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51. GBM Volumetry using the 3D Slicer Medical Image Computing Platform

Author

Egger, Jan, Kapur, Tina, Fedorov, Andriy, Pieper, Steve, Miller, James V., Veeraraghavan, Harini, Freisleben, Bernd, Golby, Alexandra, Nimsky, Christopher, and Kikinis, Ron

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Volumetric change in glioblastoma multiforme (GBM) over time is a critical factor in treatment decisions. Typically, the tumor volume is computed on a slice-by-slice basis using MRI scans obtained at regular intervals. (3D)Slicer - a free platform for biomedical research - provides an alternative to this manual slice-by-slice segmentation process, which is significantly faster and requires less user interaction. In this study, 4 physicians segmented GBMs in 10 patients, once using the competitive region-growing based GrowCut segmentation module of Slicer, and once purely by drawing boundaries completely manually on a slice-by-slice basis. Furthermore, we provide a variability analysis for three physicians for 12 GBMs. The time required for GrowCut segmentation was on an average 61% of the time required for a pure manual segmentation. A comparison of Slicer-based segmentation with manual slice-by-slice segmentation resulted in a Dice Similarity Coefficient of 88.43 +/- 5.23% and a Hausdorff Distance of 2.32 +/- 5.23 mm., Comment: 7 pages, 6 figures, 2 tables, 1 equation, 43 references

Published
2013
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52. Pituitary Adenoma Volumetry with 3D Slicer

Author

Egger, Jan, Kapur, Tina, Nimsky, Christopher, and Kikinis, Ron

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In this study, we present pituitary adenoma volumetry using the free and open source medical image computing platform for biomedical research: (3D) Slicer. Volumetric changes in cerebral pathologies like pituitary adenomas are a critical factor in treatment decisions by physicians and in general the volume is acquired manually. Therefore, manual slice-by-slice segmentations in magnetic resonance imaging (MRI) data, which have been obtained at regular intervals, are performed. In contrast to this manual time consuming slice-by-slice segmentation process Slicer is an alternative which can be significantly faster and less user intensive. In this contribution, we compare pure manual segmentations of ten pituitary adenomas with semi-automatic segmentations under Slicer. Thus, physicians drew the boundaries completely manually on a slice-by-slice basis and performed a Slicer-enhanced segmentation using the competitive region-growing based module of Slicer named GrowCut. Results showed that the time and user effort required for GrowCut-based segmentations were on average about thirty percent less than the pure manual segmentations. Furthermore, we calculated the Dice Similarity Coefficient (DSC) between the manual and the Slicer-based segmentations to proof that the two are comparable yielding an average DSC of 81.97\pm3.39%., Comment: 7 pages, 5 figures, 2 tables, 30 references

Published
2012
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54. Markov Random Field Segmentation of Brain MR Images

Author

Held, Karsten, Kops, Elena Rota, Krause, Bernd J., Wells III, William M., Kikinis, Ron, and Mueller-Gaertner, Hans-Wilhelm

Subjects
Computer Science - Computer Vision and Pattern Recognition, Condensed Matter - Statistical Mechanics, Physics - Data Analysis, Statistics and Probability, Physics - Medical Physics
Abstract

We describe a fully-automatic 3D-segmentation technique for brain MR images. Using Markov random fields the segmentation algorithm captures three important MR features, i.e. non-parametric distributions of tissue intensities, neighborhood correlations and signal inhomogeneities. Detailed simulations and real MR images demonstrate the performance of the segmentation algorithm. The impact of noise, inhomogeneity, smoothing and structure thickness is analyzed quantitatively. Even single echo MR images are well classified into gray matter, white matter, cerebrospinal fluid, scalp-bone and background. A simulated annealing and an iterated conditional modes implementation are presented. Keywords: Magnetic Resonance Imaging, Segmentation, Markov Random Fields, Comment: 34 pages, 10 figures; the paper (published in 1997) has introduced the concept of Markov random field to the segmentation of medical MR images. For the published version see http://dx.doi.org/10.1109/42.650883

Published
2009
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55. Errors in Quantitative Image Analysis due to Platform-Dependent Image Scaling

