18 results on '"Brecheisen R"'
Search Results
2. Illustrative uncertainty visualization of DTI fiber pathways
- Author
-
Brecheisen, R., Platel, B., ter Haar Romeny, B. M., and Vilanova, A.
- Published
- 2013
- Full Text
- View/download PDF
3. Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder
- Author
-
Schwarz, E., Doan, N. T., Pergola, G., Westlye, L. T., Kaufmann, T., Wolfers, T., Brecheisen, R., Quarto, T., Ing, A. J., Di Carlo, P., Gurholt, T. P., Harms, R. L., Noirhomme, Q., Moberget, T., Agartz, I., Andreassen, O. A., Bellani, M., Bertolino, A., Blasi, G., Brambilla, P., Buitelaar, J. K., Cervenka, S., Flyckt, L., Frangou, S., Franke, B., Hall, J., Heslenfeld, D. J., Kirsch, P., Mcintosh, A. M., Nothen, M. M., Papassotiropoulos, A., de Quervain, D. J. -F., Rietschel, M., Schumann, G., Tost, H., Witt, S. H., Zink, M., Meyer-Lindenberg, A., Bettella, F., Brandt, C. L., Clarke, T. -K., Coynel, D., Degenhardt, F., Djurovic, S., Eisenacher, S., Fastenrath, M., Fatouros-Bergman, H., Forstner, A. J., Frank, J., Gambi, F., Gelao, B., Geschwind, L., Di Giannantonio, M., Di Giorgio, A., Hartman, C. A., Heilmann-Heimbach, S., Herms, S., Hoekstra, P. J., Hoffmann, P., Hoogman, M., Jonsson, E. G., Loos, E., Maggioni, E., Oosterlaan, J., Papalino, M., Rampino, A., Romaniuk, L., Selvaggi, P., Sepede, G., Sonderby, I. E., Spalek, K., Sussmann, J. E., Thompson, P. M., Vasquez, A. A., Vogler, C., Whalley, H., Farde, L., Engberg, G., Erhardt, S., Schwieler, L., Collste, K., Victorsson, P., Malmqvist, A., Hedberg, M., Orhan, F., Cognitive Psychology, IBBA, Behavioural Sciences, Elvira Brattico / Principal Investigator, Department of Psychology and Logopedics, Cognitive Brain Research Unit, Faculty of Medicine, University of Helsinki, General Paediatrics, ARD - Amsterdam Reproduction and Development, Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Clinical Cognitive Neuropsychiatry Research Program (CCNP), Multiscale Imaging of Brain Connectivity, RS: FPN CN 11, Vision, and RS: FPN CN 1
- Subjects
0301 basic medicine ,Male ,Multivariate statistics ,Bipolar Disorder ,SEGMENTATION ,3124 Neurology and psychiatry ,Machine Learning ,0302 clinical medicine ,DEFICITS ,Gray Matter ,Psychiatry ,RISK ,medicine.diagnostic_test ,220 Statistical Imaging Neuroscience ,LIKELIHOOD ESTIMATION ,Middle Aged ,MRI SCANS ,Magnetic Resonance Imaging ,Justice and Strong Institutions ,3. Good health ,Psychiatry and Mental health ,medicine.anatomical_structure ,bipolar disorders ,Schizophrenia ,Female ,brain structural patterns ,MRI ,Adult ,SDG 16 - Peace ,Adolescent ,Brain Structure and Function ,Grey matter ,Psykiatri ,CLASSIFICATION ,Article ,lcsh:RC321-571 ,03 medical and health sciences ,Cellular and Molecular Neuroscience ,Young Adult ,Text mining ,medicine ,Humans ,Bipolar disorder ,lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry ,Biological Psychiatry ,METAANALYSIS ,schizophrenia ,grey matter alterations ,Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7] ,business.industry ,1ST-EPISODE ,SDG 16 - Peace, Justice and Strong Institutions ,Magnetic resonance imaging ,medicine.disease ,030104 developmental biology ,Sample size determination ,Attention Deficit Disorder with Hyperactivity ,Case-Control Studies ,VOLUME ,business ,Neuroscience ,030217 neurology & neurosurgery - Abstract
Contains fulltext : 202693.pdf (Publisher’s version ) (Open Access) Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/ hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients.
