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Abdel-Alim T, Kurniawan M, Mathijssen I, Dremmen M, Dirven C, Niessen W, Roshchupkin G, van Veelen ML. Sagittal Craniosynostosis: Comparing Surgical Techniques Using 3D Photogrammetry. Plast Reconstr Surg 2023; 152:675e-688e. [PMID: 36946583 PMCID: PMC10521803 DOI: 10.1097/prs.0000000000010441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 10/10/2022] [Indexed: 03/23/2023]
Abstract
BACKGROUND The aim of this study was to compare three surgical interventions for correction of sagittal synostosis-frontobiparietal remodeling (FBR), extended strip craniotomy (ESC), and spring-assisted correction (SAC)-based on three-dimensional (3D) photogrammetry and operation characteristics. METHODS Patients who were born between 1991 and 2019 and diagnosed with nonsyndromic sagittal synostosis who underwent FBR, ESC, or SAC and had at least one postoperative 3D photogrammetry image taken during one of six follow-up appointments until age 6 were considered for this study. Operative characteristics, postoperative complications, reinterventions, and presence of intracranial hypertension were collected. To assess cranial growth, orthogonal cranial slices and 3D photocephalometric measurements were extracted automatically and evaluated from 3D photogrammetry images. RESULTS A total of 322 postoperative 3D images from 218 patients were included. After correcting for age and sex, no significant differences were observed in 3D photocephalometric measurements. Mean cranial shapes suggested that postoperative growth and shape gradually normalized with higher occipitofrontal head circumference and intracranial volume values compared with normal values, regardless of type of surgery. Flattening of the vertex seems to persist after surgical correction. The authors' cranial 3D mesh processing tool has been made publicly available as a part of this study. CONCLUSIONS The findings suggest that until age 6, there are no significant differences among the FBR, ESC, and SAC in their ability to correct sagittal synostosis with regard to 3D photocephalometric measurements. Therefore, efforts should be made to ensure early diagnosis so that minimally invasive surgery is a viable treatment option. CLINICAL QUESTION/LEVEL OF EVIDENCE Therapeutic, III.
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Affiliation(s)
- Tareq Abdel-Alim
- From the Departments of Neurosurgery
- Radiology and Nuclear Medicine
| | | | | | | | | | | | | | - Marie-Lise van Veelen
- From the Departments of Neurosurgery
- the Pediatric Brain Center, Erasmus MC, University Medical Center
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2
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Abdel-Alim T, Tio P, Kurniawan M, Mathijssen I, Dirven C, Niessen W, Roshchupkin G, van Veelen ML. Reliability and Agreement of Automated Head Measurements From 3-Dimensional Photogrammetry in Young Children. J Craniofac Surg 2023; 34:1629-1634. [PMID: 37307495 PMCID: PMC10445626 DOI: 10.1097/scs.0000000000009448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 04/25/2023] [Indexed: 06/14/2023] Open
Abstract
This study aimed to assess the reliability and agreement of automated head measurements using 3-dimensional (3D) photogrammetry in young children. Specifically, the study evaluated the agreement between manual and automated occipitofrontal circumference (OFC) measurements (n = 264) obtained from 3D images of 188 patients diagnosed with sagittal synostosis using a novel automated method proposed in this study. In addition, the study aimed to determine the interrater and intrarater reliability of the automatically extracted OFC, cephalic index, and volume. The results of the study showed that the automated OFC measurements had an excellent agreement with manual measurements, with a very strong regression score ( R2 = 0.969) and a small mean difference of -0.1 cm (-0.2%). The limits of agreement ranged from -0.93 to 0.74 cm, falling within the reported limits of agreement for manual OFC measurements. High interrater and intrarater reliability of OFC, cephalic index, and volume measurements were also demonstrated. The proposed method for automated OFC measurements was found to be a reliable alternative to manual measurements, which may be particularly beneficial in young children who undergo 3D imaging in craniofacial centers as part of their treatment protocol and in research settings that require a reproducible and transparent pipeline for anthropometric measurements. The method has been incorporated into CraniumPy, an open-source tool for 3D image visualization, registration, and optimization, which is publicly available on GitHub ( https://github.com/T-AbdelAlim/CraniumPy ).
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Affiliation(s)
- Tareq Abdel-Alim
- Department of Neurosurgery, Erasmus University Medical Center
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center
| | - Pauline Tio
- Department of Plastic and Reconstructive Surgery, Erasmus University Medical Center, Rotterdam
| | - Melissa Kurniawan
- Department of Neurosurgery, Erasmus University Medical Center
- Department of Plastic and Reconstructive Surgery, Erasmus University Medical Center, Rotterdam
| | - Irene Mathijssen
- Department of Plastic and Reconstructive Surgery, Erasmus University Medical Center, Rotterdam
| | - Clemens Dirven
- Department of Neurosurgery, Erasmus University Medical Center
| | - Wiro Niessen
- Faculty of Medical Sciences, University Groningen, Groningen, The Netherlands
| | - Gennady Roshchupkin
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center
- Department of Epidemiology, Erasmus University Medical Center
| | - Marie-Lise van Veelen
- Department of Neurosurgery, Erasmus University Medical Center
- Child Brain Center, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands
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3
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Pizzini FB, Pesapane F, Niessen W, Geerts-Ossevoort L, Broeckx N. ESMRMB Round Table report on "Can Europe Lead in Machine Learning of MRI-Data?". MAGMA 2021; 33:217-219. [PMID: 31897906 DOI: 10.1007/s10334-019-00821-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Francesca B Pizzini
- Radiology, Department of Diagnostic and Public Health, Verona University, Verona, Italy.
| | - Filippo Pesapane
- Postgraduation School in Radiodiagnostics, Università Degli Studi di Milano, Milan, Italy
- IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Wiro Niessen
- Erasmus MC-University Medical Centre Rotterdam, Rotterdam, The Netherlands
- Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
| | | | - Nils Broeckx
- Dewallens and Partners Law Firm, Leuven, Belgium
- PR2 Research Group, Faculty of Law, University of Antwerp, Antwerp, Belgium
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4
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Luu HM, van Walsum T, Franklin D, Pham PC, Vu LD, Moelker A, Staring M, VanHoang X, Niessen W, Trung NL. Efficiently compressing 3D medical images for teleinterventions via CNNs and anisotropic diffusion. Med Phys 2021; 48:2877-2890. [PMID: 33656213 DOI: 10.1002/mp.14814] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 01/29/2021] [Accepted: 02/14/2021] [Indexed: 12/23/2022] Open
Abstract
PURPOSE Efficient compression of images while preserving image quality has the potential to be a major enabler of effective remote clinical diagnosis and treatment, since poor Internet connection conditions are often the primary constraint in such services. This paper presents a framework for organ-specific image compression for teleinterventions based on a deep learning approach and anisotropic diffusion filter. METHODS The proposed method, deep learning and anisotropic diffusion (DLAD), uses a convolutional neural network architecture to extract a probability map for the organ of interest; this probability map guides an anisotropic diffusion filter that smooths the image except at the location of the organ of interest. Subsequently, a compression method, such as BZ2 and HEVC-visually lossless, is applied to compress the image. We demonstrate the proposed method on three-dimensional (3D) CT images acquired for radio frequency ablation (RFA) of liver lesions. We quantitatively evaluate the proposed method on 151 CT images using peak-signal-to-noise ratio ( PSNR ), structural similarity ( SSIM ), and compression ratio ( CR ) metrics. Finally, we compare the assessments of two radiologists on the liver lesion detection and the liver lesion center annotation using 33 sets of the original images and the compressed images. RESULTS The results show that the method can significantly improve CR of most well-known compression methods. DLAD combined with HEVC-visually lossless achieves the highest average CR of 6.45, which is 36% higher than that of the original HEVC and outperforms other state-of-the-art lossless medical image compression methods. The means of PSNR and SSIM are 70 dB and 0.95, respectively. In addition, the compression effects do not statistically significantly affect the assessments of the radiologists on the liver lesion detection and the lesion center annotation. CONCLUSIONS We thus conclude that the method has a high potential to be applied in teleintervention applications.
