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GBM-Reservoir: Brain tumor (Glioblastoma Multiforme) MRI dataset collection with ground truth segmentation masks

DATA IN BRIEF 2025年 58卷 111287页

作者： Solak, Naida Ferreira, Andre Luijten, Gijs Puladi, Behrus Alves, Victor Egger, Jan Univ Hosp Essen UKE Inst AI Med IKIM Essen Germany Graz Univ Technol TU Graz Graz Styria Austria Comp Algorithms Med Lab Cafe Lab Graz Styria Austria Univ Med Essen Ctr Virtual & Extended Real Med Essen Germany Univ Minho Ctr Algoritmi LASI Braga Portugal Univ Hosp RWTH Aachen Inst Med Informat Aachen Germany Univ Hosp RWTH Aachen Dept Oral & Maxillofacial Surg Aachen Germany

In this article, we present a brain tumor database collection comprising 23,049 samples, with each sample including four different types of MRI brain scans: FLAIR, T1, T1ce, and T2. Additionally, one or two segmentation masks (ground truth) are provided for each sample. The first mask is the raw output from the registration process and is provided for all samples, while the second mask, provided particularly for synthetic samples, is a post-processed version of the first, designed to simplify interpretation and optimize it for network training. These samples have been acquired via registration process of 438 samples available at the moment of registration from the original dataset provided by the BraTS 2022 Challenge. Registering each pair of existing brain scans results in two additional scans that retain a similar brain shape while featuring varying tumor locations. Consequently, by registering all possible pairs, a dataset originally consisting of n samples can be expanded to n2 samples. The original dataset was collected from different institutions under standard clinical conditions, but with different equipment and imaging protocols. As a result, the image quality is hetero- geneous, reflecting the diversity of clinical practices across institutions. This dataset can be utilized for various tasks, such as developing fully automated segmentation algorithms for new, unseen brain tumor cases, particularly through deep learning-based approaches, since ground truth is provided for each sample. (c) 2025 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC license (http://***/licenses/by-nc/4.0/)

关键词： Brain tumor segmentation Data augmentation Registration BraTS Deep learning

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medShapeNet - a large-scale dataset of 3D medical shapes for computer vision

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BIOmedICAL ENGINEERING-BIOmedIZINISCHE TECHNIK 2025年第1期70卷 71-90页

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Viet Duc Vu Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Maechler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Kuestner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Loeffler, Maximilian T. Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nuernberger, Andreas Pedrosa, Joo Ferreira, Carlos Aresta, Guilherme Cunha, Antonio Campilho, Aurelio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Roehrig, Rainer Hoelzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, Andre Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hoerst, Fabian Brehmer, Alexan Univ Hosp Essen AoR Inst Artificial Intelligence Med IKIM Girardetstr 2 D-45131 Essen Germany Graz Univ Technol Inst Comp Graph & Vis ICG Inffeldgasse 16c A-8010 Graz Austria Comp Algorithms Med Lab Cafe Graz Austria Univ Med Essen AoR Canc Res Ctr Cologne Essen CCCE Hufelandstr 55 D-45147 Essen Germany Univ Med Essen Univ Hosp Essen Ctr Virtual & Extended Real Med ZvRM Hufelandstr 55 D-45147 Essen Germany Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Swiss Fed Inst Technol Lausanne EPFL Comp Vis Lab Lausanne Switzerland Univ Hosp RWTH Aachen Dept Oral & Maxillofacial Surg Aachen Germany Shanghai Jiao Tong Univ Renji Hosp Sch Med Dept Radiol Shanghai Peoples R China AGH Univ Sci & Technol Dept Measurement & Elect Krakow Poland Univ Appl Sci Western Switzerland HES SO Valais Inst Informat Syst Sierre Switzerland Univ Basel Dept Biomed Engn Ctr Med Image Anal & Nav CIAN Allschwil Switzerland Univ Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA ZhejiangLab Res Ctr Connected Healthcare Big Data Hangzhou Zhejiang Peoples R China Graz Univ Technol Inst Mech Graz Austria Justus Liebig Univ Giessen Univ Hosp Giessen Dept Diagnost & Intervent Radiol Giessen Germany Mehran Univ Engn & Technol Dept Mech Engn Jamshoro Sindh Pakistan Shanghai Jiao Tong Univ Inst Med Robot Shanghai Peoples R China Univ Hosp Canadian Surg Technol & Adv Robot CSTAR London ON Canada Shanghai Jiao Tong Univ Inst Biomed Mfg & Life Qual Engn Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai Peoples R China Med Univ Graz Dept Cardiac Surg Graz Austria Techn Univ Munchen Univ Klinikum Geschaftsfuhrender Oberarzt Abt Intervent & Diagn Munich Germany Icometrix Leuven Belgium Tech Univ Munich Dept Informat Garching Germany Univ Zurich Dept Quantitat Biomed Zurich Switzerland Tech Univ Dortmund Fac Stat Dortmund Germany Shanghai Jiao Tong Univ Inst Med Robot Shanghai Peoples R China Univ Techn Univ Munchen Geschaftsfuhrender Oberarzt Abt Interv