- Published
- 2019
4. Visualization of uncertainty in fiber tracking based on diffusion tensor imaging
- Author
-
Brecheisen, R., ter Haar Romeny, Bart M., Vilanova, Anna, Platel, Bram, and Medical Image Analysis
- Abstract
Diffusion tensor imaging (DTI) is an imaging technique based on magnetic resonance that describes, in each point of the tissue, the distribution of diffusing water molecules. The distribution is mathematically modelled using a second-order tensor. In fibrous tissues the diffusion tensor will have an elongated, ellipsoid shape whose main axis is assumed to be aligned with the underlying fiber structure. Fiber tractography traces paths through the tensor field by following each tensor's main direction thereby resulting in a three-dimensional reconstruction of the fibers. This is particularly interesting for the exploration and visualization of neuronal connections in brain white matter and has great potential for applications in neuroscience and neurosurgery. DTI and fiber tractography are unique in that they provide insight into white matter structures in vivo and non-invasively. However, despite these capabilities the application of DTI and fiber tractography in clinical practice remains limited. The image acquisition and post-processing pipeline is complex and consists of many stages. At each stage errors and uncertainties are introduced due to image noise, magnetic distortions, partial volume effects, scanner settings, diffusion model assumptions and user parameters. These uncertainties are propagated through the pipeline and possibly enhanced in subsequent stages thereby leading to potentially unreliable results in the final tractography output. To the user the processing pipeline behaves like a black box whose internal details remain hidden and whose quality of output cannot be reliably assessed. Contrary to standard CT and MR images it is not possible to look at the "raw" diffusion-weighted images. Without further processing the images are practically meaningless. This means the user either has to accept (and trust) the processing output or refrain from using fiber tracking all together. In this thesis we assume that the user has certain reservations about the quality of the tractography output. Unfortunately, there is no gold standard against which the output of tractography can be validated. Consequently, we cannot make definitive statements about the "true" certainty or uncertainty of fiber reconstructions. We can, however, discuss tractography output in terms of stability and reproducibility. The output of tractography algorithms can be subject to large variations. In this thesis we present a number of visualization strategies that make these variations visible to the user and allow a better assessment of the reliability of fiber reconstructions obtained from any given tractography algorithm.
- Published
- 2012
5. Illustrative uncertainty visualization for DTI fiber pathways
- Author
-
Brecheisen, R., Platel, B., Haar Romeny, B.M. ter, Vilanova, Anna, Medical Image Analysis, and Visualization
- Abstract
Diffusion Tensor Imaging (DTI) and fiber tracking provide unique insight into the 3D structure of fibrous tissues in the brain. However, the output of fiber tracking contains a sig- nificant amount of uncertainty accumulated in the various steps of the processing pipeline. Existing DTI visualization methods do not present these uncertainties to the end user. This creates an impression of certainty that can be mislead- ing and even dangerous in applications such as neurosurgery which rely heavily on risk assessment and decision-making. However, adding uncertainty to an already complex visual- ization can easily lead to cognitive overload. In this work we propose illustrative confidence intervals to reduce the com- plexity of the visualization and present only those aspects of uncertainty that are of interest to the user. We look specifi- cally at the uncertainty in fiber shape due to noise and mod- eling errors. Any method that produces a set of streamlines with associated confidence values can be visualized with our framework.
