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1. CURRENT CHALLENGES AND SOLUTION APPROACHES IN EMPIRICAL ENGINEERING DESIGN RESEARCH – A WORKSHOP FOR EMPIRICAL RESEARCH

Author

Dieter Krause, E. Garrelts, Selin Üreten, Matthias Eisenmann, Thomas Nelius, and Sven Matthiesen

Subjects
Research design, 0209 industrial biotechnology, business.industry, Computer science, Concept map, 05 social sciences, 02 engineering and technology, General Medicine, Automation, Practical guideline, Task (project management), Engineering management, 020901 industrial engineering & automation, Empirical research, 0502 economics and business, ddc:620, Design methods, business, Engineering design process, Engineering & allied operations, 050203 business & management
Abstract

The requirements on validity for studies in design research are very high. Therefore, this paper aims at identifying challenges that occur when setting up studies and suggests solution strategies to address them. Three different institutes combining their experience discussed several studies in a workshop. Resulting main challenges are to find a suitable task, to operationalise the variables and to deal with a high analysis effort per participant. Automation in data evaluation and a detailed practical guideline on studies in design research are considered necessary.

Published
2020
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2. Surgical data science – from concepts toward clinical translation

Author

Hubertus Feussner, Swaroop Vedula, Ozanan R. Meireles, Nicolas Padoy, Carla M. Pugh, Russell H. Taylor, Duygu Sarikaya, Bernard Gibaud, Jan Goedeke, Pietro Mascagni, Pierre Jannin, Hirenkumar Nakawala, Toby Collins, Beat P. Müller-Stich, Teodor P. Grantcharov, Stamatia Giannarou, Ines Gockel, Gregory D. Hager, Dogu Teber, Doreen Heckmann-Nötzel, Justin W. Collins, Matthias Eisenmann, Lena Maier-Hein, Sinan Onogur, Anand Malpani, Frank Ückert, Hani J. Marcus, Germain Forestier, Luc Joyeux, Daniel R. Leff, Keno März, Daniel A. Hashimoto, Johannes Fallert, Thomas Neumuth, Minu D. Tizabi, Tobias Roß, Makoto Hashizume, Lars Mündermann, Kevin Cleary, Raphael Sznitman, Kyle Lam, Nassir Navab, Alexander Seitel, Martin Wagner, Amin Madani, Stefanie Speidel, Ron Kikinis, Adrian Park, Danail Stoyanov, Gabor Fichtinger, Hannes Kenngott, German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Heidelberg University, Gazi University, Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Recherche Contre les Cancers de l'Appareil Digestif-European Institute of Telesurgery (IRCAD/EITS), Johns Hopkins University (JHU), La société Karl STORZ, Technische Universität München = Technical University of Munich (TUM), Imperial College London, Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), L'Institut hospitalo-universitaire de Strasbourg (IHU Strasbourg), Institut National de Recherche en Informatique et en Automatique (Inria)-l'Institut de Recherche contre les Cancers de l'Appareil Digestif (IRCAD)-Les Hôpitaux Universitaires de Strasbourg (HUS)-La Fédération des Crédits Mutuels Centre Est (FCMCE)-L'Association pour la Recherche contre le Cancer (ARC)-La société Karl STORZ, Università degli studi di Verona = University of Verona (UNIVR), Johns Hopkins University School of Medicine [Baltimore], Stanford University School of Medicine [CA, USA], University College of London [London] (UCL), Sheikh Zayed Institute for Pediatric Surgical Innovation, Washington DC, Queen's University [Kingston, Canada], Institut de Recherche en Informatique Mathématiques Automatique Signal - IRIMAS - UR 7499 (IRIMAS), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), Monash university, University of Toronto, Kyushu University, Heidelberg University Hospital [Heidelberg], Harvard Medical School [Boston] (HMS), University of Bern, Universität Leipzig, University Hospital Leipzig, Ludwig-Maximilians-Universität München (LMU), Massachusetts General Hospital [Boston], Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Baylor