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26 results on '"Fleishman, Greg"'

1. Fast Predictive Simple Geodesic Regression

Author

Ding, Zhipeng, Fleishman, Greg, Yang, Xiao, Thompson, Paul, Kwitt, Roland, and Niethammer, Marc

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Deformable image registration and regression are important tasks in medical image analysis. However, they are computationally expensive, especially when analyzing large-scale datasets that contain thousands of images. Hence, cluster computing is typically used, making the approaches dependent on such computational infrastructure. Even larger computational resources are required as study sizes increase. This limits the use of deformable image registration and regression for clinical applications and as component algorithms for other image analysis approaches. We therefore propose using a fast predictive approach to perform image registrations. In particular, we employ these fast registration predictions to approximate a simplified geodesic regression model to capture longitudinal brain changes. The resulting method is orders of magnitude faster than the standard optimization-based regression model and hence facilitates large-scale analysis on a single graphics processing unit (GPU). We evaluate our results on 3D brain magnetic resonance images (MRI) from the ADNI datasets., Comment: 19 pages, 10 figures, 13 tables

Published
2017

4. Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentation

Author

Fleishman, Greg M., Valcarcel, Alessandra, Pham, Dzung L., Roy, Snehashis, Calabresi, Peter A., Yushkevich, Paul, Shinohara, Russell T., Oguz, Ipek, 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, Crimi, Alessandro, editor, Bakas, Spyridon, editor, Kuijf, Hugo, editor, Menze, Bjoern, editor, and Reyes, Mauricio, editor

Published
2018
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5. Fast Predictive Simple Geodesic Regression

Author

The Alzheimer’s Disease Neuroimaging Initiative, Ding, Zhipeng, Fleishman, Greg, Yang, Xiao, Thompson, Paul, Kwitt, Roland, Niethammer, Marc, 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, Cardoso, M. Jorge, editor, Arbel, Tal, editor, Carneiro, Gustavo, editor, Syeda-Mahmood, Tanveer, editor, Tavares, João Manuel R.S., editor, Moradi, Mehdi, editor, Bradley, Andrew, editor, Greenspan, Hayit, editor, Papa, João Paulo, editor, Madabhushi, Anant, editor, Nascimento, Jacinto C., editor, Cardoso, Jaime S., editor, Belagiannis, Vasileios, editor, and Lu, Zhi, editor

Published
2017
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6. Symmetric Interleaved Geodesic Shooting in Diffeomorphisms

Author

Fleishman, Greg M., Thomas Fletcher, P., Thompson, Paul M., 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, Niethammer, Marc, editor, Styner, Martin, editor, Aylward, Stephen, editor, Zhu, Hongtu, editor, Oguz, Ipek, editor, Yap, Pew-Thian, editor, and Shen, Dinggang, editor

Published
2017
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7. A Hierarchical Bayesian Model for Multi-Site Diffeomorphic Image Atlases

Author

Hromatka, Michelle, Zhang, Miaomiao, Fleishman, Greg M., Gutman, Boris, Jahanshad, Neda, Thompson, Paul, Fletcher, P. Thomas, 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, Navab, Nassir, editor, Hornegger, Joachim, editor, Wells, William M., editor, and Frangi, Alejandro, editor

Published
2015
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8. A Riemannian Framework for Intrinsic Comparison of Closed Genus-Zero Shapes

Author

Gutman, Boris A., Fletcher, P. Thomas, Jorge Cardoso, M., Fleishman, Greg M., Lorenzi, Marco, Thompson, Paul M., Ourselin, Sebastien, 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, Ourselin, Sebastien, editor, Alexander, Daniel C., editor, Westin, Carl-Fredrik, editor, and Cardoso, M. Jorge, editor

Published
2015
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9. Simultaneous Longitudinal Registration with Group-Wise Similarity Prior

Author

Fleishman, Greg M., Gutman, Boris A., Fletcher, P. Thomas, Thompson, Paul M., 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, Ourselin, Sebastien, editor, Alexander, Daniel C., editor, Westin, Carl-Fredrik, editor, and Cardoso, M. Jorge, editor

Published
2015
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14. Erratum to: Fast Predictive Simple Geodesic Regression