Author

Chenevert, Thomas L, Malyarenko, Dariya I, Newitt, David, Li, Xin, Jayatilake, Mohan, Tudorica, Alina, Fedorov, Andriy, Kikinis, Ron, Liu, Tiffany Ting, Muzi, Mark, Oborski, Matthew J, Laymon, Charles M, Li, Xia, Thomas, Yankeelov, Jayashree, Kalpathy-Cramer, Mountz, James M, Kinahan, Paul E, Rubin, Daniel L, Fennessy, Fiona, Huang, Wei, Hylton, Nola, and Ross, Brian D

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Biomedical Imaging, Bioengineering, Biochemistry and Cell Biology, Clinical Sciences, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

PurposeTo evaluate the ability of various software (SW) tools used for quantitative image analysis to properly account for source-specific image scaling employed by magnetic resonance imaging manufacturers.MethodsA series of gadoteridol-doped distilled water solutions (0%, 0.5%, 1%, and 2% volume concentrations) was prepared for manual substitution into one (of three) phantom compartments to create "variable signal," whereas the other two compartments (containing mineral oil and 0.25% gadoteriol) were held unchanged. Pseudodynamic images were acquired over multiple series using four scanners such that the histogram of pixel intensities varied enough to provoke variable image scaling from series to series. Additional diffusion-weighted images were acquired of an ice-water phantom to generate scanner-specific apparent diffusion coefficient (ADC) maps. The resulting pseudodynamic images and ADC maps were analyzed by eight centers of the Quantitative Imaging Network using 16 different SW tools to measure compartment-specific region-of-interest intensity.ResultsImages generated by one of the scanners appeared to have additional intensity scaling that was not accounted for by the majority of tested quantitative image analysis SW tools. Incorrect image scaling leads to intensity measurement bias near 100%, compared to nonscaled images.ConclusionCorrective actions for image scaling are suggested for manufacturers and quantitative imaging community.

Published
2014

57. Correction: Three-Dimensional Digital Reconstruction of the Cerebellar Cortex: Lobule Thickness, Surface Area Measurements, and Layer Architecture

Author

Zheng, Junxiao, primary, Yang, Qinzhu, additional, Makris, Nikos, additional, Huang, Kaibin, additional, Liang, Jianwen, additional, Ye, Chenfei, additional, Yu, Xiaxia, additional, Tian, Mu, additional, Ma, Ting, additional, Mou, Tian, additional, Guo, Wenlong, additional, Kikinis, Ron, additional, and Gao, Yi, additional

Published
2023
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62. Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction

Author

Irimia, Andrei, Wang, Bo, Aylward, Stephen R, Prastawa, Marcel W, Pace, Danielle F, Gerig, Guido, Hovda, David A, Kikinis, Ron, Vespa, Paul M, and Van Horn, John D

Subjects
Pediatric, Traumatic Head and Spine Injury, Clinical Research, Neurosciences, Brain Disorders, Traumatic Brain Injury (TBI), Biomedical Imaging, Childhood Injury, Physical Injury - Accidents and Adverse Effects, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, Neurological, Trauma, Neuroimaging, MRI/fMRI, Diffusion tensor, Outcome measures, 3D, three-dimensional, AAL, Automatic Anatomical Labeling, ADC, apparent diffusion coefficient, ANTS, Advanced Normalization ToolS, BOLD, blood oxygen level dependent, CC, corpus callosum, CT, computed tomography, DAI, diffuse axonal injury, DSI, diffusion spectrum imaging, DTI, diffusion tensor imaging, DWI, diffusion weighted imaging, FA, fractional anisotropy, FLAIR, Fluid Attenuated Inversion Recovery, FSE, Functional Status Examination, GCS, Glasgow Coma Score, GM, gray matter, GOS, Glasgow Outcome Score, GRE, Gradient Recalled Echo, HARDI, high-angular-resolution diffusion imaging, IBA, Individual Brain Atlas, LDA, linear discriminant analysis, MRI, magnetic resonance imaging, NINDS, National Institute of Neurological Disorders and Stroke, PCA, principal component analysis, PROMO, PROspective MOtion Correction, SPM, Statistical Parametric Mapping, SWI, Susceptibility Weighted Imaging, TBI, traumatic brain injury, TBSS, tract-based spatial statistics, WM, white matter, fMRI, functional magnetic resonance imaging
Abstract

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome.

