Bennett A. Landman

Prasanna Parvathaneni, Bennett A. Landman, Kurt G. Schilling, 2015, Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging.

Algorithmic Enhancements to Big Data Computing Frameworks for Medical Image Processing

Aniruddha S. Gokhale, Shunxing Bao, Bennett A. Landman, 2017, 2017 IEEE International Conference on Cloud Engineering (IC2E).

A surgeon specific automatic path planning algorithm for deep brain stimulation

Benoit M. Dawant, Pierre-François D'Haese, Yuan Liu, 2012, Medical Imaging.

Harmonization of White and Gray Matter Features in Diffusion Microarchitecture for Cross-Sectional Studies

Shunxing Bao, Prasanna Parvathaneni, Ilwoo Lyu, 2019 .

Assessment of Orbital Computed Tomography (CT) Imaging Biomarkers in Patients with Thyroid Eye Disease

Bennett A. Landman, Robert L. Galloway, Louise A. Mawn, 2019, Journal of Digital Imaging.

Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions pdf

Alejandro F. Frangi, Peter F. Neher, Klaus H. Maier-Hein, 2018, ArXiv.

A large annotated medical image dataset for the development and evaluation of segmentation algorithms pdf

Ronald M. Summers, M. Jorge Cardoso, Lena Maier-Hein, 2019, ArXiv.

Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision

Bennett A. Landman, Richard G. Abramson, Shizhong Han, 2019, Medical Imaging: Image Processing.

Collaborative labeling of malignant glioma

Bennett A. Landman, Andrew J. Asman, Zhoubing Xu, 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

Learning from dispersed manual annotations with an optimized data weighting policy

Shunxing Bao, Yuankai Huo, Bennett A. Landman, 2020, Journal of medical imaging.

Splenomegaly segmentation using global convolutional kernels and conditional generative adversarial networks

Shunxing Bao, Camilo Bermudez, Yuankai Huo, 2017, Medical Imaging.

Resolution of crossing fibers with constrained compressed sensing using diffusion tensor MRI

Jerry L. Prince, Pierre-Louis Bazin, Bennett A. Landman, 2012, NeuroImage.

Groupwise multi-atlas segmentation of the spinal cord's internal structure

Seth A. Smith, Daniel S. Reich, Bennett A. Landman, 2014, Medical Image Anal..

Revealing latent value of clinically acquired CTs of traumatic brain injury through multi-atlas segmentation in a retrospective study of 1,003 with external cross-validation

Andrew J. Plassard, Bennett A. Landman, Andrew J. Asman, 2015, Medical Imaging.

Functional Networks in Temporal-Lobe Epilepsy: A Voxel-Wise Study of Resting-State Functional Connectivity and Gray-Matter Concentration

Bennett A. Landman, John C. Gore, Zhaohua Ding, 2013, Brain Connect..

Robust Multicontrast MRI Spleen Segmentation for Splenomegaly Using Multi-Atlas Segmentation

Yuankai Huo, Jiaqi Liu, Bennett A. Landman, 2017, IEEE Transactions on Biomedical Engineering.

How to Exploit Weaknesses in Biomedical Challenge Design and Organization

Lena Maier-Hein, Annika Reinke, Patrick Scholz, 2018, MICCAI.

Accounting for Random Regressors: A Unified Approach to Multi-modality Imaging

Xue Yang, Susan M. Resnick, Bennett A. Landman, 2011, MBIA.

Collaborative and Reproducible Research: Goals, Challenges, and Strategies

Bennett A. Landman, George Shih, Steve G. Langer, 2018, Journal of Digital Imaging.

Approaching expert results using a hierarchical cerebellum parcellation protocol for multiple inexpert human raters

Jerry L. Prince, Bennett A. Landman, Jerry L Prince, 2013, NeuroImage.

PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images

Qi Yang, Benjamin N. Conrad, Baxter P. Rogers, 2020 .

Robust maximum likelihood estimation in Q-space MRI

Peter A. Calabresi, Jerry L. Prince, Seth A. Smith, 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

Correction to "Formulating Spatially Varying Performance in the Statistical Fusion Framework"

Bennett A. Landman, Andrew J. Asman, 2012, IEEE Trans. Medical Imaging.