College of Medicine (BCM), Baylor University, University Health Network, UCL Institute of Neurology, Queen Square [London], Universität Karlsruhe (TH), University Hospital Hamburg-Eppendorf, Universitaetsklinikum Hamburg-Eppendorf = University Medical Center Hamburg-Eppendorf [Hamburg] (UKE), National Center for Tumor Diseases [Dresden] (NCT), Technische Universität Dresden = Dresden University of Technology (TU Dresden)-German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ)-Helmholtz-Zentrum Dresden-Rossendorf (HZDR), This work was supported by the European Research Council (ERC) starting grant COMBIOSCOPY under the New Horizon Framework Programme grant agreement [ERC-2015-StG-637960], the Helmholtz Imaging Platform (HIP), a platform of the Helmholtz Incubator on Information and Data Science, the NCT Heidelberg, BPI France (project CONDOR), the Johns Hopkins Science of Learning Institute Research Grant, the National Institutes of Health [NIDCR R01 DE025265, P41 EB015902, P41 EB015898, R01 CA235589], the Surgical Oncology Program of the National Center for Tumor Diseases (NCT) Heidelberg, KARL STORZ SE & Co. KG, the Royal Society (UF140290) and NIHR Imperial BRC (Biomedical Research Centre), the ERC - H2020 Autonomous Robotic Surgery (ARS) grant agreement [ERC-2016-ADG-742671], the Surgical Metrics Project - American College of Surgeons (National Society Contract), the Quantified Physician - 7-SIGMA Simulation Systems, Minnesota, MN (Industry Contract), the Tourniquet Master Training - DOD SBIR Phase IIb - Continuation Award [W81XWH-13-C-0021], the Ontology for Human Motion and Psychomotor Performance - Stanford University Media-X, Motion Analysis for Microvascular Anastomosis - University of Wisconsin (Academic Contract), the Precision Learning Initiative - American Medical Association (National Society Grant), Quantifying the Metrics of Surgical Mastery: An Exploration in Data Science (NIH) [R01DK123445], the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) [203145Z/16/Z], Engineering and Physical Sciences Research Council (EPSRC) [EP/P027938/1, EP/R004080/1, EP/P012841/1], the Royal Academy of Engineering Chair in Emerging Technologies, the St. Michael’s Hospital, the University of Toronto, the Grant-in-Aid for Scientific Research on Innovative Area from MEXT, Japan, the National Cancer Data Ecosystem, contract number 19X037Q under Task Order HHSN26100071 from NCI, the project ProteCT [BMBF 16SV8568], the German Research Foundation (DFG, Deutsche Forschungsgemeinschaft) as part of Germany’s Excellence Strategy - EXC 2050/1 - Project ID 390696704 - Cluster of Excellence 'Centre for Tactile Internet with Human-in-the-Loop' (CeTI), the Canada Research Chair in Computer Integrated Surgery, Natural Sciences and Engineering Research Council of Canada, the ANR with grants ANR-16-CE33-0009 (project DeepSurg), ANR-10-IAHU-02 (IHU Strasbourg) and ANR-20-CHIA-0029-01 (Chair AI4ORSafety), and the Federal Ministry of Economics and Energy (BMWi) and the German Aerospace Center (DLR) within the OP 4.1 project [BMWI 01MT17001C]., ANR-16-CE33-0009,DeepSurg,Apprentissage Profond à partir de Vidéos Chirurgicales Multi-vues et Multimodales pour Faciliter la Gestion du Bloc Opératoire(2016), ANR-10-IAHU-0002,MIX-Surg,Institut de Chirurgie Mini-Invasive guidée par l'Image(2010), ANR-20-CHIA-0029,AI4ORSafety,Evaluation automatique des vues endoscopiques pour la validation des points de contrôle au bloc opératoire(2020), European Project: 637960,H2020,ERC-2014-STG,COMBIOSCOPY(2015), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace (FMNGE), Kyushu University [Fukuoka], Universität Leipzig [Leipzig], Institute of Neurology - UCL/Queen Square [London, UK] (IN-UCL-QS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), National Center for Tumor Diseases - Deutsches Krebsforschungszentrum [Heidelberg, Allemagne] (NCT / DKFZ), and Technische Universität Dresden = Dresden University of Technology (TU Dresden)