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Affiliation(s)
- Ha Manh Luu
- AVITECH, University of Engineering and Technology, VNU, Hanoi, Vietnam.,Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands.,FET, University of Engineering and Technology, VNU, Hanoi, Vietnam
| | - Theo van Walsum
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Daniel Franklin
- School of Electrical and Data Engineering, University of Technology Sydney, Sydney, Australia
| | - Phuong Cam Pham
- Nuclear Medicine and Oncology Center, Bach Mai Hospital, Hanoi, Vietnam
| | - Luu Dang Vu
- Radiology Center, Bach Mai Hospital, Hanoi, Vietnam
| | - Adriaan Moelker
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Marius Staring
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Xiem VanHoang
- FET, University of Engineering and Technology, VNU, Hanoi, Vietnam
| | - Wiro Niessen
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Nguyen Linh Trung
- AVITECH, University of Engineering and Technology, VNU, Hanoi, Vietnam
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Dubost F, Bruijne MD, Nardin M, Dalca AV, Donahue KL, Giese AK, Etherton MR, Wu O, Groot MD, Niessen W, Vernooij M, Rost NS, Schirmer MD. Multi-atlas image registration of clinical data with automated quality assessment using ventricle segmentation. Med Image Anal 2020; 63:101698. [PMID: 32339896 PMCID: PMC7275913 DOI: 10.1016/j.media.2020.101698] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 12/03/2019] [Accepted: 04/06/2020] [Indexed: 02/08/2023]
Abstract
Registration is a core component of many imaging pipelines. In case of clinical scans, with lower resolution and sometimes substantial motion artifacts, registration can produce poor results. Visual assessment of registration quality in large clinical datasets is inefficient. In this work, we propose to automatically assess the quality of registration to an atlas in clinical FLAIR MRI scans of the brain. The method consists of automatically segmenting the ventricles of a given scan using a neural network, and comparing the segmentation to the atlas ventricles propagated to image space. We used the proposed method to improve clinical image registration to a general atlas by computing multiple registrations - one directly to the general atlas and others via different age-specific atlases - and then selecting the registration that yielded the highest ventricle overlap. Finally, as an example application of the complete pipeline, a voxelwise map of white matter hyperintensity burden was computed using only the scans with registration quality above a predefined threshold. Methods were evaluated in a single-site dataset of more than 1000 scans, as well as a multi-center dataset comprising 142 clinical scans from 12 sites. The automated ventricle segmentation reached a Dice coefficient with manual annotations of 0.89 in the single-site dataset, and 0.83 in the multi-center dataset. Registration via age-specific atlases could improve ventricle overlap compared to a direct registration to the general atlas (Dice similarity coefficient increase up to 0.15). Experiments also showed that selecting scans with the registration quality assessment method could improve the quality of average maps of white matter hyperintensity burden, instead of using all scans for the computation of the white matter hyperintensity map. In this work, we demonstrated the utility of an automated tool for assessing image registration quality in clinical scans. This image quality assessment step could ultimately assist in the translation of automated neuroimaging pipelines to the clinic.
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Affiliation(s)
- Florian Dubost
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA; Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands.
| | - Marleen de Bruijne
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Marco Nardin
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Adrian V Dalca
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA
| | - Kathleen L Donahue
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Anne-Katrin Giese
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Mark R Etherton
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Marius de Groot
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC - University Medical Center Rotterdam, the Netherlands
| | - Wiro Niessen
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands; Department of Imaging Physics, Faculty of Applied Science, TU Delft, Delft, The Netherlands
| | - Meike Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, the Netherlands; Department of Epidemiology, Erasmus MC - University Medical Center Rotterdam, the Netherlands
| | - Natalia S Rost
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Markus D Schirmer
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA; Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA; Department of Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Germany.
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6
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Slurink I, Groen K, Gotz HM, Meima A, Kroone MM, Hogewoning AA, Ott A, Niessen W, Dukers-Muijers N, Hoebe C, Koedijk F, Kampman C, van Bergen J. Contribution of general practitioners and sexual health centres to sexually transmitted infection consultations in five Dutch regions using laboratory data of Chlamydia trachomatis testing. Int J STD AIDS 2020; 31:517-525. [PMID: 32131701 DOI: 10.1177/0956462420905275] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Effective sexually transmitted infection (STI) control requires opportunities for appropriate testing, counselling and treatment. In the Netherlands, people may attend general practitioners (GPs) and sexual health centres (SHCs; also known as STI clinics) for STI consultations. We assessed the contribution of GPs and SHCs to STI consultations in five Dutch regions with different urbanization levels, using data of urogenital Chlamydia trachomatis (CT) testing. Data (2011–2016) were retrieved from laboratories, aggregated by gender and age group (15–24 and 25–64 years). Results show that test rates and GP contribution varied widely between regions. GP contribution decreased over time in Amsterdam (60–48%), Twente (79–61%), Maastricht (60–50%) and Northeast-Netherlands (82–77%), but not in Rotterdam (65–67%). Decreases resulted from increases in SHC test rates and slight decreases in GP test rates. GPs performed more tests for women and those aged 25–64 years compared to SHCs (relative risks ranging from 1.49 to 4.76 and 1.58 to 7.43, respectively). The average yearly urogenital CT positivity rate was 9.2% at GPs and 10.7% at SHCs. Overall, GPs accounted for most STI consultations, yet SHC contribution increased. Continued focus on good quality STI care at GPs is essential, as increasing demands for care can not be entirely covered by SHCs.
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Affiliation(s)
- Ial Slurink
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - K Groen
- Department of Pulmonology, Interstitial Lung Diseases Center of Excellence, St Antonius Hospital, Nieuwegein, The Netherlands
| | - H M Gotz
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.,Municipal Public Health Service Rotterdam-Rijnmond, Rotterdam, The Netherlands.,Department of Public Health, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - A Meima
- Municipal Public Health Service Rotterdam-Rijnmond, Rotterdam, The Netherlands
| | - M M Kroone
- Department of Infectious Diseases, Municipal Public Health Service Amsterdam, Amsterdam, The Netherlands
| | - A A Hogewoning
- Department of Infectious Diseases, Municipal Public Health Service Amsterdam, Amsterdam, The Netherlands
| | - A Ott
- Department of Medical Microbiology, Certe, Groningen, The Netherlands
| | - W Niessen
- Municipal Public Health Service Groningen, Groningen, The Netherlands
| | - Nhtm Dukers-Muijers
- Department of Medical Microbiology, Care and Public Health Research Institute (CAPHRI), Maastricht University Medical Centre (MUMC+), Maastricht, The Netherlands.,Department of Sexual Health, Infectious Diseases and Environmental Health, South Limburg Public Health Service, Heerlen, The Netherlands
| | - Cjpa Hoebe
- Department of Medical Microbiology, Care and Public Health Research Institute (CAPHRI), Maastricht University Medical Centre (MUMC+), Maastricht, The Netherlands.,Department of Sexual Health, Infectious Diseases and Environmental Health, South Limburg Public Health Service, Heerlen, The Netherlands
| | - Fdh Koedijk
- Public Health Service Twente, Enschede, The Netherlands
| | - Cjg Kampman
- Public Health Service Twente, Enschede, The Netherlands
| | - Jeam van Bergen
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.,Department of General Practice, Amsterdam University Medical Centre, Amsterdam, The Netherlands.,STI AIDS Netherlands (SOA AIDS Nederland), Amsterdam, The Netherlands
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7
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Recht MP, Dewey M, Dreyer K, Langlotz C, Niessen W, Prainsack B, Smith JJ. Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations. Eur Radiol 2020; 30:3576-3584. [PMID: 32064565 DOI: 10.1007/s00330-020-06672-5] [Citation(s) in RCA: 93] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 12/21/2019] [Accepted: 01/23/2020] [Indexed: 12/31/2022]
Abstract
Artificial intelligence (AI) has the potential to significantly disrupt the way radiology will be practiced in the near future, but several issues need to be resolved before AI can be widely implemented in daily practice. These include the role of the different stakeholders in the development of AI for imaging, the ethical development and use of AI in healthcare, the appropriate validation of each developed AI algorithm, the development of effective data sharing mechanisms, regulatory hurdles for the clearance of AI algorithms, and the development of AI educational resources for both practicing radiologists and radiology trainees. This paper details these issues and presents possible solutions based on discussions held at the 2019 meeting of the International Society for Strategic Studies in Radiology. KEY POINTS: • Radiologists should be aware of the different types of bias commonly encountered in AI studies, and understand their possible effects. • Methods for effective data sharing to train, validate, and test AI algorithms need to be developed. • It is essential for all radiologists to gain an understanding of the basic principles, potentials, and limits of AI.