Vascular burden and APOE ε4 are associated with white matter microstructural decline in cognitively normal older adults

Yuankai Huo, Susan M. Resnick, Bennett A. Landman, 2019, NeuroImage.

Automated Characterization of Body Composition and Frailty with Clinically Acquired CT

Yuankai Huo, Bennett A. Landman, J. Jeffrey Carr, 2017, MSKI@MICCAI.

Toward content-based image retrieval with deep convolutional neural networks

Daniel Fabbri, Andrew J. Plassard, Bennett A. Landman, 2015, Medical Imaging.

Quadratic: quality of dice in registration circuits

Bennett A. Landman, Shikha Chaganti, B. Landman, 2018, Medical Imaging.

Cortical Surface Parcellation Using Spherical Convolutional Neural Networks

Shunxing Bao, Yuankai Huo, Prasanna Parvathaneni, 2019, MICCAI.

Generalized statistical label fusion using multiple consensus levels

Bennett A. Landman, Andrew J. Asman, Zhoubing Xu, 2012, Medical Imaging.

Deep Multi-task Prediction of Lung Cancer and Cancer-free Progression from Censored Heterogenous Clinical Imaging pdf

Yuankai Huo, Bennett A. Landman, Yucheng Tang, 2020, Medical Imaging: Image Processing.

Deep Multi-path Network Integrating Incomplete Biomarker and Chest CT Data for Evaluating Lung Cancer Risk pdf

Bennett A. Landman, Yucheng Tang, Riqiang Gao, 2020, ArXiv.

CircleNet: Anchor-Free Glomerulus Detection with Circle Representation

Bennett A. Landman, Le Lu, Ruining Deng, 2020, MICCAI.

Applying the algorithm "assessing quality using image registration circuits" (AQUIRC) to multi-atlas segmentation

Benoit M. Dawant, Bennett A. Landman, Andrew J. Asman, 2014, Medical Imaging.

Effect of regularization parameter and scan time on crossing fibers with constrained compressed sensing

Jerry L. Prince, Bennett A. Landman, Jerry L Prince, 2011, Medical Imaging.

Internal-transfer Weighting of Multi-task Learning for Lung Cancer Detection pdf

Yuankai Huo, Bennett A. Landman, Yucheng Tang, 2020, Medical Imaging: Image Processing.

Impact of family structure and common environment on heritability estimation for neuroimaging genetics studies using Sequential Oligogenic Linkage Analysis Routines

Paul M. Thompson, Peter Kochunov, Thomas E. Nichols, 2014, Journal of medical imaging.

Statistical parametric mapping of brain morphology: Sensitivity is dramatically increased by using brain-extracted images as inputs

Bennett A. Landman, George Fein, Hoang Tran, 2006, NeuroImage.

Evaluation of five image registration tools for abdominal CT: pitfalls and opportunities with soft anatomy

Bennett A. Landman, Richard G. Abramson, Zhoubing Xu, 2015, Medical Imaging.

Multi-scale hippocampal parcellation improves atlas-based segmentation accuracy

Maureen McHugo, Stephan Heckers, Andrew J. Plassard, 2017, Medical Imaging.

Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury

Snehashis Roy, Camilo Bermudez, Bennett A. Landman, 2019, Medical Imaging: Image Processing.

Statistical label fusion with hierarchical performance models

Bennett A. Landman, Andrew J. Asman, Alexander S. Dagley, 2014, Medical Imaging.

Multi-modal and targeted imaging improves automated mid-brain segmentation

Benoit M. Dawant, Pierre-François D'Haese, Andrew J. Plassard, 2017, Medical Imaging.

SHARD: spherical harmonic-based robust outlier detection for HARDI methods

Prasanna Parvathaneni, Ilwoo Lyu, Baxter P. Rogers, 2018, Medical Imaging.

Whole Brain Functional Connectivity Using Multi-scale Spatio-Spectral Random Effects Model

Xue Yang, Bennett A. Landman, Hakmook Kang, 2013, MBIA.

Towards automatic quantitative quality control for MRI

Bennett A. Landman, Brian Caffo, Carolyn B. Lauzon, 2012, Medical Imaging.

MRI correlates of chronic symptoms in mild traumatic brain injury pdf

Beth Miller, Bennett A. Landman, Kurt G. Schilling, 2020, Medical Imaging: Image Processing.