Published
2012

63. Patient-Tailored Connectomics Visualization for the Assessment of White Matter Atrophy in Traumatic Brain Injury

Author

Irimia, Andrei, Chambers, Micah C, Torgerson, Carinna M, Filippou, Maria, Hovda, David A, Alger, Jeffry R, Gerig, Guido, Toga, Arthur W, Vespa, Paul M, Kikinis, Ron, and Van Horn, John D

Subjects
Physical Injury - Accidents and Adverse Effects, Brain Disorders, Traumatic Brain Injury (TBI), Neurosciences, Traumatic Head and Spine Injury, Injuries and accidents, Neurological, Good Health and Well Being, DTI, atrophy, connectomics, rehabilitation, traumatic brain injury, Clinical Sciences, Psychology
Abstract

Available approaches to the investigation of traumatic brain injury (TBI) are frequently hampered, to some extent, by the unsatisfactory abilities of existing methodologies to efficiently define and represent affected structural connectivity and functional mechanisms underlying TBI-related pathology. In this paper, we describe a patient-tailored framework which allows mapping and characterization of TBI-related structural damage to the brain via multimodal neuroimaging and personalized connectomics. Specifically, we introduce a graphically driven approach for the assessment of trauma-related atrophy of white matter connections between cortical structures, with relevance to the quantification of TBI chronic case evolution. This approach allows one to inform the formulation of graphical neurophysiological and neuropsychological TBI profiles based on the particular structural deficits of the affected patient. In addition, it allows one to relate the findings supplied by our workflow to the existing body of research that focuses on the functional roles of the cortical structures being targeted. A graphical means for representing patient TBI status is relevant to the emerging field of personalized medicine and to the investigation of neural atrophy.

Published
2012

65. From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 78 COVID-19 Cases

Author

Bumm, Rudolf, primary, Zaffino, Paolo, additional, Lasso, Andras, additional, Estépar, Raúl San José, additional, Pieper, Steven, additional, Wasserthal, Jakob, additional, Spadea, Maria Francesca, additional, Latshang, Tsogyal, additional, Kawel-Böhm, Nadine, additional, Wäckerlin, Adrian, additional, Werner, Raphael, additional, Hässig, Gabriela, additional, Furrer, Markus, additional, and Kikinis, Ron, additional

Published
2023
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66. From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 81 COVID-19 Cases

Author

Bumm, Rudolf, primary, Zaffino, Paolo, additional, Lasso, Andras, additional, Estépar, Raúl San José, additional, Pieper, Steven, additional, Wasserthal, Jakob, additional, Spadea, Maria Francesca, additional, Latshang, Tsogyal, additional, Kawel-Böhm, Nadine, additional, Wäckerlin, Adrian, additional, Werner, Raphael, additional, Hässig, Gabriela, additional, Furrer, Markus, additional, and Kikinis, Ron, additional

Published
2023
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69. Content-Based Retrieval of Brain Diffusion Magnetic Resonance Image

Author

Liu, Siqi, Hadi, Nur, Liu, Sidong, Pujol, Sonia, Kikinis, Ron, Zhang, Fan, Feng, Dagan, Cai, Weidong, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Müller, Henning, editor, Jimenez del Toro, Oscar Alfonso, editor, Hanbury, Allan, editor, Langs, Georg, editor, and Foncubierta Rodriguez, Antonio, editor

Published
2015
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70. Multi-Phase Feature Representation Learning for Neurodegenerative Disease Diagnosis

Author

Liu, Siqi, Liu, Sidong, Cai, Weidong, Pujol, Sonia, Kikinis, Ron, Feng, David Dagan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Siekmann, Jörg, Series editor, Chalup, Stephan K., editor, Blair, Alan D., editor, and Randall, Marcus, editor

Published
2015
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71. DEEPRGVP: A novel microstructure-informed supervised contrastive learning framework for automated identification of the retinogeniculate pathway using dMRI tractography