- Published
- 2011
6. GPU-accelerated 3D multimodal visualization techniques for tumor resection in neurosurgery
- Author
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Brecheisen, R., Vilanova, Anna, Platel, B., Haar Romeny, B.M. ter, Medical Image Analysis, and Visualization
- Subjects
genetic structures - Abstract
Brain tumor resections, especially when the tumor is deeply embedded in the brain, are high-risk procedures. The difficulty lies not only in removal of the tumor itself but also in the process of gaining access to it without unnecessarily damaging surrounding tissues and brain structures. To highlight these tissues and structures different image modalities are needed such as CT for bone structures, MRI for brain matter, CT/MRI angiography for blood vessels, fMRI for cortical activation regions and diffusion tensor imaging (DTI) for fiber tracts. Furthermore, to correctly assess the spatial relation between these structures it is important to visualize and interact real time with the image data in 3D. For this purpose, we propose a new rendering algorithm based on Graphics Processing Unit (GPU)-accelerated raycasting and depth peeling to visualize multiple volumetric datasets intersected with an arbitrary number of opaque or semi-transparent geometric models. Such models may represent foreign objects relevant for surgical applications such as virtual surgical tools, 3D pointers, measuring tools or grid lines for spatial orientation.
- Published
- 2008
7. Illustrative uncertainty visualization of DTI fiber pathways
- Author
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Brecheisen, R., primary, Platel, B., additional, ter Haar Romeny, B. M., additional, and Vilanova, A., additional
- Published
- 2012
- Full Text
- View/download PDF
8. Parameter Sensitivity Visualization for DTI Fiber Tracking
- Author
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Brecheisen, R., primary, Vilanova, A., additional, Platel, B., additional, and ter Haar Romeny, B., additional
- Published
- 2009
- Full Text
- View/download PDF
9. Validation of an automated segmentation method for body composition analysis in colorectal cancer patients using diagnostic abdominal computed tomography images.
- Author
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Querido NR, Bours MJL, Brecheisen R, Valkenburg-van Iersel L, Breukink SO, Janssen-Heijnen MLG, Keulen ETP, Konsten JLM, de Vos-Geelen J, Weijenberg MP, and Simons CCJM
- Subjects
- Humans, Male, Female, Aged, Middle Aged, Reproducibility of Results, Muscle, Skeletal diagnostic imaging, Aged, 80 and over, Image Processing, Computer-Assisted, Adult, Intra-Abdominal Fat diagnostic imaging, Abdomen diagnostic imaging, Colorectal Neoplasms diagnostic imaging, Body Composition, Tomography, X-Ray Computed methods
- Abstract
Background & Aims: Several automated programs have been developed to facilitate body composition analysis of images from abdominal computed tomography (CT) scans. External validation in patients with colorectal cancer is necessary for use in research and clinical practice. Our aim was to validate an automatic method (AutoMATiCA) of segmenting CT images at the third lumbar level (L3) from patients with colorectal cancer, by comparing with manual segmentation., Methods: Diagnostic abdominal CT scans of consecutive patients with stage I-III colorectal cancer were analysed to measure cross-sectional areas and tissue densities of skeletal muscle and intra-muscular, visceral, and subcutaneous adipose tissue. Trained analysts performed manual segmentation of L3 CT images using SliceOmatic. Automatic segmentation was performed using AutoMATiCA, an open-source software. The Dice similarity coefficient (DSC) was calculated to assess segmentation accuracy. Agreement of automatic with manual segmentation was evaluated using intra-class correlation coefficients (ICCs) and Bland-Altman plots with limits of agreement., Results: A total of 292 scans were included, of which 62% were from male patients. The agreement of AutoMATiCA with the manual segmentation was excellent, with median DSC values ranging from 0.900 to 0.991 and ICCs above 0.95 for all segmented areas. No systematic deviations were observed in Bland-Altman plots for all segmented areas, with overall narrow limits of agreement., Conclusions: AutoMATiCA provides an accurate segmentation of abdominal CT images from patients with colorectal cancer. Our findings support its use as a highly efficient automated tool for body composition analysis in research and potentially also in clinical practice., Competing Interests: Declaration of competing interest Judith de Vos-Geelen has served as a consultant for Amgen, AstraZeneca, MSD, Pierre Fabre, and Servier, and has received institutional research funding from Servier. All outside the submitted work., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)
- Published
- 2024
- Full Text
- View/download PDF
10. External validation of a deep learning model for automatic segmentation of skeletal muscle and adipose tissue on abdominal computed tomography images.