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models). However, a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instruments is missing. Methods: We present medShapeNet to translate data-driven vision algorithms to medical applications and to adapt state-of-the-art vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. We present use cases in classifying brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. Results: By now, medShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Conclusions: medShapeNet contains medical shapes from anatomy and surgical instruments and will continue to collect data for benchmarks and applications. The project page is: https://***/.

关键词： 3D medical shapes benchmark anatomy education shapeomics augmented reality virtual reality

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The EU project Real4Reg: unlocking real-world data with AI

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HEALTH RESEARCH POLICY AND SYSTEMS 2025年第1期23卷 1-8页

作者： Peltner, Jonas Becker, Cornelia Wicherski, Julia Wortberg, Silja Aborageh, Mohamed Costa, Ines Ehrenstein, Vera Fernandes, Joana Hess, Steffen Horvath-Puho, Erzsebet Korcinska Handest, Monika Roberta Lentzen, Manuel Maguire, Peggy Meedom, Niels Henrik Moore, Rebecca Moore, Vanessa Nagy, David Mcnamara, Hillary Paakinaho, Anne Pfeifer, Kerstin Pylkkanen, Liisa Rajamaki, Blair Reviers, Evy Roethlein, Christoph Russek, Martin Silva, Celia De Valck, Dirk Vo, Thuan Brauner, Elvira Froehlich, Holger Furtado, Claudia Hartikainen, Sirpa Kallio, Aleksi Tolppanen, Anna-Maija Haenisch, Britta German Ctr Neurodegenerat Dis DZNE Bonn Germany Fed Inst Drugs & Med Devices Div Res Kurt Georg Kiesinger Allee 3 D-53175 Bonn Germany Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat D-53754 St Augustin Sankt Augustin Germany Natl Author Med & Hlth Prod IP Hlth Technol Assessment Dept DATS INFARMED Lisbon Portugal Aarhus Univ Dept Clin Med Dept Clin Epidemiol Aarhus Denmark Aarhus Univ Ctr Populat Med Aarhus Denmark Natl Author Med & Hlth Prod IP Informat & Strateg Planning Dept DIPE INFARMED P-1749004 Lisbon Portugal Fed Inst Drugs & Med Devices Hlth Data Lab FDZ D-53175 Bonn Germany Data Analyt Ctr Danish Med Agcy Copenhagen Denmark European Inst Womens Hlth EIWH Dublin Ireland Univ Eastern Finland Sch Pharm Kuopio Finland Univ Turku Dept Oncol Turku Finland Univ Louvain Louvain Belgium Univ Bonn Bonn Aachen Int Ctr IT Bonn Germany NOVA Univ Lisbon NOVA Natl Sch Publ Hlth Lisbon Portugal CSC IT Ctr Sci Ltd Espoo Finland Univ Bonn Med Fac Ctr Translat Med Bonn Germany

