neuroimage analysis for automated brain disease diagnosis
play

Neuroimage Analysis for Automated Brain Disease Diagnosis Mingxia - PowerPoint PPT Presentation

Oct 14, 2023 •125 likes •693 views

Neuroimage Analysis for Automated Brain Disease Diagnosis Mingxia Liu University of North Carolina at Chapel Hill mxliu@med.unc.edu http://mingxia.web.unc.edu/ 07-17-2019 Background Healthy Brain vs. Alzheimers Sulcus Sulcus Gyrus

  1. Neuroimage Analysis for Automated Brain Disease Diagnosis Mingxia Liu University of North Carolina at Chapel Hill mxliu@med.unc.edu http://mingxia.web.unc.edu/ 07-17-2019

  2. Background Healthy Brain vs. Alzheimer’s Sulcus Sulcus Gyrus Gyrus Ventricle Alzheimer’s Alzheimer’s Healthy Brain Healthy Brain

  3. Background • Alzheimer’s Disease (AD) – A progressive disease Brain Health • Calling Need – Developing computer-aided methods for MCI/AD diagnosis Time Alzheimer’s Mild Cognitive Normal Control Impairment (MCI) Disease Stable MCI (sMCI) Progressive MCI (pMCI)

  4. Background Biomarkers for early diagnosis of AD and MCI • Structural Magnetic Resonance Imaging (MRI) • FDG-Positron Emission Tomography (PET) Cerebrospinal Fluid (CSF) ‐‐‐ A β42, t ‐ tau and p ‐ tau • Multi-modal data Brain CSF Dura MRI PET CSF

  5. Brain Disease Diagnosis – Typical Pipeline Neuroimaging Image Feature Classifier Data Preprocessing Extraction/Selection Learning sMRI PET fMRI Machine Learning & Deep Learning M. Liu , etc., Landmark-based Deep Multi-Instance Learning for Brain Disease Diagnosis, Medical Image Analysis , 2018. M. Liu , etc. Joint Classification and Regression via Deep Multi-task Multi-channel Learning for Alzheimer’s Disease Diagnosis. IEEE Trans. on Biomedical Engineering , 2018.

  6. Challenges in Computer-aided Disease Diagnosis • Effective feature representation of neuroimages • Missing multi-modal data • Heterogeneous data at different imaging sites

  7. Outline sMRI PET CSF sMRI • Structural MRI (sMRI) • Missing Data • Multi-modal Data Fusion • Domain Adaptation Single-modal Neuroimage Multi-modal Neuroimage

  8. Part I. Single-modal Neuroimage Analysis • Structural MRI based Brain Disease Diagnosis

  9. Anatomical Landmarks for Structural MRI • Landmark-based Deep Representation of sMRI C. Lian, M. Liu, J. Zhang, and D. Shen. IEEE Trans. on Pattern Analysis and Machine Intelligence , 2019. M. Liu, J. Zhang, C. Lian, and D. Shen. IEEE Trans. on Cybernetics , 2019. M. Liu, J. Zhang, E. Adeli, and D. Shen. IEEE Trans. on Biomedical Engineering , 2019. M. Liu, J. Zhang, E. Adeli, and D. Shen. Medical Image Analysis , 2018. M. Liu, J. Zhang, D. Nie, P.T. Yap, and D. Shen. IEEE Journal of Biomedical and Health Informatics , 2018. J. Zhang, M. Liu, and D. Shen. IEEE Trans. on Image Processing , 2017.

  10. Anatomical Landmark-based Deep Network Pre-processed Deep Multi-channel Convolutional Landmark Discovery Patch Extraction MR Images Neural Network (CNN) ... ... ... CNN … … Training Data Training Data Training Data Training MRIs Landmark Detection Test MRI Disease Clinical Score Classification Prediction ata ata ata * Featured Article of IEEE Journal of Biomedical and Health Informatics, 2018

  11. Anatomical Landmark-based Deep Network 1,740 landmarks via group Top 50 landmarks comparison between AD and NC 0.01 p -values 0 M. Liu, J. Zhang, E. Adeli, and D. Shen. Medical Image Analysis , 2018. M. Liu, J. Zhang, D. Nie, P.T. Yap, and D. Shen. IEEE Journal of Biomedical and Health Informatics , 2018.