- Author
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van Dijk DPJ, Volmer LF, Brecheisen R, Martens B, Dolan RD, Bryce AS, Chang DK, McMillan DC, Stoot JHMB, West MA, Rensen SS, Dekker A, Wee L, and Damink SWMO
- Abstract
Background: Body composition assessment using computed tomography (CT) images at the L3-level is increasingly applied in cancer research. Robust high-throughput automated segmentation is key to assess large patient cohorts and to support implementation of body composition analysis into routine clinical practice. We trained and externally validated a deep learning neural network (DLNN) to automatically segment L3-CT images., Methods: Expert-drawn segmentations of visceral and subcutaneous adipose tissue (VAT/SAT) and skeletal muscle (SM) of L3-CT-images of 3,187 patients undergoing abdominal surgery were used to train a DLNN. The external validation cohort was comprised of 2,535 patients with abdominal cancer. DLNN performance was evaluated with (geometric) Dice Similarity (DS) and Lin's Concordance Correlation Coefficient., Results: There was a strong concordance between automatic and manual segmentations with median DS for SM, VAT, and SAT of 0.97 (interquartile range, IQR: 0.95-0.98), 0.98 (IQR: 0.95-0.98), and 0.95 (IQR: 0.92-0.97), respectively. Concordance correlations were excellent: SM 0.964 (0.959-0.968), VAT 0.998 (0.998-0.998), and SAT 0.992 (0.991-0.993). Bland-Altman metrics indicated only small and clinically insignificant systematic offsets; SM radiodensity: 0.23 hounsfield units (0.5%), SM: 1.26 cm2.m-2 (2.8%), VAT: -1.02 cm2.m-2 (1.7%), and SAT: 3.24 cm2.m-2 (4.6%)., Conclusion: A robustly-performing and independently externally validated DLNN for automated body composition analysis was developed., Advances in Knowledge: CT-based body composition analysis is highly prognostic for long-term overall survival in oncology. This DLNN was succesfully trained and externally validated on several large patient cohorts and will therefore enable large scale population studies and implementation of body composition analysis into clinical practice., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology.)
- Published
- 2024
- Full Text
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11. Identifying radiomics signatures in body composition imaging for the prediction of outcome following pancreatic cancer resection.
- Author
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van der Kroft G, Wee L, Rensen SS, Brecheisen R, van Dijk DPJ, Eickhoff R, Roeth AA, Ulmer FT, Dekker A, Neumann UP, and Olde Damink SWM
- Abstract
Background: Computerized radiological image analysis (radiomics) enables the investigation of image-derived phenotypes by extracting large numbers of quantitative features. We hypothesized that radiomics features may contain prognostic information that enhances conventional body composition analysis. We aimed to investigate whether body composition-associated radiomics features hold additional value over conventional body composition analysis and clinical patient characteristics used to predict survival of pancreatic ductal adenocarcinoma (PDAC) patients., Methods: Computed tomography images of 304 patients undergoing elective pancreatic cancer resection were analysed. 2D radiomics features were extracted from skeletal muscle and subcutaneous and visceral adipose tissue (SAT and VAT) compartments from a single slice at the third lumbar vertebra. The study population was randomly split (80:20) into training and holdout subsets. Feature ranking with Least Absolute Shrinkage Selection Operator (LASSO) followed by multivariable stepwise Cox regression in 1000 bootstrapped re-samples of the training data was performed and tested on the holdout data. The fitted regression predictors were used as "scores" for a clinical (C-Score), body composition (B-Score), and radiomics (R-Score) model. To stratify patients into the highest 25% and lowest 25% risk of mortality compared to the middle 50%, the Harrell Concordance Index was used., Results: Based on LASSO and stepwise cox regression for overall survival, ASA ≥3 and age were the most important clinical variables and constituted the C-score, and VAT-index (VATI) was the most important body composition variable and constituted the B-score. Three radiomics features (SATI_original_shape2D_Perimeter, VATI_original_glszm_SmallAreaEmphasis, and VATI_original_firstorder_Maximum) emerged as the most frequent set of features and yielded an R-Score. Of the mean concordance indices of C-, B-, and R-scores, R-score performed best (0.61, 95% CI 0.56-0.65, p<0.001), followed by the C-score (0.59, 95% CI 0.55-0.63, p<0.001) and B-score (0.55, 95% CI 0.50-0.60, p=0.03). Kaplan-Meier projection revealed that C-, B, and R-scores showed a clear split in the survival curves in the training set, although none remained significant in the holdout set., Conclusion: It is feasible to implement a data-driven radiomics approach to body composition imaging. Radiomics features provided improved predictive performance compared to conventional body composition variables for the prediction of overall survival of PDAC patients undergoing primary resection., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 van der Kroft, Wee, Rensen, Brecheisen, van Dijk, Eickhoff, Roeth, Ulmer, Dekker, Neumann and Olde Damink.)