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Affiliation(s)
- Michael P Recht
- Department of Radiology, New York University Robert I Grossman School of Medicine, New York, NY, USA.
| | - Marc Dewey
- Charité - Universitätsmedizin Berlin, Humboldt-Universität and Freie Universität zu Berlin, Berlin, Germany
| | - Keith Dreyer
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Curtis Langlotz
- Department of Radiology and Biomedical Informatics, Stanford University, Palo Alto, CA, USA
| | - Wiro Niessen
- Department of Radiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
| | - Barbara Prainsack
- Department of Political Science, University of Vienna, Vienna, Austria
- Department of Global Health & Social Medicine, King's College, London, UK
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8
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Amier R, Marcks N, Hooghiemstra A, Nijveldt R, van Buchem M, De Roos A, Biessels GJ, Kappelle LJ, van Oostenbrugge R, Bots M, Niessen W, van Osch M, van der Flier W, Rocca HPBL, van Rossum A. INVESTIGATING THE RELATION OF HYPERTENSIVE DISEASE WITH VASCULAR BRAIN INJURY AND COGNITIVE IMPAIRMENT USING HEART-BRAIN MAGNETIC RESONANCE IN PATIENTS WITH CARDIOVASCULAR RISK FACTORS: THE HEART-BRAIN CONNECTION STUDY. J Am Coll Cardiol 2019. [DOI: 10.1016/s0735-1097(19)32271-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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9
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Dubost F, Adams H, Bortsova G, Ikram MA, Niessen W, Vernooij M, de Bruijne M. 3D regression neural network for the quantification of enlarged perivascular spaces in brain MRI. Med Image Anal 2019; 51:89-100. [DOI: 10.1016/j.media.2018.10.008] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Revised: 10/13/2018] [Accepted: 10/25/2018] [Indexed: 10/28/2022]
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10
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Maier-Hein L, Eisenmann M, Reinke A, Onogur S, Stankovic M, Scholz P, Arbel T, Bogunovic H, Bradley AP, Carass A, Feldmann C, Frangi AF, Full PM, van Ginneken B, Hanbury A, Honauer K, Kozubek M, Landman BA, März K, Maier O, Maier-Hein K, Menze BH, Müller H, Neher PF, Niessen W, Rajpoot N, Sharp GC, Sirinukunwattana K, Speidel S, Stock C, Stoyanov D, Taha AA, van der Sommen F, Wang CW, Weber MA, Zheng G, Jannin P, Kopp-Schneider A. Why rankings of biomedical image analysis competitions should be interpreted with care. Nat Commun 2018; 9:5217. [PMID: 30523263 PMCID: PMC6284017 DOI: 10.1038/s41467-018-07619-7] [Citation(s) in RCA: 143] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Accepted: 11/07/2018] [Indexed: 11/08/2022] Open
Abstract
International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future.
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Affiliation(s)
- Lena Maier-Hein
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.
| | - Matthias Eisenmann
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Annika Reinke
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Sinan Onogur
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Marko Stankovic
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Patrick Scholz
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Tal Arbel
- Centre for Intelligent Machines, McGill University, Montreal, QC, H3A0G4, Canada
| | - Hrvoje Bogunovic
- Christian Doppler Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology, Medical University Vienna, 1090, Vienna, Austria
| | - Andrew P Bradley
- Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD, 4001, Australia
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Carolin Feldmann
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Alejandro F Frangi
- CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine, The University of Leeds, Leeds, Yorkshire, LS2 9JT, UK
| | - Peter M Full
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Bram van Ginneken
- Department of Radiology and Nuclear Medicine, Medical Image Analysis, Radboud University Center, 6525 GA, Nijmegen, The Netherlands
| | - Allan Hanbury
- Institute of Information Systems Engineering, TU Wien, 1040, Vienna, Austria
- Complexity Science Hub Vienna, 1080, Vienna, Austria
| | - Katrin Honauer
- Heidelberg Collaboratory for Image Processing (HCI), Heidelberg University, 69120, Heidelberg, Germany
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Masaryk University, 60200, Brno, Czech Republic
| | - Bennett A Landman
- Electrical Engineering, Vanderbilt University, Nashville, TN, 37235-1679, USA
| | - Keno März
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Oskar Maier
- Institute of Medical Informatics, Universität zu Lübeck, 23562, Lübeck, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing (MIC), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Bjoern H Menze
- Institute for Advanced Studies, Department of Informatics, Technical University of Munich, 80333, Munich, Germany
| | - Henning Müller
- Information System Institute, HES-SO, Sierre, 3960, Switzerland
| | - Peter F Neher
- Division of Medical Image Computing (MIC), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Wiro Niessen
- Departments of Radiology, Nuclear Medicine and Medical Informatics, Erasmus MC, 3015 GD, Rotterdam, The Netherlands
| | - Nasir Rajpoot
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK
| | - Gregory C Sharp
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, 02114, USA
| | | | - Stefanie Speidel
- Division of Translational Surgical Oncology (TCO), National Center for Tumor Diseases Dresden, 01307, Dresden, Germany
| | - Christian Stock
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Danail Stoyanov
- Centre for Medical Image Computing (CMIC) & Department of Computer Science, University College London, London, W1W 7TS, UK
| | - Abdel Aziz Taha
- Data Science Studio, Research Studios Austria FG, 1090, Vienna, Austria
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands
| | - Ching-Wei Wang
- AIExplore, NTUST Center of Computer Vision and Medical Imaging, Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, 106, Taiwan
| | - Marc-André Weber
- Institute of Diagnostic and Interventional Radiology, University Medical Center Rostock, 18051, Rostock, Germany
| | - Guoyan Zheng
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, 3014, Switzerland
| | - Pierre Jannin
- Univ Rennes, Inserm, LTSI (Laboratoire Traitement du Signal et de l'Image) - UMR_S 1099, Rennes, 35043, Cedex, France
| | - Annette Kopp-Schneider
- Division of Biostatistics, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
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Luu HM, Moelker A, Klein S, Niessen W, van Walsum T. Quantification of nonrigid liver deformation in radiofrequency ablation interventions using image registration. ACTA ACUST UNITED AC 2018; 63:175005. [DOI: 10.1088/1361-6560/aad706] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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Van Vugt J, Coebergh van den Braak R, Schippers HJ, Veen K, Levolger S, de Bruin R, Koek M, Niessen W, Ijzermans J, Willemssen F. SUN-P201: Contrast-Enhancement Influences Skeletal Muscle Density, but Not Skeletal Muscle Mass, Measurements on Computed Tomography. Clin Nutr 2017. [DOI: 10.1016/s0261-5614(17)30427-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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13
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Zahnd G, Hoogendoorn A, Combaret N, Karanasos A, Péry E, Sarry L, Motreff P, Niessen W, Regar E, van Soest G, Gijsen F, van Walsum T. Contour segmentation of the intima, media, and adventitia layers in intracoronary OCT images: application to fully automatic detection of healthy wall regions. Int J Comput Assist Radiol Surg 2017; 12:1923-1936. [PMID: 28801817 PMCID: PMC5656722 DOI: 10.1007/s11548-017-1657-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Accepted: 08/03/2017] [Indexed: 11/29/2022]
Abstract
PURPOSE Quantitative and automatic analysis of intracoronary optical coherence tomography images is useful and time-saving to assess cardiovascular risk in the clinical arena. METHODS First, the interfaces of the intima, media, and adventitia layers are segmented, by means of an original front propagation scheme, running in a 4D multi-parametric space, to simultaneously extract three non-crossing contours in the initial cross-sectional image. Second, information resulting from the tentative contours is exploited by a machine learning approach to identify healthy and diseased regions of the arterial wall. The framework is fully automatic. RESULTS The method was applied to 40 patients from two different medical centers. The framework was trained on 140 images and validated on 260 other images. For the contour segmentation method, the average segmentation errors were [Formula: see text] for the intima-media interface, [Formula: see text] for the media-adventitia interface, and [Formula: see text] for the adventitia-periadventitia interface. The classification method demonstrated a good accuracy, with a median Dice coefficient equal to 0.93 and an interquartile range of (0.78-0.98). CONCLUSION The proposed framework demonstrated promising offline performances and could potentially be translated into a reliable tool for various clinical applications, such as quantification of tissue layer thickness and global summarization of healthy regions in entire pullbacks.