BackgroundThe use of real-world data is established in post-authorization regulatory processes such as pharmacovigilance of drugs and medical devices, but is still frequently challenged in the pre-authorization phase of medicinal products. In addition, the use of real-world data, even in post-authorization steps, is constrained by the availability and heterogeneity of real-world data and by challenges in analysing data from different settings and sources. Moreover, there are emerging opportunities in the use of artificial intelligence in healthcare research, but also a lack of knowledge on its appropriate application to heterogeneous real-world data sources to increase evidentiary value in the regulatory decision-making and health technology assessment *** Real4Reg project aims to enable the use of real-world data by developing user-friendly solutions for the data analytical needs of health regulatory and health technology assessment bodies across the European Union. These include artificial intelligence algorithms for the effective analysis of real-world data in regulatory decision-making and health technology assessment. The project aims to investigate the value of real-world data from different sources to generate high-quality, accessible, population-based information relevant along the product life cycle. A total of four use cases are used to provide good practice examples for analyses of real-world data for the evaluation and pre-authorization stage, the improvement of methods for external validity in observational data, for post-authorization safety studies and comparative effectiveness using real-world data. This position paper introduces the objectives and structure of the Real4Reg project and discusses its important role in the context of existing European projects focussing on real-world ***4Reg focusses on the identification and description of benefits and risks of new and optimized methods in real-world data analysis includ

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computational drug repurposing: approaches, evaluation of in silico resources and case studies

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NATURE REVIEWS DRUG DISCOVERY 2025年 1-22页

作者： Tanoli, Ziaurrehman Fernandez-Torras, Adria Ozcan, Umut Onur Kushnir, Aleksandr Nader, Kristen Michelle Gadiya, Yojana Fiorenza, Laura Ianevski, Aleksandr Vaha-Koskela, Markus Miihkinen, Mitro Seemab, Umair Leinonen, Henri Seashore-Ludlow, Brinton Tampere, Marianna Kalman, Adelinn Ballante, Flavio Benfenati, Emilio Saunders, Gary Potdar, Swapnil Garcia, Ismael Gomez Garcia-Serna, Ricard Talarico, Carmine Beccari, Andrea Rosario Schaal, Wesley Polo, Andrea Costantini, Susan Cabri, Enrico Jacobs, Marc Saarela, Jani Budillon, Alfredo Spjuth, Ola Ostling, Paivi Xhaard, Henri Quintana, Jordi Mestres, Jordi Gribbon, Philip Ussi, Anton E. Lo, Donald C. de Kort, Martin Wennerberg, Krister Fratelli, Maddalena Carreras-Puigvert, Jordi Aittokallio, Tero Univ Helsinki Inst Mol Med Finland FIMM HiLIFE Helsinki Finland Univ Helsinki ICAN Digital Precis Canc Med Flagship Helsinki Finland Helsinki Univ Hosp Helsinki Finland Bioctr Finland Drug Discovery & Chem Biol DDCB Consortium Helsinki Finland Chemotargets SL Parc Cientif Barcelona Barcelona 08028 Catalonia Spain Fraunhofer Inst Translat Med & Pharmacol ITMP D-22525 Hamburg Germany Fraunhofer Frankfurt Germany Univ Bonn Bonn Aachen Int Ctr Informat Technol B IT Bonn Germany Politecn Milan Dipartimento Elettron Informaz & Bioingn DEIB I-20133 Milan Italy IRCCS Ist Ric Farmacolog Mario Negri Milan Italy Univ Eastern Finland Fac Hlth Sci Sch Pharm Kuopio Finland Karolinska Inst Dept Oncol Pathol Sci Life Lab SciLifeLab Stockholm Sweden Karolinska Inst Chem Biol Consortium Sweden CBCS Dept Med Biochem & Biophys Stockholm Sweden European Infrastruct Translat Med EATRIS ERIC NL-1081 HZ Amsterdam Netherlands Dompe Farmaceut SpA EXSCALATE Naples Italy Uppsala Univ Dept Pharmaceut Biosci Uppsala Sweden Uppsala Univ Sci Life Lab Uppsala Sweden IRCCS Fdn G Pascale Ist Nazl Tumori Naples Italy Fraunhofer Inst Algorithms & Sci Comp SCAI D-53754 St Augustin Augustin Germany Univ Helsinki Fac Pharm Helsinki Finland Univ Girona Inst Quim Computac & Catalisi Girona 17071 Catalonia Spain Univ Copenhagen Biotech Res & Innovat Ctr Copenhagen Denmark Oslo Univ Hosp Inst Canc Res Dept Canc Genet Oslo Norway Univ Oslo Fac Med Oslo Ctr Biostat & Epidemiol OCBE Oslo Norway

