Let's agree to disagree: learning highly debatable multirater labelling

Classification and differentiation of small pathological objects may greatly vary among human raters due to differences in training, expertise and their consistency over time. In a radiological setting, objects commonly have high within-class appearance variability whilst sharing certain characteristics across different classes, making their distinction even more difficult. As an example, markers of cerebral small vessel disease, such as enlarged perivascular spaces (EPVS) and lacunes, can be very varied in their appearance while exhibiting high inter-class similarity, making this task highly challenging for human raters. In this work, we investigate joint models of individual rater behaviour and multirater consensus in a deep learning setting, and apply it to a brain lesion object-detection task. Results show that jointly modelling both individual and consensus estimates leads to significant improvements in performance when compared to directly predicting consensus labels, while also allowing the characterization of human-rater consistency.Comment: Accepted at MICCAI 201

Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1-EPVS, 9 for Task 2-Microbleeds and 6 for Task 3-Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1-EPVS and Task 2-Microbleeds and not practically useful results yet for Task 3-Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level

Production of He-4 and (4) in Pb-Pb collisions at root(NN)-N-S=2.76 TeV at the LHC

Results on the production of He-4 and (4) nuclei in Pb-Pb collisions at root(NN)-N-S = 2.76 TeV in the rapidity range vertical bar y vertical bar <1, using the ALICE detector, are presented in this paper. The rapidity densities corresponding to 0-10% central events are found to be dN/dy4(He) = (0.8 +/- 0.4 (stat) +/- 0.3 (syst)) x 10(-6) and dN/dy4 = (1.1 +/- 0.4 (stat) +/- 0.2 (syst)) x 10(-6), respectively. This is in agreement with the statistical thermal model expectation assuming the same chemical freeze-out temperature (T-chem = 156 MeV) as for light hadrons. The measured ratio of (4)/He-4 is 1.4 +/- 0.8 (stat) +/- 0.5 (syst). (C) 2018 Published by Elsevier B.V.Peer reviewe

Radiolabeled leucocyte imaging in diffuse granulomatous involvement of the meninges in Wegener's granulomatosis:scintigraphic findings and their role in monitoring treatment response to specific immunotherapy (humanized monoclonal antilymphocyte antibodies)

Diffuse involvement of the meninges by remote granulomas in Wegener's granulomatosis is rare. This study reports the radiolabeled leucocyte imaging findings in five such patients. The diagnosis was made by MR imaging in five patients and confirmed in four by findings at meningeal biopsy. The potential role of serial radiolabeled leucocyte examinations in assessing treatment response is discussed

Update on the Clinical, Radiographic, and Neurobehavioral Manifestations in FXTAS and FMR1 Premutation Carriers

Fragile X-associated tremor/ataxia syndrome (FXTAS) is a progressive neurodegenerative disorder caused by a repeat expansion in the fragile X mental retardation 1 (FMR1) gene. The disorder is characterized by kinetic tremor and cerebellar ataxia, shows age-dependent penetrance, and occurs more frequently in men. This paper summarizes the key emerging issues in FXTAS as presented at the Second International Conference on the FMR1 Premutation: Basic Mechanisms &amp; Clinical Involvement in 2015. The topics discussed include phenotype-genotype relationships, neurobehavioral function, and updates on FXTAS genetics and imaging

Training dataset for the VALDO 2021 challenge - Vascular Lesion Detection

VALDO Challenge 2021 Training dataset This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. This dataset corresponds to the training data of the VALDO Challenge 2021 - For more information about the challenge and to participate, please see website In order to register a team, remember to fill in the registration form The challenge is separated in 3 tasks Task 1 - Perivascular spaces segmentation Task 2 - Cerebral micro bleeds segmentation Task 3 - Lacunes segmentation Details of the contents and organisation of the data is described in the README.md file Data reference and acknowledgement When using this data, we kindly ask for the following funding sources to be acknowledged: Wellcome Trust (082464/Z/07/Z), British Heart Foundation (SP/07/001/23603, PG/08/103, PG/12/29/29497 and CS/13/1/30327), Erasmus MC University Medical Center, the Erasmus University Rotterdam, the Netherlands Organization for Scientific Research (NWO) Grant 918-46-615, the Netherlands Organization for Health Research and Development (ZonMW), the Research Institute for Disease in the Elderly (RIDE), and the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement No. 601055, VPHDARE@IT, the Dutch Technology Foundation STW For all publications, please add references noted below in the reference section When using data from Task 2, please add "for the ALFA Study" as corporate author in your publication and indicate the following list of contributors: Müge Akinci, Annabella Beteta, Raffaele Cacciaglia, Alba Cañas, Irene Cumplido, Carme Deulofeu, Ruth Dominguez, Maria Emilio, Carles Falcón, Karine Fauria, Sherezade Fuentes, Juan Domingo Gispert, Oriol Grau-Rivera, José M. González-de-Echávarri, Laura Hernandez, Gema Huesa, Jordi Huguet, Iva Knezevic, Eider M. Arenaza-Urquijo, Eva M Palacios, Paula Marne, Tania Menchón, Marta Milà-Alomà, Carolina Minguillon, José Luis Molinuevo, Grégory Operto, Albina Polo, Gemma Salvadó, Sandra Pradas, Blanca Rodríguez, Aleix Sala-Vila, Gonzalo Sánchez-Benavides, Mahnaz Shekari, Anna Soteras, Marc Suárez-Calvet, Laura Stankeviciute, Marc Vilanova and Natalia Vilor-Tejedor

3D multirater RCNN for multimodal multiclass detection and characterisation of extremely small objects

Extremely small objects (ESO) have become observable on clinical routine magnetic resonance imaging acquisitions, thanks to a reduction in acquisition time at higher resolution. Despite their small size (usually <10 voxels per object for an image of more than 106 voxels), these markers reflect tissue damage and need to be accounted for to investigate the complete phenotype of complex pathological pathways. In addition to their very small size, variability in shape and appearance leads to high labelling variability across human raters, resulting in a very noisy gold standard. Such objects are notably present in the context of cerebral small vessel disease where enlarged perivascular spaces and lacunes, commonly observed in the ageing population, are thought to be associated with acceleration of cognitive decline and risk of dementia onset. In this work, we redesign the RCNN model to scale to 3D data, and to jointly detect and characterise these important markers of age-related neurovascular changes. We also propose training strategies enforcing the detection of extremely small objects, ensuring a tractable and stable training process