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Affiliation(s)
- Guillaume Zahnd
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands.
| | - Ayla Hoogendoorn
- Department of Biomedical Engineering, Thorax Center, Erasmus MC, Rotterdam, The Netherlands
| | - Nicolas Combaret
- Image Science for Interventional Techniques Laboratory, Université Clermont Auvergne, Université d'Auvergne, CNRS, UMR 6284, Clermont-Ferrand, France.,Department of Cardiology, Gabriel-Montpied Hospital, Clermont-Ferrand University Hospital, Clermont-Ferrand, France
| | - Antonios Karanasos
- Department of Interventional Cardiology, Thorax Center, Erasmus MC, Rotterdam, The Netherlands
| | - Emilie Péry
- Image Science for Interventional Techniques Laboratory, Université Clermont Auvergne, Université d'Auvergne, CNRS, UMR 6284, Clermont-Ferrand, France
| | - Laurent Sarry
- Image Science for Interventional Techniques Laboratory, Université Clermont Auvergne, Université d'Auvergne, CNRS, UMR 6284, Clermont-Ferrand, France
| | - Pascal Motreff
- Image Science for Interventional Techniques Laboratory, Université Clermont Auvergne, Université d'Auvergne, CNRS, UMR 6284, Clermont-Ferrand, France.,Department of Cardiology, Gabriel-Montpied Hospital, Clermont-Ferrand University Hospital, Clermont-Ferrand, France
| | - Wiro Niessen
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands
| | - Evelyn Regar
- Department of Interventional Cardiology, Thorax Center, Erasmus MC, Rotterdam, The Netherlands
| | - Gijs van Soest
- Department of Biomedical Engineering, Thorax Center, Erasmus MC, Rotterdam, The Netherlands
| | - Frank Gijsen
- Department of Biomedical Engineering, Thorax Center, Erasmus MC, Rotterdam, The Netherlands
| | - Theo van Walsum
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands
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Tap L, van Opbroek A, Niessen W, Smits M, Mattace- Raso F. P25 VASCULAR AGING IS ASSOCIATED WITH THE SEVERITY OF CEREBRAL WHITE MATTER LESION LOAD. Artery Res 2017. [DOI: 10.1016/j.artres.2017.10.166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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15
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Luu HM, Klink C, Niessen W, Moelker A, van Walsum T. Non-Rigid Registration of Liver CT Images for CT-Guided Ablation of Liver Tumors. PLoS One 2016; 11:e0161600. [PMID: 27611780 PMCID: PMC5017717 DOI: 10.1371/journal.pone.0161600] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Accepted: 08/08/2016] [Indexed: 12/23/2022] Open
Abstract
CT-guided percutaneous ablation for liver cancer treatment is a relevant technique for patients not eligible for surgery and with tumors that are inconspicuous on US imaging. The lack of real-time imaging and the use of a limited amount of CT contrast agent make targeting the tumor with the needle challenging. In this study, we evaluate a registration framework that allows the integration of diagnostic pre-operative contrast enhanced CT images and intra-operative non-contrast enhanced CT images to improve image guidance in the intervention. The liver and tumor are segmented in the pre-operative contrast enhanced CT images. Next, the contrast enhanced image is registered to the intra-operative CT images in a two-stage approach. First, the contrast-enhanced diagnostic image is non-rigidly registered to a non-contrast enhanced image that is conventionally acquired at the start of the intervention. In case the initial registration is not sufficiently accurate, a refinement step is applied using non-rigid registration method with a local rigidity term. In the second stage, the intra-operative CT-images that are used to check the needle position, which often consist of only a few slices, are registered rigidly to the intra-operative image that was acquired at the start of the intervention. Subsequently, the diagnostic image is registered to the current intra-operative image, using both transformations, this allows the visualization of the tumor region extracted from pre-operative data in the intra-operative CT images containing needle. The method is evaluated on imaging data of 19 patients at the Erasmus MC. Quantitative evaluation is performed using the Dice metric, mean surface distance of the liver border and corresponding landmarks in the diagnostic and the intra-operative images. The registration of the diagnostic CT image to the initial intra-operative CT image did not require a refinement step in 13 cases. For those cases, the resulting registration had a Dice coefficient for the livers of 91.4%, a mean surface distance of 4.4 mm and a mean distance between corresponding landmarks of 4.7 mm. For the three cases with a refinement step, the registration result significantly improved (p<0.05) compared to the result of the initial non rigid registration method (DICE of 90.3% vs 71.3% and mean surface distance of 5.1 mm vs 11.3 mm and mean distance between corresponding landmark of 6.4 mm vs 10.2 mm). The registration of the preoperative data with the needle image in 16 cases yielded a DICE of 90.1% and a mean surface distance of 5.2 mm. The remaining three cases with DICE smaller than 80% were classified as unsuccessful registration. The results show that this is promising tool for liver image registration in interventional radiology.
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Affiliation(s)
- Ha Manh Luu
- Biomedical Imaging Group Rotterdam, Departments of Radiology & Medical Informatics, Erasmus MC, Dr. Molewaterplein 50/60, Rotterdam, The Netherlands
- * E-mail:
| | - Camiel Klink
- Department of Radiology, Erasmus MC, Dr. Molewaterplein 50/60, Rotterdam, The Netherlands
| | - Wiro Niessen
- Biomedical Imaging Group Rotterdam, Departments of Radiology & Medical Informatics, Erasmus MC, Dr. Molewaterplein 50/60, Rotterdam, The Netherlands
| | - Adriaan Moelker
- Department of Radiology, Erasmus MC, Dr. Molewaterplein 50/60, Rotterdam, The Netherlands
| | - Theo van Walsum
- Biomedical Imaging Group Rotterdam, Departments of Radiology & Medical Informatics, Erasmus MC, Dr. Molewaterplein 50/60, Rotterdam, The Netherlands
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Poelman R, Luijt D, van Rhee-Luderer R, Niessen W, Rahamat-Langendoen J, van Genne M, Van Leer-Buter C, Niesters H. From TYPENED to REGIOtype to EUROtype: Moving towards a comprehensive surveillance strategy for emerging viruses. J Clin Virol 2016. [DOI: 10.1016/j.jcv.2016.08.112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Luu HM, Klink C, Niessen W, Moelker A, van Walsum T, Klink C, Moelker A. An automatic registration method for pre- and post-interventional CT images for assessing treatment success in liver RFA treatment. Med Phys 2016; 42:5559-67. [PMID: 26329002 DOI: 10.1118/1.4927790] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
PURPOSE In image-guided radio frequency ablation for liver cancer treatment, pre- and post-interventional CT images are typically used to verify the treatment success of the therapy. In current clinical practice, the tumor zone in the diagnostic, preinterventional images is mentally or manually mapped to the ablation zone in the post-interventional images to decide success of the treatment. However, liver deformation and differences in image quality as well as in texture of the ablation zone and the tumor area make the mental or manual registration a challenging task. Purpose of this paper is to develop an automatic framework to register the pre-interventional image to the post-interventional image. METHODS The authors propose a registration approach enabling a nonrigid deformation of the tumor to the ablation zone, while keeping locally rigid deformation of the tumor area. The method was evaluated on CT images of 38 patient datasets from Erasmus MC. The evaluation is based on Dice coefficients of the liver segmentation on both the pre-interventional and post-interventional images, and mean distances between the liver segmentations. Additionally, residual distances after registration between corresponding landmarks and local mean surface distance in the images were computed. RESULTS The results show that rigid registration gives a Dice coefficient of 87.9%, a mean distance of the liver surfaces of 5.53 mm, and a landmark error of 5.38 mm, while non-rigid registration with local rigid deformation has a Dice coefficient of 92.2%, a mean distance between the liver segmentation boundaries near the tumor area of 3.83 mm, and a landmark error of 2.91 mm, where a part of this error can be attributed to the slice spacing in the authors' CT images. CONCLUSIONS This method is thus a promising tool to assess the success of RFA liver cancer treatment.