Repurposing of existing drugs for new indications has attracted substantial attention owing to its potential to accelerate drug development and reduce costs. Hundreds of computational resources such as databases and predictive platforms have been developed that can be applied for drug repurposing, making it challenging to select the right resource for a specific drug repurposing project. With the aim of helping to address this challenge, here we overview computational approaches to drug repurposing based on a comprehensive survey of available in silico resources using a purpose-built drug repurposing ontology that classifies the resources into hierarchical categories and provides application-specific information. We also present an expert evaluation of selected resources and three drug repurposing case studies implemented within the Horizon Europe REmedi4ALL project to demonstrate the practical use of the resources. This comprehensive Review with expert evaluations and case studies provides guidelines and recommendations on the best use of various in silico resources for drug repurposing and establishes a basis for a sustainable and extendable drug repurposing web catalogue.

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RADAR-AD: assessment of multiple remote monitoring technologies for early detection of Alzheimer's disease

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ALZHEIMERS RESEARCH & THERAPY 2025年第1期17卷 1-17页

作者： Lentzen, Manuel Vairavan, Srinivasan Muurling, Marijn Alepopoulos, Vasilis Atreya, Alankar Boada, Merce de Boer, Casper Conde, Pauline Curcic, Jelena Frisoni, Giovanni Galluzzi, Samantha Gjestsen, Martha Therese Gkioka, Mara Grammatikopoulou, Margarita Hausner, Lucrezia Hinds, Chris Lazarou, Ioulietta de Mendonca, Alexandre Nikolopoulos, Spiros Religa, Dorota Scebba, Gaetano Visser, Pieter Jelle Wittenberg, Gayle Narayan, Vaibhav A. Coello, Neva Brem, Anna-Katharine Aarsland, Dag Froehlich, Holger RADAR AD Fraunhofer Inst Algorithms & Sci Comp SCAI St Augustin Germany Univ Bonn Bonn Aachen Int Ctr IT B It Bonn Germany Janssen Res & Dev LLC Titusville NJ USA Vrije Univ Amsterdam Alzheimer Ctr Amsterdam Neurol Amsterdam UMC Locat VUmc Amsterdam Netherlands Amsterdam Neurosci Neurodegenerat Amsterdam Netherlands Informat Technol Inst Ctr Res & Technol Hellas Thessaloniki Greece Univ Oxford Oxford England Univ Int Catalunya Ace Alzheimer Ctr Barcelona Barcelona Spain Inst Salud Carlos III Networking Res Ctr Neurodegenerat Dis CIBERNED Seville Spain Kings Coll London London England Novartis Biomed Res Basel Switzerland Univ Geneva Memory Ctr Geneva Switzerland Univ Hosp Geneva Switzerland IRCCS Ist Ctr San Giovanni Dio Fatebenefratelli Lab Alzheimers Neuroimaging & Epidemiol Brescia Italy Stavanger Univ Hosp Ctr Age Related Med Stavanger Norway Univ Bergen Dept Clin Med Bergen Norway Aristotle Univ Thessaloniki Alzheimer Hellas Thessaloniki Greece Aristotle Univ Thessaloniki Lab Neurodegenerat Dis Thessaloniki Greece Heidelberg Univ Cent Inst Mental Hlth Med Fac Mannheim Mannheim Germany Univ Lisbon Fac Med Lisbon Portugal Karolinska Inst Ctr Alzheimer Res Dept Neurobiol Care Sci & Soc NVS Stockholm Sweden Davos Alzheimers Collaborat Geneva Switzerland Univ Bern Univ Hosp Old Age Psychiat Bern Switzerland Stavanger Univ Hosp Stavanger Norway Maastricht Univ Sch Mental Hlth & Neurosci Dept Psychiat & Neuropsychol Maastricht Netherlands