- Published
- 2023
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12. Clinical evaluation of automated segmentation for body composition analysis on abdominal L3 CT slices in polytrauma patients.
- Author
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Ackermans LLGC, Volmer L, Timmermans QMMA, Brecheisen R, Damink SMWO, Dekker A, Loeffen D, Poeze M, Blokhuis TJ, Wee L, and Ten Bosch JA
- Subjects
- Body Composition, Humans, Subcutaneous Fat, Tomography, X-Ray Computed, Multiple Trauma diagnostic imaging, Sarcopenia
- Abstract
Introduction: Sarcopenia is a muscle disease that involves loss of muscle strength and physical function and is associated with adverse health effects. Even though sarcopenia has attracted increasing attention in the literature, many research findings have not yet been translated into clinical practice. In this article, we aim to validate a deep learning neural network for automated segmentation of L3 CT slices and aim to explore the potential for clinical utilization of such a tool for clinical practice., Materials and Methods: A deep learning neural network was trained on a multi-centre collection of 3413 abdominal cancer surgery subjects to automatically segment muscle, subcutaneous and visceral adipose tissue at the L3 lumbar vertebral level. 536 Polytrauma subjects were used as an independent test set to show generalizability. The Dice Similarity Coefficient was calculated to validate the geometric similarity. Quantitative agreement was quantified using Bland-Altman's Limits of Agreement interval and Lin's Concordance Correlation Coefficient. To determine the potential clinical usability, randomly selected segmentation images were presented to a panel of experienced clinicians to rate on a Likert scale., Results: Deep learning results gave excellent agreement versus a human expert operator for all of the body composition indices, with Concordance Correlation Coefficient for skeletal muscle index of 0.92, Skeletal muscle radiation attenuation 0.94, Visceral Adipose Tissue index 0.99 and Subcutaneous Adipose Tissue Index 0.99. Triple-blinded visual assessment of segmentation by clinicians correlated only to the Dice coefficient, but had no association to quantitative body composition metrics which were accurate irrespective of clinicians' visual rating., Conclusion: A deep learning method for automatic segmentation of truncal muscle, visceral and subcutaneous adipose tissue on individual L3 CT slices has been independently validated against expert human-generated results for an enlarged polytrauma registry dataset. Time efficiency, consistency and high accuracy relative to human experts suggest that quantitative body composition analysis with deep learning should is a promising tool for clinical application in a hospital setting., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses r interpretation of data; in the writing of the manuscript, or in the decision to publish the results., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)
- Published
- 2022
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13. Strategies to Avoid Artifacts in Mass Spectrometry-Based Epitranscriptome Analyses.
- Author
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Kaiser S, Byrne SR, Ammann G, Asadi Atoi P, Borland K, Brecheisen R, DeMott MS, Gehrke T, Hagelskamp F, Heiss M, Yoluç Y, Liu L, Zhang Q, Dedon PC, Cao B, and Kellner S
- Subjects
- Animals, Humans, Mass Spectrometry, Mice, Nucleic Acid Conformation, Escherichia coli chemistry, Phosphorothioate Oligonucleotides analysis, Saccharomyces cerevisiae chemistry
- Abstract
In this report, we perform structure validation of recently reported RNA phosphorothioate (PT) modifications, a new set of epitranscriptome marks found in bacteria and eukaryotes including humans. By comparing synthetic PT-containing diribonucleotides with native species in RNA hydrolysates by high-resolution mass spectrometry (MS), metabolic stable isotope labeling, and PT-specific iodine-desulfurization, we disprove the existence of PTs in RNA from E. coli, S. cerevisiae, human cell lines, and mouse brain. Furthermore, we discuss how an MS artifact led to the initial misidentification of 2'-O-methylated diribonucleotides as RNA phosphorothioates. To aid structure validation of new nucleic acid modifications, we present a detailed guideline for MS analysis of RNA hydrolysates, emphasizing how the chosen RNA hydrolysis protocol can be a decisive factor in discovering and quantifying RNA modifications in biological samples., (© 2021 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.)