Author

The Alzheimer’s Disease Neuroimaging Initiative, Ding, Zhipeng, Fleishman, Greg, Yang, Xiao, Thompson, Paul, Kwitt, Roland, Niethammer, Marc, 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, Cardoso, M. Jorge, editor, Arbel, Tal, editor, Carneiro, Gustavo, editor, Syeda-Mahmood, Tanveer, editor, Tavares, João Manuel R.S., editor, Moradi, Mehdi, editor, Bradley, Andrew, editor, Greenspan, Hayit, editor, Papa, João Paulo, editor, Madabhushi, Anant, editor, Nascimento, Jacinto C., editor, Cardoso, Jaime S., editor, Belagiannis, Vasileios, editor, and Lu, Zhi, editor

Published
2017
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15. Practical Implementation and Application of Geodesic Regression in Diffeomorphisms to Brain Image Time Series

Author

Fleishman, Greg Fleishman

Subjects
Applied mathematics, Neurosciences, Computer science, Alzheimer's Disease, Atrophy, Brain MRI, Diffeomorphism, Image Registration, Optimization
Abstract

Diffeomorphisms have received significant research focus in the medical image registration community over the past 15 years due in part to their desirable mathematical properties: the preservation of image topology and the guaranteed existence of a number of spatial derivatives. The research area began with fundamental mathematical developments detailing how a diffeomorphism can be defined and constructed in the image registration context, and subsequently, algorithms were proposed to implement the continuous domain diffeomorphic theory in the discrete domain. After several iterations in form, the geodesic regression formulation emerged, which can be understood as a natural generalization of Euclidean linear regression to a nonlinear manifold of diffeomorphisms.Geodesic Regression in Diffeomorphisms (GRiD) is the optimization of an initial momentum field which parameterizes a geodesic flow of diffeomorphisms through a time series of images. The method involves several computational challenges: the optimization of a very high dimensional nonlinear objective function, the integration of several coupled systems of partial differential equations, and the implementation of several fundamental operations including composition of images with deformations, regularization of vector fields, and evaluation of possibly complex image similarity functionals. Additionally, several of these components have free parameters that must be selected carefully to ensure convergence and the biological validity of results.GRiD theory offers many advantages over standard image registration for the study of image deformations over time; it provides a succinct but comprehensive summary of the primary mode of image deformation over time evident in a time series, all the while guaranteeing desirable mathematical properties of the transformation flow. This powerful theory will be immensely useful in the study of growth, development, and aging in both health and disease. However, the complicated nature of the algorithm has prevented its widespread adoption in the applied medical imaging community.The goal of this dissertation is to exposit practical and down to earth derivation, implementation, and application of GRiD. Chapters 1-3 cover those topics exactly. Additionally chapters 4 and 5 cover methods for selecting image matching functional and determining some of the model's free parameters. Chapter 6 is a complete large scale study of atrophy in Alzheimer's disease using GRiD. Chapters 7 and 8 discuss a novel extension of the GRiD model wherein multiple GRiD optimizations inform each other simultaneously.

Published
2016

17. Expansion-Assisted Iterative-FISH defines lateral hypothalamus spatio-molecular organization

Author

Wang, Yuhan, primary, Eddison, Mark, additional, Fleishman, Greg, additional, Weigert, Martin, additional, Xu, Shengjin, additional, Henry, Fredrick E., additional, Wang, Tim, additional, Lemire, Andrew L., additional, Schmidt, Uwe, additional, Yang, Hui, additional, Rokicki, Konrad, additional, Goina, Cristian, additional, Svoboda, Karel, additional, Myers, Eugene W., additional, Saalfeld, Stephan, additional, Korff, Wyatt, additional, Sternson, Scott M., additional, and Tillberg, Paul W., additional

Published
2021
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23. Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentation.