Interfaces and integration of medical image analysis frameworks: Challenges and opportunities

Aaron Carass, Min Chen, Bennett A. Landman, 2010, 2010 Biomedical Sciences and Engineering Conference.

RAP-Net: Coarse-to-Fine Multi-Organ Segmentation with Single Random Anatomical Prior pdf

Shunxing Bao, Bennett A. Landman, Richard G. Abramson, 2020, ArXiv.

Multi-atlas segmentation for abdominal organs with Gaussian mixture models

Bennett A. Landman, Richard G. Abramson, Zhoubing Xu, 2015, Medical Imaging.

Finding seeds for segmentation using statistical fusion

Jerry L. Prince, Bennett A. Landman, Andrew J. Asman, 2012, Medical Imaging.

Data-driven Probabilistic Atlases Capture Whole-brain Individual Variation pdf

Yuankai Huo, Susan M. Resnick, Bennett A. Landman, 2018, ArXiv.

Bootstrapping White Matter Segmentation, Eve plus

Andrew J. Plassard, Kendra E. Hinton, Vijay K. Venkatraman, 2015 .

Limits to anatomical accuracy of diffusion tractography using modern approaches

Peter F. Neher, Carl-Fredrik Westin, Ragini Verma, 2018 .

Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA–DTI working group

Paul M. Thompson, Arthur W. Toga, Thomas E. Nichols, 2013, NeuroImage.

Tractography Reproducibility Challenge with Empirical Data (TraCED): The 2017 ISMRM Diffusion Study Group Challenge

Peter F. Neher, Arthur W. Toga, Ragini Verma, 2018, bioRxiv.

Hierarchical Spherical Deformation for Shape Correspondence

Ilwoo Lyu, Bennett A. Landman, Martin A. Styner, 2018, MICCAI.

Robust non-local multi-atlas segmentation of the optic nerve

Bennett A. Landman, Robert L. Galloway, Louise A. Mawn, 2013, Medical Imaging.

Multi-atlas spleen segmentation on CT using adaptive context learning

Yuankai Huo, Jiaqi Liu, Bennett A. Landman, 2017, Medical Imaging.

Validating DICOM Transcoding with an Open Multi-Format Resource

Bennett A. Landman, David H. Zald, Benjamin C. Yvernault, 2014, Neuroinformatics.

5.5: Presentation session: Neuroscience informatics: “Interfaces and integration of Medical Image Analysis frameworks: Challenges and opportunities”

Bennett A. Landman, 2010 .

Characterizing Spatially Varying Performance to Improve Multi-atlas Multi-label Segmentation

Bennett A. Landman, Andrew J. Asman, B. Landman, 2011, IPMI.

A comparison of distributional considerations with statistical analysis of resting state fMRI at 3T and 7T

Xue Yang, Bennett A. Landman, Allen T. Newton, 2012, Medical Imaging.

Semi-supervised Machine Learning with MixMatch and Equivalence Classes

Camilo Bermudez, Bennett A. Landman, Vishwesh Nath, 2020, iMIMIC/MIL3iD/LABELS@MICCAI.

Improving cerebellar segmentation with statistical fusion

Jerry L. Prince, Andrew J. Plassard, Bennett A. Landman, 2016, SPIE Medical Imaging.

Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

Christophe Lenglet, Vince D. Calhoun, Chun-Hung Yeh, 2020, NeuroImage.

Efficient Anatomical Labeling by Statistical Recombination of Partially Label Datasets

Jerry L. Prince, Bennett A. Landman, John A. Bogovic, 2009 .

BIAS: Transparent reporting of biomedical image analysis challenges

Lena Maier-Hein, Annika Reinke, Matthias Eisenmann, 2019, Medical Image Analysis.

Statistical fusion of surface labels provided by multiple raters

Jerry L. Prince, Pierre-Louis Bazin, Bennett A. Landman, 2010, Medical Imaging.

Comparison of Cortical and Subcortical Measurements in Normal Older Adults across Databases and Software Packages

Bennett A. Landman, Manus J. Donahue, Daniel O. Claassen, 2017, Journal of Alzheimer's disease reports.

Multisurgeon, Multisite Validation of a Trajectory Planning Algorithm for Deep Brain Stimulation Procedures

Benoit M. Dawant, Pierre-François D'Haese, Yuan Liu, 2014, IEEE Transactions on Biomedical Engineering.