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Affiliation(s)
- Ha Manh Luu
- Departments of Radiology and Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC, Dr. Molewaterplein 50/60, Rotterdam 3015 GE, The Netherlands
| | - Camiel Klink
- Department of Radiology, Erasmus MC, Dr. Molewaterplein 50/60, Rotterdam 3015 GE, The Netherlands
| | - Wiro Niessen
- Departments of Radiology and Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC, Dr. Molewaterplein 50/60, Rotterdam 3015 GE, The Netherlands
| | - Adriaan Moelker
- Department of Radiology, Erasmus MC, Dr. Molewaterplein 50/60, Rotterdam 3015 GE, The Netherlands
| | - Theo van Walsum
- Departments of Radiology and Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC, Dr. Molewaterplein 50/60, Rotterdam 3015 GE, The Netherlands
| | - Camiel Klink
- Department of Radiology, Erasmus MC, Dr. Molewaterplein 50/60, Rotterdam 3015 GE, The Netherlands
| | - Adriaan Moelker
- Department of Radiology, Erasmus MC, Dr. Molewaterplein 50/60, Rotterdam 3015 GE, The Netherlands
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Zahnd G, Schrauwen J, Karanasos A, Regar E, Niessen W, van Walsum T, Gijsen F. Fusion of fibrous cap thickness and wall shear stress to assess plaque vulnerability in coronary arteries: a pilot study. Int J Comput Assist Radiol Surg 2016; 11:1779-90. [PMID: 27236652 PMCID: PMC5034011 DOI: 10.1007/s11548-016-1422-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2015] [Accepted: 05/11/2016] [Indexed: 12/16/2022]
Abstract
Purpose Identification of rupture-prone plaques in coronary arteries is a major clinical challenge. Fibrous cap thickness and wall shear stress are two relevant image-based risk factors, but these two parameters are generally computed and analyzed separately. Accordingly, combining these two parameters can potentially improve the identification of at-risk regions. Therefore, the purpose of this study is to investigate the feasibility of the fusion of wall shear stress and fibrous cap thickness of coronary arteries in patient data. Methods Fourteen patients were included in this pilot study. Imaging of the coronary arteries was performed with optical coherence tomography and with angiography. Fibrous cap thickness was automatically quantified from optical coherence tomography pullbacks using a contour segmentation approach based on fast marching. Wall shear stress was computed by applying computational fluid dynamics on the 3D volume reconstructed from two angiograms. The two parameters then were co-registered using anatomical landmarks such as side branches. Results The two image modalities were successfully co-registered, with a mean (±SD) error corresponding to \documentclass[12pt]{minimal}
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\begin{document}$$8.6\,\pm \,6.7\,\%$$\end{document}8.6±6.7% of the length of the analyzed region. For all the analyzed participants, the average thinnest portion of each fibrous cap was \documentclass[12pt]{minimal}
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\begin{document}$$129\,\pm \,69\,\upmu \text {m}$$\end{document}129±69μm, and the average WSS value at the location of the fibrous cap was \documentclass[12pt]{minimal}
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\begin{document}$$1.46\,\pm \,1.16\,\text {Pa}$$\end{document}1.46±1.16Pa. A unique index was finally generated for each patient via the fusion of fibrous cap thickness and wall shear stress measurements, to translate all the measured parameters into a single risk map. Conclusion The introduced risk map integrates two complementary parameters and has potential to provide valuable information about plaque vulnerability.
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Affiliation(s)
- Guillaume Zahnd
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands.
| | - Jelle Schrauwen
- Department of Biomedical Engineering, Thorax Center, Erasmus MC, Rotterdam, The Netherlands
| | - Antonios Karanasos
- Department of Interventional Cardiology, Thorax Center, Erasmus MC, Rotterdam, The Netherlands
| | - Evelyn Regar
- Department of Interventional Cardiology, Thorax Center, Erasmus MC, Rotterdam, The Netherlands
| | - Wiro Niessen
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands
| | - Theo van Walsum
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands
| | - Frank Gijsen
- Department of Biomedical Engineering, Thorax Center, Erasmus MC, Rotterdam, The Netherlands
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de Jong MA, Wollstein A, Ruff C, Dunaway D, Hysi P, Spector T, Niessen W, Koudstaal MJ, Kayser M, Wolvius EB, Bohringer S. An Automatic 3D Facial Landmarking Algorithm Using 2D Gabor Wavelets. IEEE Trans Image Process 2016; 25:580-588. [PMID: 26540684 DOI: 10.1109/tip.2015.2496183] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
In this paper, we present a novel approach to automatic 3D facial landmarking using 2D Gabor wavelets. Our algorithm considers the face to be a surface and uses map projections to derive 2D features from raw data. Extracted features include texture, relief map, and transformations thereof. We extend an established 2D landmarking method for simultaneous evaluation of these data. The method is validated by performing landmarking experiments on two data sets using 21 landmarks and compared with an active shape model implementation. On average, landmarking error for our method was 1.9 mm, whereas the active shape model resulted in an average landmarking error of 2.3 mm. A second study investigating facial shape heritability in related individuals concludes that automatic landmarking is on par with manual landmarking for some landmarks. Our algorithm can be trained in 30 min to automatically landmark 3D facial data sets of any size, and allows for fast and robust landmarking of 3D faces.
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Korsager AS, Fortunati V, van der Lijn F, Carl J, Niessen W, Østergaard LR, van Walsum T. The use of atlas registration and graph cuts for prostate segmentation in magnetic resonance images. Med Phys 2015; 42:1614-24. [PMID: 25832052 DOI: 10.1118/1.4914379] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
PURPOSE An automatic method for 3D prostate segmentation in magnetic resonance (MR) images is presented for planning image-guided radiotherapy treatment of prostate cancer. METHODS A spatial prior based on intersubject atlas registration is combined with organ-specific intensity information in a graph cut segmentation framework. The segmentation is tested on 67 axial T2-weighted MR images in a leave-one-out cross validation experiment and compared with both manual reference segmentations and with multiatlas-based segmentations using majority voting atlas fusion. The impact of atlas selection is investigated in both the traditional atlas-based segmentation and the new graph cut method that combines atlas and intensity information in order to improve the segmentation accuracy. Best results were achieved using the method that combines intensity information, shape information, and atlas selection in the graph cut framework. RESULTS A mean Dice similarity coefficient (DSC) of 0.88 and a mean surface distance (MSD) of 1.45 mm with respect to the manual delineation were achieved. CONCLUSIONS This approaches the interobserver DSC of 0.90 and interobserver MSD 0f 1.15 mm and is comparable to other studies performing prostate segmentation in MR.
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Affiliation(s)
- Anne Sofie Korsager
- Department of Health Science and Technology, Aalborg University, Aalborg 9220, Denmark
| | - Valerio Fortunati
- Biomedical Imaging Group of Rotterdam, Department of Medical Informatics and Radiology, Erasmus MC, Rotterdam 3015 GE Rotterdam, The Netherlands
| | - Fedde van der Lijn
- Biomedical Imaging Group of Rotterdam, Department of Medical Informatics and Radiology, Erasmus MC, Rotterdam 3015 GE Rotterdam, The Netherlands
| | - Jesper Carl
- Department of Medical Physics, Oncology, Aalborg University Hospital, Aalborg 9220, Denmark
| | - Wiro Niessen
- Biomedical Imaging Group of Rotterdam, Department of Medical Informatics and Radiology, Erasmus MC, Rotterdam 3015 GE Rotterdam, The Netherlands
| | - Lasse Riis Østergaard
- Department of Health Science and Technology, Aalborg University, Aalborg 9220, Denmark
| | - Theo van Walsum
- Biomedical Imaging Group of Rotterdam, Department of Medical Informatics and Radiology, Erasmus MC, Rotterdam 3015 GE Rotterdam, The Netherlands
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Luu HM, Klink C, Niessen W, Moelker A, van Walsum T. Erratum: “An automatic registration method for pre- and post-interventional CT images for assessing treatment success in liver RFA treatment” [Med. Phys. 42, 5559-5567 (2015)]. Med Phys 2015; 42:7202. [DOI: 10.1118/1.4935867] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Zahnd G, Karanasos A, van Soest G, Regar E, Niessen W, Gijsen F, van Walsum T. Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming. Int J Comput Assist Radiol Surg 2015; 10:1383-94. [PMID: 25740203 PMCID: PMC4563002 DOI: 10.1007/s11548-015-1164-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2014] [Accepted: 02/13/2015] [Indexed: 12/03/2022]
Abstract
OBJECTIVES Fibrous cap thickness is the most critical component of plaque stability. Therefore, in vivo quantification of cap thickness could yield valuable information for estimating the risk of plaque rupture. In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure. However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment. METHODS A novel semiautomatic segmentation method to quantify coronary fibrous cap thickness in optical coherence tomography is introduced. To cope with the most challenging issue when estimating cap thickness (namely the diffuse appearance of the anatomical abluminal interface to be detected), the proposed method is based on a robust dynamic programming framework using a geometrical a priori. To determine the optimal parameter settings, a training phase was conducted on 10 patients. RESULTS Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours. When assessing minimal cap thickness, segmentation results from the proposed method were in good agreement with the reference tracings performed by a medical expert (mean absolute error and standard deviation of 22 ± 18 μm) and were similar to inter-observer reproducibility (21 ± 19 μm, R = .74), while being significantly faster and fully reproducible. CONCLUSION The proposed framework demonstrated promising performances and could potentially be used for online identification of high-risk plaques.