Author

Li, Sipei, primary, He, Jianzhong, additional, Xue, Tengfei, additional, Xie, Guoqiang, additional, Yao, Shun, additional, Chen, Yuqian, additional, Torio, Erickson F., additional, Feng, Yuanjing, additional, Bastos, Dhiego CA, additional, Rathi, Yogesh, additional, Makris, Nikos, additional, Kikinis, Ron, additional, Bi, Wenya Linda, additional, Golby, Alexandra J, additional, O’Donnell, Lauren J, additional, and Zhang, Fan, additional

Published
2023
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74. Video 1. SlicerDMRI Quick Tutorial from SlicerDMRI: Open Source Diffusion MRI Software for Brain Cancer Research

Author

Norton, Isaiah, primary, Essayed, Walid Ibn, primary, Zhang, Fan, primary, Pujol, Sonia, primary, Yarmarkovich, Alex, primary, Golby, Alexandra J., primary, Kindlmann, Gordon, primary, Wassermann, Demian, primary, Estepar, Raul San Jose, primary, Rathi, Yogesh, primary, Pieper, Steve, primary, Kikinis, Ron, primary, Johnson, Hans J., primary, Westin, Carl-Fredrik, primary, and O'Donnell, Lauren J., primary

Published
2023
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75. Data from SlicerDMRI: Open Source Diffusion MRI Software for Brain Cancer Research

Author

Norton, Isaiah, primary, Essayed, Walid Ibn, primary, Zhang, Fan, primary, Pujol, Sonia, primary, Yarmarkovich, Alex, primary, Golby, Alexandra J., primary, Kindlmann, Gordon, primary, Wassermann, Demian, primary, Estepar, Raul San Jose, primary, Rathi, Yogesh, primary, Pieper, Steve, primary, Kikinis, Ron, primary, Johnson, Hans J., primary, Westin, Carl-Fredrik, primary, and O'Donnell, Lauren J., primary

Published
2023
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76. Supplemental video 2 from NCI Imaging Data Commons

Author

Fedorov, Andrey, primary, Longabaugh, William J.R., primary, Pot, David, primary, Clunie, David A., primary, Pieper, Steve, primary, Aerts, Hugo J.W.L., primary, Homeyer, André, primary, Lewis, Rob, primary, Akbarzadeh, Afshin, primary, Bontempi, Dennis, primary, Clifford, William, primary, Herrmann, Markus D., primary, Höfener, Henning, primary, Octaviano, Igor, primary, Osborne, Chad, primary, Paquette, Suzanne, primary, Petts, James, primary, Punzo, Davide, primary, Reyes, Madelyn, primary, Schacherer, Daniela P., primary, Tian, Mi, primary, White, George, primary, Ziegler, Erik, primary, Shmulevich, Ilya, primary, Pihl, Todd, primary, Wagner, Ulrike, primary, Farahani, Keyvan, primary, and Kikinis, Ron, primary

Published
2023
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77. Supplemental video 1 from NCI Imaging Data Commons

Author

Fedorov, Andrey, primary, Longabaugh, William J.R., primary, Pot, David, primary, Clunie, David A., primary, Pieper, Steve, primary, Aerts, Hugo J.W.L., primary, Homeyer, André, primary, Lewis, Rob, primary, Akbarzadeh, Afshin, primary, Bontempi, Dennis, primary, Clifford, William, primary, Herrmann, Markus D., primary, Höfener, Henning, primary, Octaviano, Igor, primary, Osborne, Chad, primary, Paquette, Suzanne, primary, Petts, James, primary, Punzo, Davide, primary, Reyes, Madelyn, primary, Schacherer, Daniela P., primary, Tian, Mi, primary, White, George, primary, Ziegler, Erik, primary, Shmulevich, Ilya, primary, Pihl, Todd, primary, Wagner, Ulrike, primary, Farahani, Keyvan, primary, and Kikinis, Ron, primary

Published
2023
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78. Supplemental video 4 from NCI Imaging Data Commons