- Published
- 2021
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14. CT-Based Prediction of Liver Function and Post-PVE Hypertrophy Using an Artificial Neural Network.
- Author
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Heise D, Schulze-Hagen M, Bednarsch J, Eickhoff R, Kroh A, Bruners P, Eickhoff SB, Brecheisen R, Ulmer F, and Neumann UP
- Abstract
Background: This study aimed to evaluate whether hypertrophy after portal vein embolization (PVE) and maximum liver function capacity (LiMAx) are predictable by an artificial neural network (ANN) model based on computed tomography (CT) texture features., Methods: We report a retrospective analysis on 118 patients undergoing preoperative assessment by CT before and after PVE for subsequent extended liver resection due to a malignant tumor at RWTH Aachen University Hospital. The LiMAx test was carried out in a subgroup of 55 patients prior to PVE. Associations between CT texture features and hypertrophy as well as liver function were assessed by a multilayer perceptron ANN model., Results: Liver volumetry showed a median hypertrophy degree of 33.9% (16.5-60.4%) after PVE. Non-response, defined as a hypertrophy grade lower than 25%, was found in 36.5% (43/118) of the cases. The ANN prediction of the hypertrophy response showed a sensitivity of 95.8%, specificity of 44.4% and overall prediction accuracy of 74.6% ( p < 0.001). The observed median LiMAx was 327 (248-433) μg/kg/h and was strongly correlated with the predicted LiMAx (R
2 = 0.89)., Conclusion: Our study shows that an ANN model based on CT texture features is able to predict the maximum liver function capacity and may be useful to assess potential hypertrophy after performing PVE.- Published
- 2021
- Full Text
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15. Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients.
- Author
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Ackermans LLGC, Volmer L, Wee L, Brecheisen R, Sánchez-González P, Seiffert AP, Gómez EJ, Dekker A, Ten Bosch JA, Olde Damink SMW, and Blokhuis TJ
- Subjects
- Adipose Tissue diagnostic imaging, Cross-Sectional Studies, Humans, Muscle, Skeletal diagnostic imaging, Tomography, X-Ray Computed, Deep Learning, Multiple Trauma diagnostic imaging
- Abstract
Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection and quantification of sarcopenia. A prototype deep learning neural network was trained on a multi-center collection of 3413 abdominal cancer surgery subjects to automatically segment truncal muscle, subcutaneous adipose tissue and visceral adipose tissue at the L3 lumbar vertebral level. Segmentations were externally tested on 233 polytrauma subjects. Although after severe trauma abdominal CT scans are quickly and robustly delivered, with often motion or scatter artefacts, incomplete vertebral bodies or arms that influence image quality, the concordance was generally very good for the body composition indices of Skeletal Muscle Radiation Attenuation (SMRA) (Concordance Correlation Coefficient (CCC) = 0.92), Visceral Adipose Tissue index (VATI) (CCC = 0.99) and Subcutaneous Adipose Tissue Index (SATI) (CCC = 0.99). In conclusion, this article showed an automated and accurate segmentation system to segment the cross-sectional muscle and adipose area L3 lumbar spine level on abdominal CT. Future perspectives will include fine-tuning the algorithm and minimizing the outliers.
- Published
- 2021
- Full Text
- View/download PDF
16. Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder.