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Affiliation(s)
- Guillaume Zahnd
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands,
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van Kranenburg M, Karanasos A, Chelu RG, van der Heide E, Ouhlous M, Nieman K, van Mieghem N, Krestin G, Niessen W, Zijlstra F, van Geuns RJ, Daemen J. Validation of renal artery dimensions measured by magnetic resonance angiography in patients referred for renal sympathetic denervation. Acad Radiol 2015; 22:1106-14. [PMID: 26162249 DOI: 10.1016/j.acra.2015.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Revised: 03/17/2015] [Accepted: 03/18/2015] [Indexed: 10/23/2022]
Abstract
RATIONALE AND OBJECTIVES Magnetic resonance angiography (MRA) is a well-established modality for the assessment of renal artery stenosis. Using dedicated quantitative analyses, MRA can become a useful tool for assessing renal artery dimensions in patients referred for renal sympathetic denervation (RDN) and for providing accurate measurements of vascular response after RDN. The purpose of this study was to test the reproducibility of a novel MRA quantitative imaging tool and to validate these measurements against intravascular ultrasound (IVUS). MATERIALS AND METHODS In nine patients referred for renal denervation, renal artery dimensions were measured. Bland-Altman analysis was used to assess the intraobserver and interobserver reproducibility. RESULTS Mean lumen diameter was 5.8 ± 0.7 mm, with a very good intraobserver and interobserver variability of 0.7% (reproducibility: bias, 0 mm; standard deviation [SD], 0.1 mm) and 1.2% (bias, 0 mm; SD, 0.1 mm), respectively. Mean total lumen volume was 1035.3 ± 403.6 mm(3) with good intraobserver and interobserver variability of 2.9% (bias, -9.7 mm(3); SD, 34.0 mm(3)) and 2.8% (bias, -11.4 mm(3); SD, 42.4 mm(3)). The correlation (Pearson R) between mean lumen diameter measured with MRA and IVUS was 0.750 (P = .002). CONCLUSIONS Using a novel MRA quantitative imaging tool, renal artery dimensions can be measured with good reproducibility and accuracy. MRA-derived diameters and volumes correlated well with IVUS measurements.
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Luu HM, Klink C, Moelker A, Niessen W, van Walsum T. Quantitative evaluation of noise reduction and vesselness filters for liver vessel segmentation on abdominal CTA images. Phys Med Biol 2015; 60:3905-26. [PMID: 25909487 DOI: 10.1088/0031-9155/60/10/3905] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Liver vessel segmentation in CTA images is a challenging task, especially in the case of noisy images. This paper investigates whether pre-filtering improves liver vessel segmentation in 3D CTA images. We introduce a quantitative evaluation of several well-known filters based on a proposed liver vessel segmentation method on CTA images. We compare the effect of different diffusion techniques i.e. Regularized Perona-Malik, Hybrid Diffusion with Continuous Switch and Vessel Enhancing Diffusion as well as the vesselness approaches proposed by Sato, Frangi and Erdt. Liver vessel segmentation of the pre-processed images is performed using a histogram-based region grown with local maxima as seed points. Quantitative measurements (sensitivity, specificity and accuracy) are determined based on manual landmarks inside and outside the vessels, followed by T-tests for statistic comparisons on 51 clinical CTA images. The evaluation demonstrates that all the filters make liver vessel segmentation have a significantly higher accuracy than without using a filter (p < 0.05); Hybrid Diffusion with Continuous Switch achieves the best performance. Compared to the diffusion filters, vesselness filters have a greater sensitivity but less specificity. In addition, the proposed liver vessel segmentation method with pre-filtering is shown to perform robustly on a clinical dataset having a low contrast-to-noise of up to 3 (dB). The results indicate that the pre-filtering step significantly improves liver vessel segmentation on 3D CTA images.
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Affiliation(s)
- Ha Manh Luu
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Dr. Molewaterplein 50/60, Rotterdam, The Netherlands
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Baka N, Lelieveldt BPF, Schultz C, Niessen W, van Walsum T. Respiratory motion estimation in x-ray angiography for improved guidance during coronary interventions. Phys Med Biol 2015; 60:3617-37. [DOI: 10.1088/0031-9155/60/9/3617] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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26
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27
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van Buchem MA, Biessels GJ, Brunner la Rocca HP, de Craen AJ, van der Flier WM, Ikram MA, Kappelle LJ, Koudstaal PJ, Mooijaart SP, Niessen W, van Oostenbrugge R, de Roos A, van Rossum AC, Daemen MJ. The Heart-Brain Connection: A Multidisciplinary Approach Targeting a Missing Link in the Pathophysiology of Vascular Cognitive Impairment. ACTA ACUST UNITED AC 2014; 42 Suppl 4:S443-51. [DOI: 10.3233/jad-141542] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Mark A. van Buchem
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Geert Jan Biessels
- Department of Neurology, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Anton J.M. de Craen
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | | | - M. Arfan Ikram
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Radiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - L. Jaap Kappelle
- Department of Neurology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Peter J. Koudstaal
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Simon P. Mooijaart
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Wiro Niessen
- Department of Radiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | - Albert de Roos
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Albert C. van Rossum
- Department of Cardiology, VU University Medical Center, Amsterdam, The Netherlands
| | - Mat J.A.P. Daemen
- Department of Pathology, Academic Medical Center, Amsterdam, The Netherlands
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28
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Dibildox G, Baka N, Punt M, Aben JP, Schultz C, Niessen W, van Walsum T. 3D/3D registration of coronary CTA and biplane XA reconstructions for improved image guidance. Med Phys 2014; 41:091909. [DOI: 10.1118/1.4892055] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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29
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Verlinden VJ, Geest J, Hofman A, Niessen W, Lugt A, Vernooij M, Ikram MA. P3‐201: STRUCTURAL BRAIN CHANGES ASSOCIATE ESPECIALLY WITH DECLINE IN DAILY FUNCTIONING AND LESS WITH COGNITIVE DECLINE, INDEPENDENT OF INCIDENT DEMENTIA. Alzheimers Dement 2014. [DOI: 10.1016/j.jalz.2014.05.1292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Affiliation(s)
| | | | | | - Wiro Niessen
- Erasmus University Medical CenterRotterdamNetherlands
| | - Aad Lugt
- Erasmus University Medical CenterRotterdamNetherlands
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30
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Zonneveld H, Loehrer E, Krestin G, Niessen W, Hofman A, Ikram MA, Vernooij M. P3‐193: LONGITUDINAL CHANGE IN TOTAL CEREBRAL BLOOD FLOW AND PARENCHYMAL CEREBRAL BLOOD FLOW IN THE GENERAL AGING POPULATION. Alzheimers Dement 2014. [DOI: 10.1016/j.jalz.2014.05.1283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
| | | | | | - Wiro Niessen
- Erasmus MC University Medical CenterRotterdamNetherlands
| | - Albert Hofman
- Erasmus MC University Medical CenterRotterdamNetherlands
| | | | - Meike Vernooij
- Erasmus MC University Medical CenterRotterdamNetherlands
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31
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Cremers L, Groot M, Hofman A, Krestin G, Lugt A, Niessen W, Ikram MA, Vernooij M. P3‐196: WHITE MATTER DEGENERATES OVER TIME: A LONGITUDINAL DIFFUSION MRI ANALYSIS. Alzheimers Dement 2014. [DOI: 10.1016/j.jalz.2014.05.1286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Lotte Cremers
- Erasmus MC University Medical CenterRotterdamNetherlands
| | - Marius Groot
- Erasmus MC University Medical CenterRotterdamNetherlands
| | - Albert Hofman
- Erasmus MC University Medical CenterRotterdamNetherlands
| | | | - Aad Lugt
- Erasmus MC University Medical CenterRotterdamNetherlands
| | - Wiro Niessen
- Erasmus MC University Medical CenterRotterdamNetherlands
| | | | - Meike Vernooij
- Erasmus MC University Medical CenterRotterdamNetherlands