Sulcal depth-based cortical shape analysis in normal healthy control and schizophrenia groups

Ilwoo Lyu, Neil D. Woodward, Bennett A. Landman, 2018, Medical Imaging.

Learning 3D White Matter Microstructure from 2D Histology

Yuankai Huo, Bennett A. Landman, Vishwesh Nath, 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

Medical Imaging 2019: Image Processing

Elsa D. Angelini, Bennett A. Landman, 2019 .

Diffusion Tensor Estimation by Maximizing Rician Likelihood

Jerry L. Prince, Pierre-Louis Bazin, Bennett A. Landman, 2007, 2007 IEEE 11th International Conference on Computer Vision.

Effects of b-value and number of gradient directions on diffusion MRI measures obtained with Q-ball imaging

Bennett A. Landman, Vishwesh Nath, Zhaohua Ding, 2017, Medical Imaging.

Towards Machine Learning Prediction of Deep Brain Stimulation (DBS) Intra-operative Efficacy Maps

Benoit M. Dawant, Camilo Bermudez, Yuankai Huo, 2019, Medical Imaging: Image Processing.

Accelerated Compressed Sensing of Diffusion-Inferred Intra-Voxel Structure through Adaptive Refinement

Jerry L. Prince, Bennett A. Landman, John A. Bogovic, 2010 .

Consideration of cerebrospinal fluid intensity variation in diffusion weighted MRI

Prasanna Parvathaneni, Carlo Pierpaoli, Baxter P. Rogers, 2019, Medical Imaging.

Contrast Phase Classification with a Generative Adversarial Network pdf

Camilo Bermudez, Bennett A. Landman, Richard G. Abramson, 2020, Medical Imaging: Image Processing.

Convergent individual differences in visual cortices, but not the amygdala across standard amygdalar fMRI probe tasks

Victoria Villalta-Gil, Benjamin B. Lahey, Bennett A. Landman, 2017, NeuroImage.

Technology Enablers for Big Data, Multi-Stage Analysis in Medical Image Processing

Aniruddha S. Gokhale, Shunxing Bao, Yuankai Huo, 2018, 2018 IEEE International Conference on Big Data (Big Data).

Lasting consequences of concussion on the aging brain: Findings from the Baltimore Longitudinal Study of Aging

Christos Davatzikos, Susan M. Resnick, Bennett A. Landman, 2020, NeuroImage.

Erratum to: The Java Image Science Toolkit (JIST) for Rapid Prototyping and Publishing of Neuroimaging Software

Aaron Carass, Jerry L. Prince, Pierre-Louis Bazin, 2010, Neuroinformatics.

Registration-free Distortion Correction of Diffusion Weighted MRI

Neil D. Woodward, David H. Zald, Baxter P. Rogers, 2020 .

Lung Cancer Detection using Co-learning from Chest CT Images and Clinical Demographics

Shunxing Bao, Yuankai Huo, Bennett A. Landman, 2019, Medical Imaging: Image Processing.

Deep multi-path network integrating incomplete biomarker and chest CT data for evaluating lung cancer risk

Yuankai Huo, Bennett A. Landman, Yucheng Tang, 2021, Medical Imaging.

Attention-Guided Supervised Contrastive Learning for Semantic Segmentation pdf

Xin Yu, Shunxing Bao, Yuankai Huo, 2021, ArXiv.

Compound Figure Separation of Biomedical Images with Side Loss pdf

Catie Chang, Shunxing Bao, Yuankai Huo, 2021, DGM4MICCAI/DALI@MICCAI.

Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow

Fang-Cheng Yeh, Bennett A. Landman, Kurt G. Schilling, 2021, NeuroImage.

Common Limitations of Image Processing Metrics: A Picture Story pdf

Ronald M. Summers, Sotirios A. Tsaftaris, Lena Maier-Hein, 2021, ArXiv.

Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective pdf

Yuankai Huo, Bennett A. Landman, Thomas A. Lasko, 2021, MICCAI.

Medical Imaging 2021: Image Processing

Bennett A. Landman, Ivana Išgum, 2021 .