BackgroundAlzheimer's disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide, leading to cognitive and functional decline. Early detection and intervention are crucial for enhancing the quality of life of patients and their families. Remote Monitoring Technologies (RMTs) offer a promising solution for early detection by tracking changes in behavioral and cognitive functions, such as memory, language, and problem-solving skills. Timely detection of these symptoms can facilitate early intervention, potentially slowing disease progression and enabling appropriate treatment and *** RADAR-AD study was designed to evaluate the accuracy and validity of multiple RMTs in detecting functional decline across various stages of AD in a real-world setting, compared to standard clinical rating scales. Our approach involved a univariate analysis using Analysis of Covariance (ANCOVA) to analyze individual features of six RMTs while adjusting for variables such as age, sex, years of education, clinical site, BMI and season. Additionally, we employed four machine learning classifiers - Logistic Regression, Decision Tree, Random Forest, and XGBoost - using a nested cross-validation approach to assess the discriminatory capabilities of the *** ANCOVA results indicated significant differences between healthy and AD subjects regarding reduced physical activity, less REM sleep, altered gait patterns, and decreased cognitive functioning. The machine-learning-based analysis demonstrated that RMT-based models could identify subjects in the prodromal stage with an Area Under the ROC Curve of 73.0 %. In addition, our findings show that the Amsterdam iADL questionnaire has high discriminatory *** show promise in AD detection already in the prodromal stage. Using them could allow for earlier detection and intervention, thereby improving patients' quality of life. Furthermore, the Amsterdam iADL questionnaire holds high p

关键词： Alzheimer's disease Remote monitoring technologies Wearables Mobile applications Discriminative capacity

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Learning Volumetric Shape Super-Resolution for Cranial Implant Design 1

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1st AutoImplant Cranial Implant Design Challenge (AICIDC) at the medical Image computing and computer Assisted Interventions Conference (MICCAI)

作者： Eder, Matthias Li, Jianning Egger, Jan Graz Univ Technol Inst Comp Graph & Vis Graz Austria Comp Algorithms Med Lab Cafe Lab Graz Austria Med Univ Graz Dept Oral & Maxillofacial Surg Graz Austria

ISBN: (数字)9783030643270

ISBN: (纸本)9783030643263;9783030643270

Cranioplasty is the process of repairing cranial defects or deformations, which may be the result of injuries or necessary medical treatments such as brain tumor surgery. For this procedure, it is necessary to generate a high-quality cranial implant, which needs to be shaped individually for each skull and each defect. This tends to be a very time consuming task and requires also in-depth knowledge of various CAM/CAD programs. In this work, we present a novel automatic three-stage implant generation pipeline. First, skull completion is conducted in low resolution using a trained artificial neural network (ANN). Second, the completed low-resolution skull is sent to a super-resolution network, which up-samples the low-resolution skull to higher resolution while, at the same time, filling the skull surface with geometric details. Finally, by simple subtraction and blob filtering, the desired high-resolution implant is generated.

关键词： Shape completion Super-resolution Cranioplasty Deep learning

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High-Resolution Cranial Implant Prediction via Patch-Wise Training 1

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1st AutoImplant Cranial Implant Design Challenge (AICIDC) at the medical Image computing and computer Assisted Interventions Conference (MICCAI)

作者： Jin, Yuan Li, Jianning Egger, Jan Graz Univ Technol Inst Comp Graph & Vis Graz Austria Comp Algorithms Med Lab Cafe Lab Graz Austria Med Univ Graz Dept Oral & Maxillofacial Surg Graz Austria

ISBN: (数字)9783030643270

ISBN: (纸本)9783030643263;9783030643270

In this study, we proposed two methods for AutoImplant (https://***/) - the cranial implant design challenge. The shape of the implant is predicted based on the inputted defective skull. This task can be accomplished either by directly predicting the implant with the defective skull, or indirectly rebuilding the complete skull and then taking the difference between the defective and complete skulls. In our work, a deep learning model is applied to automatically predict the implant. In order to solve the problem that high resolution images can often not be directly inputted to the deep learning model, two proposed methods of resize and patch-based are examined. On the test set, the proposed resize method achieves an average dice similarity score (DSC) of 0.7350 and a Hausdorff distance (HD) of 7.2425 mm, while the proposed patch-based method achieves an average DSC of 0.8887 and a HD of 5.5339 mm.

关键词： Shape completion Super-resolution Cranioplasty Deep learning AutoImplant

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Single-Shot Deep Volumetric Regression for Mobile medical Augmented Reality 10th

Single-Shot Deep Volumetric Regression for Mobile Medical Au...