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32
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Bron E, Smits M, van Swieten J, Niessen W, Klein S. Feature Selection Based on SVM Significance Maps for Classification of Dementia. Machine Learning in Medical Imaging 2014. [DOI: 10.1007/978-3-319-10581-9_34] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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33
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Shahzad R, Bos D, Metz C, Rossi A, Kirişli H, van der Lugt A, Klein S, Witteman J, de Feyter P, Niessen W, van Vliet L, van Walsum T. Automatic quantification of epicardial fat volume on non-enhanced cardiac CT scans using a multi-atlas segmentation approach. Med Phys 2013; 40:091910. [DOI: 10.1118/1.4817577] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
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34
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Verlinden V, Vernooij M, Geest J, Hofman A, Niessen W, Lugt A, Ikram M. P3–182: Brain atrophy is associated with decline in activities of daily living after up to 7 years of follow‐up. Alzheimers Dement 2013. [DOI: 10.1016/j.jalz.2013.05.1254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Affiliation(s)
| | | | | | | | | | - Aad Lugt
- Erasmus MC Rotterdam Netherlands
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35
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Groot M, Verhaaren B, Boer R, Klein S, Hofman A, Lugt A, Ikram M, Niessen W, Vernooij M. P3–181: Development of white matter lesions is preceded by changes in normal‐appearing white matter. Alzheimers Dement 2013. [DOI: 10.1016/j.jalz.2013.05.1253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- Marius Groot
- Erasmus MC University Medical Center Rotterdam Netherlands
| | | | - Renske Boer
- Erasmus MC University Medical Center Rotterdam Netherlands
| | - Stefan Klein
- Erasmus MC University Medical Center Rotterdam Netherlands
| | - Albert Hofman
- Erasmus MC University Medical Center Rotterdam Netherlands
| | - Aad Lugt
- Erasmus MC University Medical Center Rotterdam Netherlands
| | - Mohammad Ikram
- Erasmus MC University Medical Center Rotterdam Netherlands
| | - Wiro Niessen
- Erasmus MC University Medical Center Rotterdam Netherlands
| | - Meike Vernooij
- Erasmus MC University Medical Center Rotterdam Netherlands
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36
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Loehrer E, Ikram M, Akoudad S, Vrooman H, Lugt A, Niessen W, Hofman A, Vernooij M. IC‐P‐140: Apolipoprotein E genotype influences spatial distribution of cerebral microbleeds. Alzheimers Dement 2013. [DOI: 10.1016/j.jalz.2013.05.137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | | | - Saloua Akoudad
- Erasmus MC University Medical Center Rotterdam Netherlands
| | - Henri Vrooman
- Erasmus MC University Medical Center Rotterdam Netherlands
| | - Aad Lugt
- Erasmus MC University Medical Center Rotterdam Netherlands
| | - Wiro Niessen
- Erasmus MC University Medical Center Rotterdam Netherlands
| | - Albert Hofman
- Erasmus MC University Medical Center Rotterdam Netherlands
| | - Meike Vernooij
- Erasmus MC University Medical Center Rotterdam Netherlands
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37
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Ikram M, Jong FJ, Ikram MK, Vernooij M, Niessen W, Klaver C, Lugt A, Hofman A. IC‐P‐193: Retinal vessel calibers associate differentially with grey matter and white matter atrophy on MRI. Alzheimers Dement 2013. [DOI: 10.1016/j.jalz.2013.05.192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
| | | | | | | | | | | | - Aad Lugt
- Erasmus MC Rotterdam Netherlands
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38
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Papma J, Groot M, De Koning I, Mattace‐Raso F, Lugt A, Vernooij M, Niessen W, Swieten J, Koudstaal P, Prins N, Smits M. O5–04–02: The effects of cerebral small vessel disease in normal appearing white matter integrity in mild cognitive impairment: A diffusion tensor imaging study. Alzheimers Dement 2013. [DOI: 10.1016/j.jalz.2013.04.488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Janne Papma
- Erasmus University Medical Center Rotterdam Netherlands
| | - Marius Groot
- Erasmus University Medical Center Rotterdam Netherlands
| | | | | | - Aad Lugt
- Erasmus University Medical Center Rotterdam Netherlands
| | | | - Wiro Niessen
- Erasmus University Medical Center Rotterdam Netherlands
| | - John Swieten
- Erasmus University Medical Center Rotterdam Netherlands
| | | | | | - Marion Smits
- Erasmus University Medical Center Rotterdam Netherlands
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39
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Prins N, Papma J, Smits M, Mattace‐Raso F, De Koning I, Niessen W, Swieten J, Koudstaal P. P4–166: Clinical applicability of new criteria for MCI due to Alzheimer's disease and vascular MCI. Alzheimers Dement 2013. [DOI: 10.1016/j.jalz.2013.05.1557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Affiliation(s)
- Niels Prins
- VU University Medical Center Amsterdam Netherlands
| | - Janne Papma
- Erasmus Medical Center Rotterdam Netherlands
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40
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Groot M, Ikram M, Niessen W, Vernooij M. O3–11–01: White‐matter tract diffusion measurements in the general population. Alzheimers Dement 2013. [DOI: 10.1016/j.jalz.2013.04.297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Marius Groot
- Erasmus MC University Medical Center Rotterdam Netherlands
| | - Mohammad Ikram
- Erasmus MC University Medical Center Rotterdam Netherlands
| | - Wiro Niessen
- Erasmus MC University Medical Center Rotterdam Netherlands
| | - Meike Vernooij
- Erasmus MC University Medical Center Rotterdam Netherlands
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41
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Ikram M, Jong FJ, Ikram MK, Vernooij M, Niessen W, Klaver C, Lugt A, Hofman A. O3–11–06: Retinal vessel calibers associate differentially with grey matter and white matter atrophy on MRI. Alzheimers Dement 2013. [DOI: 10.1016/j.jalz.2013.04.302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Affiliation(s)
| | | | | | | | - Wiro Niessen
- Erasmus Medical Center Rotterdam Rotterdam Netherlands
| | | | - Aad Lugt
- Erasmus Medical Center Rotterdam Rotterdam Netherlands
| | - Albert Hofman
- Erasmus Medical Center Rotterdam Rotterdam Netherlands
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42
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Akoudad S, Groot M, Koudstaal P, Lugt A, Niessen W, Hofman A, Ikram M, Vernooij M. O1–03–04: Cerebral microbleeds are related to loss of white matter structural integrity. Alzheimers Dement 2013. [DOI: 10.1016/j.jalz.2013.04.064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
| | | | | | - Aad Lugt
- Erasmus Medical Center Rotterdam Netherlands
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43
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Gupta V, Kirişli HA, Hendriks EA, van der Geest RJ, van de Giessen M, Niessen W, Reiber JHC, Lelieveldt BPF. Cardiac MR perfusion image processing techniques: a survey. Med Image Anal 2012; 16:767-85. [PMID: 22297264 DOI: 10.1016/j.media.2011.12.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2011] [Revised: 12/14/2011] [Accepted: 12/15/2011] [Indexed: 02/05/2023]
Abstract
First-pass cardiac MR perfusion (CMRP) imaging has undergone rapid technical advancements in recent years. Although the efficacy of CMRP imaging in the assessment of coronary artery diseases (CAD) has been proven, its clinical use is still limited. This limitation stems, in part, from manual interaction required to quantitatively analyze the large amount of data. This process is tedious, time-consuming, and prone to operator bias. Furthermore, acquisition and patient related image artifacts reduce the accuracy of quantitative perfusion assessment. With the advent of semi- and fully automatic image processing methods, not only the challenges posed by these artifacts have been overcome to a large extent, but a significant reduction has also been achieved in analysis time and operator bias. Despite an extensive literature on such image processing methods, to date, no survey has been performed to discuss this dynamic field. The purpose of this article is to provide an overview of the current state of the field with a categorical study, along with a future perspective on the clinical acceptance of image processing methods in the diagnosis of CAD.