Author

Fleishman GM, Valcarcel A, Pham DL, Roy S, Calabresi PA, Yushkevich P, Shinohara RT, and Oguz I

Abstract

We propose a new approach to Multiple Sclerosis lesion segmentation that utilizes synthesized images. A new method of image synthesis is considered: joint intensity fusion (JIF). JIF synthesizes an image from a library of deformably registered and intensity normalized atlases. Each location in the synthesized image is a weighted average of the registered atlases; atlas weights vary spatially. The weights are determined using the joint label fusion (JLF) framework. The primary methodological contribution is the application of JLF to MRI signal directly rather than labels. Synthesized images are then used as additional features in a lesion segmentation task using the OASIS classifier, a logistic regression model on intensities from multiple modalities. The addition of JIF synthesized images improved the Dice-Sorensen coefficient (relative to manually drawn gold standards) of lesion segmentations over the standard model segmentations by 0.0462 ± 0.0050 (mean ± standard deviation) at optimal threshold over all subjects and 10 separate training/testing folds.

Published
2018

24. ADAPTIVE GRADIENT DESCENT OPTIMIZATION OF INITIAL MOMENTA FOR GEODESIC SHOOTING IN DIFFEOMORPHISMS.

Author

Fleishman GM and Thompson PM

Abstract

Diffeomorphic image registration algorithms are widely used in medical imaging, and require optimization of a high-dimensional nonlinear objective function. The function being optimized has many characteristics that are relevant for optimization but are typically not well understood. Due to that complexity, most authors have used a simple gradient descent, but it is not often discussed how step sizes are chosen or if line searches are used. Further, if a system is to be robust to a range of input images, that may differ to varying degrees, the optimization must be adaptable. Here, we present two methods of adaptable gradient descent with line searches, and test how they affect image registration. The optimization schemes are deployed for geodesic shooting in diffeomorphisms - an approach that is used to quantify anatomical changes, such as atrophy, in longitudinal image pairs. We evaluate the optimization schemes on their convergence characteristics and based on how well the resulting atrophy scores correlate with diagnostic group and mini mental state exam (MMSE) scores. We find that the Barzilai-Borwein method with a backtracking line search outperforms other optimization schemes in convergence time and adaptability by a wide margin. We also find that the variable optimization schemes do not significantly affect the ability to measure atrophy with clinical significance.

Published
2017
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25. THE IMPACT OF MATCHING FUNCTIONAL ON ATROPHY MEASUREMENT FROM GEODESIC SHOOTING IN DIFFEOMORPHISMS.

Author

Fleishman GM and Thompson PM

Abstract

Longitudinal registration has been used to map brain atrophy and tissue loss patterns over time, in both healthy and demented subjects. However, we have not seen a thorough application of the geodesic shooting in diffeomorphisms framework for this task. The registration model is complex and several choices must be made that may significantly impact the quality of results. One of these decisions is which image matching functional should drive the registration. We investigate four matching functionals for atrophy quantification using geodesic shooting in diffeomorphisms. We check if the choice of matching functional has an impact on the correlation of atrophy scores with clinical variables. We also check the impact of matching functional choice on estimates of the N80 sample size for hypothetical clinical trials that test for slowing of brain atrophy. We find that the mutual information function, which has primarily been used for linear and multi-modal registration, achieves comparable correlation with clinical variables to other matching functionals while yielding better sample size estimates.

Published
2017
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26. A Transformation Similarity Constraint for Groupwise Nonlinear Registration in Longitudinal Neuro Imaging Studies.

Author

Fleishman GM, Gutman BA, Fletcher PT, and Thompson P

Abstract

Patients with Alzheimer's disease and other brain disorders often show a similar spatial distribution of volume change throughout the brain over time, 1,2 but this information is not yet used in registration algorithms to refine the quantification of change. Here, we develop a mathematical basis to incorporate that prior information into a longitudinal structural neuroimaging study. We modify the canonical minimization problem for non-linear registration to include a term that couples a collection of registrations together to enforce group similarity. More specifically, throughout the computation we maintain a group-level representation of the transformations and constrain updates to individual transformations to be similar to this representation. The derivations necessary to produce the Euler-Lagrange equations for the coupling term are presented and a gradient descent algorithm based on the formulation was implemented. We demonstrate using 57 longitudinal image pairs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) that longitudinal registration with such a groupwise coupling prior is more robust to noise in estimating change, suggesting such change maps may have several important applications.

Published
2015
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