Author

Fedorov, Andrey, primary, Longabaugh, William J.R., primary, Pot, David, primary, Clunie, David A., primary, Pieper, Steve, primary, Aerts, Hugo J.W.L., primary, Homeyer, André, primary, Lewis, Rob, primary, Akbarzadeh, Afshin, primary, Bontempi, Dennis, primary, Clifford, William, primary, Herrmann, Markus D., primary, Höfener, Henning, primary, Octaviano, Igor, primary, Osborne, Chad, primary, Paquette, Suzanne, primary, Petts, James, primary, Punzo, Davide, primary, Reyes, Madelyn, primary, Schacherer, Daniela P., primary, Tian, Mi, primary, White, George, primary, Ziegler, Erik, primary, Shmulevich, Ilya, primary, Pihl, Todd, primary, Wagner, Ulrike, primary, Farahani, Keyvan, primary, and Kikinis, Ron, primary

Published
2023
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79. Supplemental video 3 from NCI Imaging Data Commons

Author

Fedorov, Andrey, primary, Longabaugh, William J.R., primary, Pot, David, primary, Clunie, David A., primary, Pieper, Steve, primary, Aerts, Hugo J.W.L., primary, Homeyer, André, primary, Lewis, Rob, primary, Akbarzadeh, Afshin, primary, Bontempi, Dennis, primary, Clifford, William, primary, Herrmann, Markus D., primary, Höfener, Henning, primary, Octaviano, Igor, primary, Osborne, Chad, primary, Paquette, Suzanne, primary, Petts, James, primary, Punzo, Davide, primary, Reyes, Madelyn, primary, Schacherer, Daniela P., primary, Tian, Mi, primary, White, George, primary, Ziegler, Erik, primary, Shmulevich, Ilya, primary, Pihl, Todd, primary, Wagner, Ulrike, primary, Farahani, Keyvan, primary, and Kikinis, Ron, primary

Published
2023
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80. Data from NCI Imaging Data Commons

Author

Fedorov, Andrey, primary, Longabaugh, William J.R., primary, Pot, David, primary, Clunie, David A., primary, Pieper, Steve, primary, Aerts, Hugo J.W.L., primary, Homeyer, André, primary, Lewis, Rob, primary, Akbarzadeh, Afshin, primary, Bontempi, Dennis, primary, Clifford, William, primary, Herrmann, Markus D., primary, Höfener, Henning, primary, Octaviano, Igor, primary, Osborne, Chad, primary, Paquette, Suzanne, primary, Petts, James, primary, Punzo, Davide, primary, Reyes, Madelyn, primary, Schacherer, Daniela P., primary, Tian, Mi, primary, White, George, primary, Ziegler, Erik, primary, Shmulevich, Ilya, primary, Pihl, Todd, primary, Wagner, Ulrike, primary, Farahani, Keyvan, primary, and Kikinis, Ron, primary

Published
2023
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83. Patient-Specific Meshless Model for Whole-Body Image Registration

Author

Li, Mao, Miller, Karol, Joldes, Grand, Kikinis, Ron, Wittek, Adam, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bello, Fernando, editor, and Cotin, Stéphane, editor

Published
2014
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88. Facial Nerve Tractography Using Diffusion MRI: A Comparison of Acquisition B-Values and Single- and Multi-Fiber Tracking Strategies

Author

Epprecht, Lorenz, primary, Zekelman, Leo R., additional, Reinshagen, Katherine L., additional, Xie, Guoqiang, additional, Norton, Isaiah, additional, Kikinis, Ron, additional, Makris, Nikos, additional, Piccirelli, Marco, additional, Huber, Alexander M., additional, Lee, Daniel J., additional, Zhang, Fan, additional, and O'Donnell, Lauren J., additional

Published
2023
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98. On Describing Human White Matter Anatomy: The White Matter Query Language

Author

Wassermann, Demian, Makris, Nikos, Rathi, Yogesh, Shenton, Martha, Kikinis, Ron, Kubicki, Marek, Westin, Carl-Fredrik, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Mori, Kensaku, editor, Sakuma, Ichiro, editor, Sato, Yoshinobu, editor, Barillot, Christian, editor, and Navab, Nassir, editor

Published
2013
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99. Multifold Bayesian Kernelization in Alzheimer’s Diagnosis

Author

Liu, Sidong, Song, Yang, Cai, Weidong, Pujol, Sonia, Kikinis, Ron, Wang, Xiaogang, Feng, Dagan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Mori, Kensaku, editor, Sakuma, Ichiro, editor, Sato, Yoshinobu, editor, Barillot, Christian, editor, and Navab, Nassir, editor

Published
2013
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