  12. Landmark-based Deep Network sMRI … Patch 1 Patch 2 Patch L Conv1- Conv1- Conv1- 32@3 × 3 × 3 32@3 × 3 × 3 32@3 × 3 × 3 Class Label Conv2- Conv2- Conv2- 32@3 × 3 × 3 32@3 × 3 × 3 32@3 × 3 × 3 Soft-max Max-pooling Max-pooling Max-pooling FC11-2 Global Conv3- Conv3- Conv3- 64@2 × 2 × 2 64@2 × 2 × 2 64@2 × 2 × 2 Representation? Global Conv4- Conv4- Conv4- … 64@2 × 2 × 2 64@2 × 2 × 2 64@2 × 2 × 2 Image-level FC10-8L Max-pooling Max-pooling Max-pooling Representation Conv5- Conv5- Conv5- 128@2 × 2 × 2 128@2 × 2 × 2 128@2 × 2 × 2 FC9-32L Conv6- Conv6- Conv6- 128@2 × 2 × 2 128@2 × 2 × 2 128@2 × 2 × 2 Max-pooling Max-pooling Max-pooling Concatenated Local … Patch-level FC7-128 FC7-128 FC7-128 FC8-32 FC8-32 FC8-32 Representation M. Liu, J. Zhang, E. Adeli, and D. Shen. Medical Image Analysis , 2018. M. Liu, J. Zhang, D. Nie, P.T. Yap, and D. Shen. IEEE Journal of Biomedical and Health Informatics , 2018.

  13. Results of Classification and Regression 0.518 0.567 Correlation Coefficient 0.538 0.487 0.486 0.492 Overall Accuracy 0.46 7 0.468 0.431 0.404 0.325 0.289 Classification Results for AD vs. sMCI vs. pMCI vs. NC Regression Results for MMSE M. Liu, J. Zhang, E. Adeli, and D. Shen. Medical Image Analysis , 2018. M. Liu, J. Zhang, D. Nie, P.T. Yap, and D. Shen. IEEE Journal of Biomedical and Health Informatics , 2018.

  14. End-to-end Disease Diagnosis with sMRI • Hierarchical Fully Convolutional network C. Lian, M. Liu, J. Zhang, and D. Shen. IEEE Trans. on Pattern Analysis and Machine Intelligence , 2019. M. Liu, J. Zhang, C. Lian, and D. Shen. IEEE Trans. on Cybernetics , 2019. M. Liu, J. Zhang, E. Adeli, and D. Shen. IEEE Trans. on Biomedical Engineering , 2019. M. Liu, J. Zhang, E. Adeli, and D. Shen. Medical Image Analysis , 2018. M. Liu, J. Zhang, D. Nie, P.T. Yap, and D. Shen. IEEE Journal of Biomedical and Health Informatics , 2018. J. Zhang, M. Liu, and D. Shen. IEEE Trans. on Image Processing , 2017.

  15. Hierarchical Network for ROI Identification • Hierarchical Fully Convolutional Network (H-FCN) – Automatically and identify disease-related ROIs in the whole sMR image – Jointly learn multi-scale features and construct a classification model C. Lian, M. Liu, J. Zhang, and D. Shen. IEEE Trans. on Pattern Analysis and Machine Intelligence , 2019. 15 C. Lian, M. Liu*, L. Wang, and D. Shen. MICCAI 2019 .

  16. Hierarchical Network for ROI Identification Input: sMRI C. Lian, M. Liu, J. Zhang, and D. Shen. IEEE Trans. on Pattern Analysis and Machine Intelligence , 2019. C. Lian, M. Liu*, L. Wang, and D. Shen. MICCAI 2019 .

  17. Hierarchical Network for ROI Identification Input: sMRI 1) Location proposals C. Lian, M. Liu, J. Zhang, and D. Shen. IEEE Trans. on Pattern Analysis and Machine Intelligence , 2019. C. Lian, M. Liu*, L. Wang, and D. Shen. MICCAI 2019 .

  18. Hierarchical Network for ROI Identification PSN PSN Class_P 32 64 64 128 128 64 Input: sMRI 1) Location 2) Patch-level sub- proposals networks (PSN) ( shared weights ) C. Lian, M. Liu, J. Zhang, and D. Shen. IEEE Trans. on Pattern Analysis and Machine Intelligence , 2019. C. Lian, M. Liu*, L. Wang, and D. Shen. MICCAI 2019 .

  19. Hierarchical Network for ROI Identification PSN PSN Class_P PSN 32 64 64 128 128 64 PSN PSN … … PSN PSN Input: sMRI PSN PSN 1) Location 2) Patch-level sub- proposals networks (PSN) ( shared weights ) C. Lian, M. Liu, J. Zhang, and D. Shen. IEEE Trans. on Pattern Analysis and Machine Intelligence , 2019. C. Lian, M. Liu*, L. Wang, and D. Shen. MICCAI 2019 .