Renal cortex, medulla and pelvicaliceal system segmentation on arterial phase CT images with random patch-based networks

Shunxing Bao, Yuankai Huo, Bennett A. Landman, 2021, Medical Imaging.

Construction of a multi-phase contrast computed tomography kidney atlas

Shunxing Bao, Yuankai Huo, Bennett A. Landman, 2021, Medical Imaging.

Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training

Ilwoo Lyu, Bennett A. Landman, Silvia A. Bunge, 2021, NeuroImage.

Semantic-Aware Contrastive Learning for Multi-Object Medical Image Segmentation

Shunxing Bao, Yuankai Huo, Bennett A. Landman, 2021, IEEE Journal of Biomedical and Health Informatics.

Establishing surface correspondence for post-surgical cortical thickness changes in temporal lobe epilepsy

Ipek Oguz, Ilwoo Lyu, Bennett A. Landman, 2021, Medical Imaging.

Pancreas CT Segmentation by Predictive Phenotyping

Shunxing Bao, Yuankai Huo, Bennett A. Landman, 2021, MICCAI.

Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

Jean-Philippe Thiran, Christophe Lenglet, Maxime Descoteaux, 2021, NeuroImage.

Technical Report: Quality Assessment Tool for Machine Learning with Clinical CT pdf

Yuankai Huo, Bennett A. Landman, Pierre P. Massion, 2021, ArXiv.

On statistical tests of functional connectome fingerprinting

Bennett A. Landman, Joshua T. Vogelstein, Brian Caffo, 2021 .

Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging pdf

Shunxing Bao, Yuankai Huo, Ilwoo Lyu, 2021, ArXiv.

A cross-platform informatics system for the Gut Cell Atlas: integrating clinical, anatomical and histological data

Shunxing Bao, Yuankai Huo, Bennett A. Landman, 2021, Medical Imaging.

Circle Representation for Medical Object Detection

Le Lu, Yuankai Huo, Bennett A. Landman, 2021, IEEE transactions on medical imaging.

The Medical Segmentation Decathlon pdf

Yan Wang, Akshay Pai, Daguang Xu, 2021, ArXiv.

Body Part Regression With Self-Supervision

Shunxing Bao, Camilo Bermudez, Yuankai Huo, 2021, IEEE Transactions on Medical Imaging.

Corrigendum to "Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline" [Comput. Biol. Med. 107 (2019) 109-117]

Yuankai Huo, Bennett A. Landman, Albert Assad, 2022, Comput. Biol. Medicine.

Validation of group-wise registration for surface-based functional MRI analysis

Ilwoo Lyu, Susan M. Resnick, Bennett A. Landman, 2021, Medical Imaging.

Orthogonal diffusion-weighted MRI measures distinguish region-specific degeneration in cerebellar ataxia subtypes

Jerry L. Prince, Susumu Mori, Bennett A. Landman, 2009, Journal of Neurology.

Lower gray matter integrity is associated with greater lap time variation in high-functioning older adults

Yuankai Huo, Vijay K. Venkatraman, Christopher E. Gonzalez, 2016, Experimental Gerontology.

Magnetic Resonance Connectome Automated Pipeline

Jerry L. Prince, Bennett A. Landman, R. Jacob Vogelstein, 2011 .

On statistical tests of functional connectome fingerprinting

Martin A. Lindquist, Brian S. Caffo, Susan M. Resnick, 2018, bioRxiv.

Quantitative CT Imaging of Ventral Hernias: Preliminary Validation of an Anatomical Labeling Protocol

Bennett A. Landman, Richard G. Abramson, Andrew J. Asman, 2015, PloS one.

Diffusion Tensor Imaging Enables Robust Mapping of the Deep Cerebellar Nuclei

Jerry L. Prince, Bennett A. Landman, Jerry L Prince, 2007 .

Magnetic resonance connectome automated pipeline and repeatability analysis

Jerry L. Prince, Chuyang Ye, Bennett A. Landman, 2015 .

Tongue muscle fiber tracking during rest and tongue protrusion with oral appliances: A preliminary study with diffusion tensor imaging

Jerry L. Prince, Bennett A. Landman, Jerry L Prince, 2008 .

Simultaneous Analysis and Quality Assurance for Diffusion Tensor Imaging

Bennett A. Landman, Andrew J. Asman, Adam W. Anderson, 2013, PloS one.