- Author
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Schwarz E, Doan NT, Pergola G, Westlye LT, Kaufmann T, Wolfers T, Brecheisen R, Quarto T, Ing AJ, Di Carlo P, Gurholt TP, Harms RL, Noirhomme Q, Moberget T, Agartz I, Andreassen OA, Bellani M, Bertolino A, Blasi G, Brambilla P, Buitelaar JK, Cervenka S, Flyckt L, Frangou S, Franke B, Hall J, Heslenfeld DJ, Kirsch P, McIntosh AM, Nöthen MM, Papassotiropoulos A, de Quervain DJ, Rietschel M, Schumann G, Tost H, Witt SH, Zink M, and Meyer-Lindenberg A
- Subjects
- Adolescent, Adult, Attention Deficit Disorder with Hyperactivity diagnostic imaging, Bipolar Disorder diagnostic imaging, Case-Control Studies, Female, Gray Matter diagnostic imaging, Humans, Machine Learning, Magnetic Resonance Imaging, Male, Middle Aged, Schizophrenia diagnostic imaging, Young Adult, Attention Deficit Disorder with Hyperactivity physiopathology, Bipolar Disorder physiopathology, Gray Matter physiopathology, Schizophrenia physiopathology
- Abstract
Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/ hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients.
- Published
- 2019
- Full Text
- View/download PDF
17. "Look at my classifier's result": Disentangling unresponsive from (minimally) conscious patients.
- Author
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Noirhomme Q, Brecheisen R, Lesenfants D, Antonopoulos G, and Laureys S
- Subjects
- Consciousness Disorders classification, Consciousness Disorders diagnostic imaging, Consciousness Disorders physiopathology, Humans, Consciousness Disorders diagnosis, Machine Learning
- Abstract
Given the fact that clinical bedside examinations can have a high rate of misdiagnosis, machine learning techniques based on neuroimaging and electrophysiological measurements are increasingly being considered for comatose patients and patients with unresponsive wakefulness syndrome, a minimally conscious state or locked-in syndrome. Machine learning techniques have the potential to move from group-level statistical results to personalized predictions in a clinical setting. They have been applied for the purpose of (1) detecting changes in brain activation during functional tasks, equivalent to a behavioral command-following test and (2) estimating signs of consciousness by analyzing measurement data obtained from multiple subjects in resting state. In this review, we provide a comprehensive overview of the literature on both approaches and discuss the translation of present findings to clinical practice. We found that most studies struggle with the difficulty of establishing a reliable behavioral assessment and fluctuations in the patient's levels of arousal. Both these factors affect the training and validation of machine learning methods to a considerable degree. In studies involving more than 50 patients, small to moderate evidence was found for the presence of signs of consciousness or good outcome, where one study even showed strong evidence for good outcome., (Copyright © 2015 Elsevier Inc. All rights reserved.)
- Published
- 2017
- Full Text
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18. Validating Paediatric Morphometrics: body proportion measurement using photogrammetric anthropometry.
- Author
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Penders B, Brecheisen R, Gerver A, van Zonneveld G, and Gerver WJ
- Subjects
- Adolescent, Child, Child, Preschool, Cross-Sectional Studies, Female, Humans, Male, Software, Anthropometry methods, Body Height physiology
- Abstract
Background: Taking multiple anthropometric measurements for the description of body proportions in an accurate way is a time-consuming procedure that requires specific tools and skills. This is why we developed an alternative method based on digital photography for taking these measurements which is faster and easier to use, to make anthropometry more user-friendly and approachable to paediatricians., Methods: We conducted a cross-sectional study in 54 children between 2 and 18 years of age. We compared manual measurements with photogrammetric measurements to validate our method., Results: Inter-observer correlations of all measurements are ≥0.96 and mean differences are 0.3-0.9 cm, except for arm span. Comparison of manual to photogrammetric measurements shows mean differences of 0.6-1.3 cm, with correlations ≥0.92, except for sitting height and arm span. Correlations of ratios between methods are height/sitting height (r=0.77), biacromium/biiliacum (r=0.74) and subischial leg length/sitting height (r=0.75)., Conclusion: Photogrammetric anthropometry is faster, easier to use and provides the paediatrician with more flexibility as taking the digital photographs and performing the analysis are separated.
- Published
- 2015
- Full Text
- View/download PDF
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