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10th International Workshop on Multimodal Learning for Clinical Decision Support.(ML-CDS) / 9th International Workshop on Clinical Image-based Procedures - From Planning to Intervention (CLIP)

作者： Karner, Florian Gsaxner, Christina Pepe, Antonio Li, Jianning Fleck, Philipp Arth, Clemens Wallner, Jurgen Egger, Jan Graz Univ Technol Inst Comp Graph & Vis Graz Austria Comp Algorithms Med Lab Cafe Lab Graz Austria Med Univ Graz Dept Oral & Maxillofacial Surg Graz Austria

ISBN: (纸本)9783030609450;9783030609467

Augmented reality for medical applications allows physicians to obtain an inside view into the patient without surgery. In this context, we present an augmented reality application running on a standard smartphone or tablet computer, providing visualizations of medical image data, overlaid with the patient, in a video see-through fashion. Our system is based on the registration of medical imaging data to the patient using a single 2D photograph of the patient. From this image, a 3D model of the patient's face is reconstructed using a convolutional neural network, to which a pre-operative CT scan is automatically registered. For efficient processing, this is performed on a server PC. Finally, anatomical and pathological information is sent back to the mobile device and can be displayed, accurately registered with the live patient, on the screen. Hence, our cost-effective, markerless approach needs only a smartphone and a server PC for image processing. We present a qualitative and quantitative evaluation using real patient photos and CT from the clinical routine in facial surgery, reporting overall processing times and registration errors.

关键词： Augmented Reality 3D face reconstruction 3D registration Deep learning Volumetric Regression Network Handheld devices Smartphone Tablet

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Dataset Descriptor for the AutoImplant Cranial Implant Design Challenge 1

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1st AutoImplant Cranial Implant Design Challenge (AICIDC) at the medical Image computing and computer Assisted Interventions Conference (MICCAI)

作者： Li, Jianning Egger, Jan Graz Univ Technol Inst Comp Graph & Vis Graz Austria Comp Algorithms Med Lab Cafe Lab Graz Austria Med Univ Graz Dept Oral & Maxillofacial Surg Graz Austria

ISBN: (数字)9783030643270

ISBN: (纸本)9783030643263;9783030643270

This data descriptor elaborates on a dataset that can be used for the development of automatic, data-driven approaches for cranial implant design, which is a challenging task in cranioplasty. The dataset includes 210 complete skulls as well as their corresponding defective skulls and the implants, resulting in a total of 210 x 3 = 630 files in NRRD format. We split the dataset into a training set and a test set, each containing 100 and 110 completes skulls as well as the associated defective skulls and implants, respectively. The complete skulls are segmented from the public head computed tomography (CT) collection CQ500 (http://***/dataset), which is licensed under CC BY-NC-SA 4.0, using thresholding (Hounsfield units >= 150). On each complete skull, a synthetic defect, which resembles a real defect from craniotomy, is injected. In the test set, 100 skulls have similar defects to the training set, with respect to defect size, shape and position, while the last 10 skulls have distinct defects. The whole training set and the defective skulls in the test set are released to the participants of the MIC-CAI 2020 AutoImplant Challenge (https://***-challenge. org/). The ground truth of the test set, i.e., the complete skulls and the implants are kept private by the organizers for a single blind an objective evaluation of the participant's results.

关键词： AutoImplant dataset Descriptor Deep learning Cranial implant design

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Markerless Image-to-Face Registration for Untethered Augmented Reality in Head and Neck Surgery 22nd

Markerless Image-to-Face Registration for Untethered Augment...

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10th International Workshop on Machine Learning in medical Imaging (MLMI) / 22nd International Conference on medical Image computing and computer-Assisted Intervention (MICCAI)

作者： Gsaxner, Christina Pepe, Antonio Wallner, Juergen Schmalstieg, Dieter Egger, Jan Graz Univ Technol Inst Comp Graph & Vis Graz Austria Med Univ Graz Div Oral Maxillofacial Surg Graz Austria Comp Algorithms Med Lab Graz Austria

ISBN: (纸本)9783030322540;9783030322533

In the treatment of head and neck cancer, physicians can benefit from augmented reality in preparing and executing treatment. We present a system allowing a physician wearing an untethered augmented reality headset to see medical visualizations precisely overlaid onto the patient. Our main contribution is a strategy for markerless registration of 3D imaging to the patient's face. We use a neural network to detect the face using the headset's depth sensor and register it to computed tomography data. The face registration is seamlessly combined with the headset's continuous self-localization. We report on registration error and compare our approach to an external, high-precision tracking system.

关键词： Augmented reality 3D registration Head and neck cancer

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