  20. Hierarchical Network for ROI Identification PSN PSN Class_P Conv_R Class_R PSN 32 64 64 128 128 64 PSN 64 PSN … … PSN PSN Input: sMRI PSN PSN 1) Location 2) Patch-level sub- 3) Region-level proposals networks (PSN) ( shared sub-networks weights ) C. Lian, M. Liu, J. Zhang, and D. Shen. IEEE Trans. on Pattern Analysis and Machine Intelligence , 2019. C. Lian, M. Liu*, L. Wang, and D. Shen. MICCAI 2019 .

  21. Hierarchical Network for ROI Identification PSN PSN Class_P Conv_R Class_R PSN 32 64 64 128 128 64 PSN 64 PSN … … … PSN Conv_R Class_R PSN Input: sMRI PSN 64 PSN 1) Location 2) Patch-level sub- 3) Region-level proposals networks (PSN) ( shared sub-networks weights ) C. Lian, M. Liu, J. Zhang, and D. Shen. IEEE Trans. on Pattern Analysis and Machine Intelligence , 2019. C. Lian, M. Liu*, L. Wang, and D. Shen. MICCAI 2019 .

  22. Hierarchical Network for ROI Identification Network Pruning: To remove less informative regions PSN PSN Class_P Conv_R Class_R PSN 32 64 64 128 128 64 PSN 64 Conv_S PSN 4 × 4 × 4 Conv 3 × 3 × 3 Conv … … … 64 1 × 1 × 1 Conv 2 × 2 × 2 max pooling Class_S Classification ( 1 × 1 × 1 Conv) PSN Conv_R Class_R PSN Input: Region-level Conv Subject-level Conv Output: sMRI Class label PSN Channel concatenation Spatial concatenation 64 PSN Potentially pruned sub-networks 1) Location 2) Patch-level sub- 3) Region-level 4) Subject-level Skipped connection Conv: Convolution proposals networks (PSN) ( shared sub-networks sub-network weights ) C. Lian, M. Liu, J. Zhang, and D. Shen. IEEE Trans. on Pattern Analysis and Machine Intelligence , 2019. C. Lian, M. Liu*, L. Wang, and D. Shen. MICCAI 2019 .

  23. Identified Voxel-level Discriminative Locations 1 2 3 4 1 2 1 2 3 2 1 3 3 4 2 1 2 1 1 2 1 2 3 4 1 2 3 (a) AD Subject #1 (b) AD Subject #2 (c) AD Subject #3 1 2 3 1 2 3 1 2 3 2 3 1 1 3 3 2 2 1 1 2 3 1 2 3 1 2 3 (d) AD Subject #4 (e) AD Subject #5 (f) AD Subject #6 C. Lian, M. Liu, J. Zhang, and D. Shen. IEEE Trans. on Pattern Analysis and Machine Intelligence , 2019. C. Lian, M. Liu*, L. Wang, and D. Shen. MICCAI 2019 .

  24. Identified Patch-level Discriminative Locations Sagittal View Axial View Coronal View 3D View C. Lian, M. Liu, J. Zhang, and D. Shen. IEEE Trans. on Pattern Analysis and Machine Intelligence , 2019. C. Lian, M. Liu*, L. Wang, and D. Shen. MICCAI 2019 .

  25. Identified Region-level Discriminative Locations Sagittal View Axial View Coronal View 3D View C. Lian, M. Liu, J. Zhang, and D. Shen. IEEE Trans. on Pattern Analysis and Machine Intelligence , 2019. C. Lian, M. Liu*, L. Wang, and D. Shen. MICCAI 2019 .

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Joutsa et al. (2012). NeuroImage: 60: 19921999 Joutsa et al. (2012). NeuroImage: 60:

Joutsa et al. (2012). NeuroImage: 60: 19921999 Joutsa et al. (2012). NeuroImage: 60:

Joutsa et al. (2012). NeuroImage: 60: 19921999 Joutsa et al. (2012). NeuroImage: 60: 19921999 Linnet et al. (2010). Acta Psychiatr Scand: 122: 326333 Dopamine and gambling excitement B. BPND < 0 A. BPND 0 8 8 Excitement

314 views • 30 slides
1. Research Motivation Genetic Analysis for Disease: occurrence, diagnosis and treatment

1. Research Motivation Genetic Analysis for Disease: occurrence, diagnosis and treatment

1. Research Motivation Genetic Analysis for Disease: occurrence, diagnosis and treatment Data-driven Disease-Gene Association Prediction: Curated Databases limited knowledge within established frameworks Literature Based Discovery

390 views • 21 slides
Infectious Brain Disease Lab What is the coolest thing your brain does for you? What kinds of

Infectious Brain Disease Lab What is the coolest thing your brain does for you? What kinds of

Infectious Brain Disease Lab What is the coolest thing your brain does for you? What kinds of things make you sick? Which pathogens can get into the brain? prions Helminths (worms) brain infection How are diseases spread from 1 person to

