaniaMinisterio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento PGC2018-096663-B-C41 PGC2018-096663-A-C42 PGC2018-096663-B-C43 PGC2018-096663-B-C44Generalitat Valenciana PROMETEO/2020/019Grisolia program GRISOLIA/2018/119 CIDEGENT/2018/034Junta de Andalucia A-FQM-053-UGR18La Caixa Foundation LCF/BQ/IN17/11620019EU: MSC program 101025085Paris Ile-de-France Region, FranceGenT program CIDEGENT/2018/034 CIDEGENT/2019/043 CIDEGENT/2020/04

Embedded Software of the KM3NeT Central Logic Board

The KM3NeT Collaboration is building and operating two deep sea neutrino telescopes at the bottom of the Mediterranean Sea. The telescopes consist of latices of photomultiplier tubes housed in pressure-resistant glass spheres, called digital optical modules and arranged in vertical detection units. The two main scientific goals are the determination of the neutrino mass ordering and the discovery and observation of high-energy neutrino sources in the Universe. Neutrinos are detected via the Cherenkov light, which is induced by charged particles originated in neutrino interactions. The photomultiplier tubes convert the Cherenkov light into electrical signals that are acquired and timestamped by the acquisition electronics. Each optical module houses the acquisition electronics for collecting and timestamping the photomultiplier signals with one nanosecond accuracy. Once finished, the two telescopes will have installed more than six thousand optical acquisition nodes, completing one of the more complex networks in the world in terms of operation and synchronization. The embedded software running in the acquisition nodes has been designed to provide a framework that will operate with different hardware versions and functionalities. The hardware will not be accessible once in operation, which complicates the embedded software architecture. The embedded software provides a set of tools to facilitate remote manageability of the deployed hardware, including safe reconfiguration of the firmware. This paper presents the architecture and the techniques, methods and implementation of the embedded software running in the acquisition nodes of the KM3NeT neutrino telescopes