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Affiliation(s)
- Vikas Gupta
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
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44
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Manh Luu H, Moelker A, Klink C, Mendrik A, Niessen W, van Walsum T. Evaluation of Diffusion Filters for 3D CTA Liver Vessel Enhancement. Lecture Notes in Computer Science 2012. [DOI: 10.1007/978-3-642-33612-6_18] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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45
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Schaap M, van Walsum T, Neefjes L, Metz C, Capuano E, de Bruijne M, Niessen W. Robust shape regression for supervised vessel segmentation and its application to coronary segmentation in CTA. IEEE Trans Med Imaging 2011; 30:1974-1986. [PMID: 21708497 DOI: 10.1109/tmi.2011.2160556] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
This paper presents a vessel segmentation method which learns the geometry and appearance of vessels in medical images from annotated data and uses this knowledge to segment vessels in unseen images. Vessels are segmented in a coarse-to-fine fashion. First, the vessel boundaries are estimated with multivariate linear regression using image intensities sampled in a region of interest around an initialization curve. Subsequently, the position of the vessel boundary is refined with a robust nonlinear regression technique using intensity profiles sampled across the boundary of the rough segmentation and using information about plausible cross-sectional vessel shapes. The method was evaluated by quantitatively comparing segmentation results to manual annotations of 229 coronary arteries. On average the difference between the automatically obtained segmentations and manual contours was smaller than the inter-observer variability, which is an indicator that the method outperforms manual annotation. The method was also evaluated by using it for centerline refinement on 24 publicly available datasets of the Rotterdam Coronary Artery Evaluation Framework. Centerlines are extracted with an existing method and refined with the proposed method. This combination is currently ranked second out of 10 evaluated interactive centerline extraction methods. An additional qualitative expert evaluation in which 250 automatic segmentations were compared to manual segmentations showed that the automatically obtained contours were rated on average better than manual contours.
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Affiliation(s)
- Michiel Schaap
- Departments of Medical Informatics and Radiology, Erasmus MC—University Medical Center Rotterdam, The Netherlands.
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46
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Smal I, Carranza-Herrezuelo N, Klein S, Wielopolski P, Moelker A, Springeling T, Bernsen M, Niessen W, Meijering E. Reversible jump MCMC methods for fully automatic motion analysis in tagged MRI. Med Image Anal 2011; 16:301-24. [PMID: 21963294 DOI: 10.1016/j.media.2011.08.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2010] [Revised: 08/03/2011] [Accepted: 08/22/2011] [Indexed: 11/18/2022]
Abstract
Tagged magnetic resonance imaging (tMRI) is a well-known noninvasive method for studying regional heart dynamics. It offers great potential for quantitative analysis of a variety of kine(ma)tic parameters, but its clinical use has so far been limited, in part due to the lack of robustness and accuracy of existing tag tracking algorithms in dealing with low (and intrinsically time-varying) image quality. In this paper, we evaluate the performance of four frequently used concepts found in the literature (optical flow, harmonic phase (HARP) magnetic resonance imaging, active contour fitting, and non-rigid image registration) for cardiac motion analysis in 2D tMRI image sequences, using both synthetic image data (with ground truth) and real data from preclinical (small animal) and clinical (human) studies. In addition we propose a new probabilistic method for tag tracking that serves as a complementary step to existing methods. The new method is based on a Bayesian estimation framework, implemented by means of reversible jump Markov chain Monte Carlo (MCMC) methods, and combines information about the heart dynamics, the imaging process, and tag appearance. The experimental results demonstrate that the new method improves the performance of even the best of the four previous methods. Yielding higher consistency, accuracy, and intrinsic tag reliability assessment, the proposed method allows for improved analysis of cardiac motion.
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Affiliation(s)
- Ihor Smal
- Department of Medical Informatics, Erasmus MC - University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.
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47
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Verhaaren B, Vernooij M, Uitterlinden A, Hofman A, Niessen W, Lugt A, Breteler M, Ikram MA. P1‐388: Are SNPs associated with Alzheimer's disease also associated with cognition and structural brain changes in a relatively young population? Alzheimers Dement 2011. [DOI: 10.1016/j.jalz.2011.05.669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
| | | | | | | | | | - Aad Lugt
- Erasmus Medical CenterRotterdamNetherlands
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48
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Dzyubachyk O, Jelier R, Lehner B, Niessen W, Meijering E. Model-based approach for tracking embryogenesis in Caenorhabditis elegans fluorescence microscopy data. Annu Int Conf IEEE Eng Med Biol Soc 2010; 2009:5356-9. [PMID: 19964674 DOI: 10.1109/iembs.2009.5334046] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The nematode Caenorhabditis elegans (C. elegans) is a widely used model organism in biological investigations. Due to its well-known and invariant cell lineage tree, it can be used to study the effects of mutations and various disease processes. Effective and efficient analysis of the wealth of time-lapse fluorescence microscopy image data acquired in such studies requires automation of the cell segmentation and tracking tasks involved. This is hampered by many factors, including autofluorescence effects, low and uneven contrast throughout the images, high noise levels, large numbers of possibly simultaneous cell divisions, and touching or clustering cells. In this paper, we present a new algorithm for segmentation and tracking of cells in C. elegans embryogenesis image data. It is based on the model evolution framework for image segmentation and uses a novel multi-object tracking scheme based on energy minimization via graph cuts. Preliminary experiments on publicly available test data demonstrate the potential of the algorithm compared to existing approaches.
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Affiliation(s)
- Oleh Dzyubachyk
- Departments of Medical Informatics and Radiology, Erasmus MC - University Medical Center Rotterdam, The Netherlands.
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49
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Abstract
Quantitative analysis of biological image data generally involves the detection of many subresolution spots. Especially in live cell imaging, for which fluorescence microscopy is often used, the signal-to-noise ratio (SNR) can be extremely low, making automated spot detection a very challenging task. In the past, many methods have been proposed to perform this task, but a thorough quantitative evaluation and comparison of these methods is lacking in the literature. In this paper, we evaluate the performance of the most frequently used detection methods for this purpose. These include seven unsupervised and two supervised methods. We perform experiments on synthetic images of three different types, for which the ground truth was available, as well as on real image data sets acquired for two different biological studies, for which we obtained expert manual annotations to compare with. The results from both types of experiments suggest that for very low SNRs ( approximately 2), the supervised (machine learning) methods perform best overall. Of the unsupervised methods, the detectors based on the so-called h -dome transform from mathematical morphology or the multiscale variance-stabilizing transform perform comparably, and have the advantage that they do not require a cumbersome learning stage. At high SNRs ( > 5), the difference in performance of all considered detectors becomes negligible.
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Affiliation(s)
- Ihor Smal
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands.
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50
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Metz C, Baka N, Kirisli H, Schaap M, van Walsum T, Klein S, Neefjes L, Mollet N, Lelieveldt B, de Bruijne M, Niessen W. Conditional shape models for cardiac motion estimation. Med Image Comput Comput Assist Interv 2010; 13:452-9. [PMID: 20879262 DOI: 10.1007/978-3-642-15705-9_55] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
We propose a conditional statistical shape model to predict patient specific cardiac motion from the 3D end-diastolic CTA scan. The model is built from 4D CTA sequences by combining atlas based segmentation and 4D registration. Cardiac motion estimation is, for example, relevant in the dynamic alignment of pre-operative CTA data with intra-operative X-ray imaging. Due to a trend towards prospective electrocardiogram gating techniques, 4D imaging data, from which motion information could be extracted, is not commonly available. The prediction of motion from shape information is thus relevant for this purpose. Evaluation of the accuracy of the predicted motion was performed using CTA scans of 50 patients, showing an average accuracy of 1.1 mm.
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Affiliation(s)
- Coert Metz
- Dept. of Rad. and Med. Informatics, Erasmus MC, Rotterdam, The Netherlands
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