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial intelligence, Surgical data science, Computer science, Computer Vision and Pattern Recognition (cs.CV), media_common.quotation_subject, Aucun, Delphi method, Computer Science - Computer Vision and Pattern Recognition, Health Informatics, [SDV.MHEP.CHI]Life Sciences [q-bio]/Human health and pathology/Surgery, Field (computer science), Machine Learning (cs.LG), [SPI.AUTO]Engineering Sciences [physics]/Automatic, Machine Learning, 03 medical and health sciences, Computer Science - Computers and Society, 0302 clinical medicine, Computers and Society (cs.CY), Health care, FOS: Electrical engineering, electronic engineering, information engineering, Humans, Radiology, Nuclear Medicine and imaging, Quality (business), 030304 developmental biology, media_common, 0303 health sciences, Radiological and Ultrasound Technology, business.industry, Clinical translation, Image and Video Processing (eess.IV), Data Science, Deep learning, Electrical Engineering and Systems Science - Image and Video Processing, Computer aided surgery, Computer Graphics and Computer-Aided Design, Data science, 3. Good health, Current practice, 030220 oncology & carcinogenesis, Data analysis, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Computer Vision and Pattern Recognition, business, Data Annotation
Abstract

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.

Published
2022
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3. Common Pitfalls and Recommendations for Grand Challenges in Medical Artificial Intelligence

Author

Matthias Eisenmann, Lena Maier-Hein, Annika Reinke, and Minu D. Tizabi

Subjects
Biomedical Research, Point (typography), business.industry, Urology, Interpretation (philosophy), media_common.quotation_subject, 030232 urology & nephrology, Medical research, Domain (software engineering), 03 medical and health sciences, 0302 clinical medicine, Artificial Intelligence, 030220 oncology & carcinogenesis, Transparency (graphic), Medicine, Humans, Quality (business), Artificial intelligence, business, Algorithms, Grand Challenges, Interpretability, media_common
Abstract

With the impact of artificial intelligence (AI) algorithms on medical research on the rise, the importance of competitions for comparative validation of algorithms, so-called challenges, has been steadily increasing, to a point at which challenges can be considered major drivers of research, particularly in the biomedical image analysis domain. Given their importance, high quality, transparency, and interpretability of challenges is essential for good scientific practice and meaningful validation of AI algorithms, for instance towards clinical translation. This mini-review presents several issues related to the design, execution, and interpretation of challenges in the biomedical domain and provides best-practice recommendations. PATIENT SUMMARY: This paper presents recommendations on how to reliably compare the usefulness of new artificial intelligence methods for analysis of medical images.

Published
2021

4. Evaluating Electromagnetic Guidance for Fine Needle Aspiration in a Thyroid-like Ultrasound Phantom

Author

D Gröner, Amir Sabet, L Meier-Hein, Matthias Eisenmann, Alfred M. Franz, F Grünwald, C. T Nguyen, C Happel, Alexander Seitel, Bünyamin Pekdemir, P Sridharan, and L. Aguilera Saiz

Subjects
Fine-needle aspiration, medicine.anatomical_structure, medicine.diagnostic_test, business.industry, Thyroid, medicine, Nuclear medicine, business, Ultrasound phantom
Published
2020
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5. Large-scale medical image annotation with crowd-powered algorithms

Author

Alexander W. Sauter, Hannes Kenngott, Jasmin Metzger, Felix Wagner, Bram Stieltjes, Johannes Lebert, Matthias Eisenmann, Eric Heim, Alexander Seitel, Keno März, Tobias Roß, Gregor Sommer, Fides Regina Schwartz, Lena Maier-Hein, and Andreas Termer

Subjects
business.industry, Image Processing, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Image processing, Image segmentation, Crowdsourcing, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Annotation, 0302 clinical medicine, Automatic image annotation, Medical imaging, Medicine, Domain knowledge, Radiology, Nuclear Medicine and imaging, Segmentation, business, Algorithm, 030217 neurology & neurosurgery
Abstract

Accurate segmentations in medical images are the foundations for various clinical applications. Advances in machine learning-based techniques show great potential for automatic image segmentation, but these techniques usually require a huge amount of accurately annotated reference segmentations for training. The guiding hypothesis of this paper was that crowd-algorithm collaboration could evolve as a key technique in large-scale medical data annotation. As an initial step toward this goal, we evaluated the performance of untrained individuals to detect and correct errors made by three-dimensional (3-D) medical segmentation algorithms. To this end, we developed a multistage segmentation pipeline incorporating a hybrid crowd-algorithm 3-D segmentation algorithm integrated into a medical imaging platform. In a pilot study of liver segmentation using a publicly available dataset of computed tomography scans, we show that the crowd is able to detect and refine inaccurate organ contours with a quality similar to that of experts (engineers with domain knowledge, medical students, and radiologists). Although the crowds need significantly more time for the annotation of a slice, the annotation rate is extremely high. This could render crowdsourcing a key tool for cost-effective large-scale medical image annotation.

Published
2018

6. Surgical data science for next-generation interventions

Author

Michael Kranzfelder, Nicolai Schoch, Lena Maier-Hein, Gregory D. Hager, Germain Forestier, Stefanie Speidel, Matthias Eisenmann, Pierre Jannin, Makoto Hashizume, Hannes Kenngott, Stamatia Giannarou, Anand Malpani, Ron Kikinis, Hubertus Feussner, Darko Katic, Keno März, Thomas Neumuth, Martin Wagner, S. Swaroop Vedula, Nicolas Padoy, Adrian Park, Danail Stoyanov, Carla M. Pugh, Russell H. Taylor, Nassir Navab, and Publica

Subjects
Technology, Quality management, BIG DATA, Computer science, Process (engineering), OPERATING-ROOM, Biomedical Engineering, Psychological intervention, Medicine (miscellaneous), Bioengineering, DECISION-MAKING, Field (computer science), 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Engineering, FUTURE, INFORMATICS, KNOWLEDGE, Set (psychology), Engineering, Biomedical, Science & Technology, business.industry, SKILL, Data science, 3. Good health, Computer Science Applications, ONTOLOGY, Analytics, 030220 oncology & carcinogenesis, Scale (social sciences), Key (cryptography), business, RADIOMICS, Biotechnology, CARDIAC-SURGERY
Abstract

This paper introduces Surgical Data Science as an emerging scientific discipline. Key perspectives are based on discussions during an intensive two-day international interactive workshop that brought together leading researchers working in the related field of computer and robot assisted interventions. Our consensus opinion is that increasing access to large amounts of complex data, at scale, throughout the patient care process, complemented by advances in data science and machine learning techniques, has set the stage for a new generation of analytics that will support decision-making and quality improvement in interventional medicine. In this article, we provide a consensus definition for Surgical Data Science, identify associated challenges and opportunities and provide a roadmap for advancing the field.

Published
2017

7. Can Masses of Non-Experts Train Highly Accurate Image Classifiers?

Author

Hannes Kenngott, Daniel Kondermann, Sven Mersmann, Christian Stock, Stefanie Speidel, Lena Maier-Hein, Sebastian Bodenstedt, Matthias Eisenmann, and Alexandro Sanchez

Subjects
Annotation, Information retrieval, Market segmentation, business.industry, Computer science, Scalability, Context (language use), Online community, business, Crowdsourcing, Random forest, Outsourcing
Abstract

Machine learning algorithms are gaining increasing interest in the context of computer-assisted interventions. One of the bottlenecks so far, however, has been the availability of training data, typically generated by medical experts with very limited resources. Crowdsourcing is a new trend that is based on outsourcing cognitive tasks to many anonymous untrained individuals from an online community. In this work, we investigate the potential of crowdsourcing for segmenting medical instruments in endoscopic image data. Our study suggests that (1) segmentations computed from annotations of multiple anonymous non-experts are comparable to those made by medical experts and (2) training data generated by the crowd is of the same quality as that annotated by medical experts. Given the speed of annotation, scalability and low costs, this implies that the scientific community might no longer need to rely on experts to generate reference or training data for certain applications. To trigger further research in endoscopic image processing, the data used in this study will be made publicly available.

Published
2014
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8. A concept map for design method experiments in product development – A guideline for method developers

Author

Sven Matthiesen, Dieter Krause, Selin Üreten, Thomas Nelius, Matthias Eisenmann, and Shi Cao

Subjects
Test design, Computer science, Concept map, business.industry, New product development, Guideline, Design methods, Software engineering, business
Abstract

Due to the emergence of experiments in product development and yet still missing systematic adjustment of the generic experimental design procedure to test design methods. a facilitation is required. A concept map to depict the most important elements to consider when validating design methods in an experiment was developed systematically to guide method developers. Combining literature findings with the analysis of six cases in academia and an intensive exchange of researchers between different institutions resulted in the concept map. Future research potentials in the definition of variables. gaining objective results and managing the required tools are outlined. The concept map is available online for further feedback from the community.

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