422 views • 15 slides
Artificial Intelligence PESTOZ Crop Disease Diagnosis PESTOZ Crop Disease Diagnosis

Artificial Intelligence PESTOZ Crop Disease Diagnosis PESTOZ Crop Disease Diagnosis

Empowering Farmers with Empowering agriculture with Artificial Intelligence! Artificial Intelligence PESTOZ Crop Disease Diagnosis PESTOZ Crop Disease Diagnosis Infected crop found Click photo & Identify disease Get your Upload on

355 views • 9 slides
Hallmarks of Alzheimers disease Clinical features and diagnosis of Alzheimers disease

Hallmarks of Alzheimers disease Clinical features and diagnosis of Alzheimers disease

Hallmarks of Alzheimers disease Clinical features and diagnosis of Alzheimers disease Early onset: APP , PSEN1 , PSEN2 Progressive, unremitting neurodegenerative disease Dementia which leads to difficulty with daily life

471 views • 9 slides
Diagnosis of CDG Diagnosis of CDG Enzyme Analysis Enzyme Analysis and Other Investigations and

Diagnosis of CDG Diagnosis of CDG Enzyme Analysis Enzyme Analysis and Other Investigations and

Diagnosis of CDG Diagnosis of CDG Enzyme Analysis Enzyme Analysis and Other Investigations and Other Investigations Biochemical Genetics Network Cambridge April 2005 Viki Worthington National Hospital for Neurology and Neurosurgery, London

304 views • 18 slides
The Neurobiology of Addiction Addiction. Is it a Disease? The Disease Model of Addiction

The Neurobiology of Addiction Addiction. Is it a Disease? The Disease Model of Addiction

The Neurobiology of Addiction Addiction. Is it a Disease? The Disease Model of Addiction *Addiction is a primary, chronic disease of the brain. *It is a disease because drugs modify brain chemistry and the structure of cells and circuits

650 views • 34 slides
Introduction to Automated Task Planning Jonas Kvarnstrm Automated Planning and Diagnosis

Introduction to Automated Task Planning Jonas Kvarnstrm Automated Planning and Diagnosis

Introduction to Automated Task Planning Jonas Kvarnstrm Automated Planning and Diagnosis Group Artificial Intelligence and Integrated Computer Systems (AIICS) / IDA jonkv@ida jonkv@ida What is Planning? Shakey 3 4 Classical example:

763 views • 30 slides
What is motor neurone disease? A rapidly progressive disease that affects the brain and

What is motor neurone disease? A rapidly progressive disease that affects the brain and

What is motor neurone disease? A rapidly progressive disease that affects the brain and spinal cord. Attacks the nerves that control movement so leads to weakness and wasting of muscles, causing increasing loss of mobility in the

257 views • 11 slides
Residential Turfgrass Plant Pathology review, diagnosis, disease Disease Diagnostics Update

Residential Turfgrass Plant Pathology review, diagnosis, disease Disease Diagnostics Update

Objectives for today Residential Turfgrass Plant Pathology review, diagnosis, disease Disease Diagnostics Update on what diseases we have been seeing in the plant disease clinic on lawn grass samples Diagnostic tips and tricks for common

384 views • 10 slides
DIVING INTO ALZHEIMERS DISEASE with transcriptome analysis of a zebrafish model Nhi Hin

DIVING INTO ALZHEIMERS DISEASE with transcriptome analysis of a zebrafish model Nhi Hin

DIVING INTO ALZHEIMERS DISEASE with transcriptome analysis of a zebrafish model Nhi Hin (Master of Philosophy) Bioinformatics Hub, University of Adelaide What does the Alzheimers disease brain look like? Higher resolution e.g.

368 views • 22 slides
1 Differential Diagnosis March 14, 2019 Diagnosis is the identification of the

1 Differential Diagnosis March 14, 2019 Diagnosis is the identification of the

1 Differential Diagnosis March 14, 2019 Diagnosis is the identification of the nature and cause of a certain phenomenon di ff erential diagnosis is the distinguishing of a particular disease or condition from others that

1.12k views • 51 slides
Challenges in the Diagnosis of Interstitial Lung Disease New Classification of IIP Prior

Challenges in the Diagnosis of Interstitial Lung Disease New Classification of IIP Prior

5/12/2014 Overview Challenges in the Diagnosis of Interstitial Lung Disease New Classification of IIP Prior classification Modifications for new classification Diagnosis of UIP/NSIP Clinical, radiologic, pathologic findings

190 views • 15 slides
Challenges in the Diagnosis of Interstitial Lung Disease New Classification of IIP Prior

Challenges in the Diagnosis of Interstitial Lung Disease New Classification of IIP Prior

5/24/2014 Overview Challenges in the Diagnosis of Interstitial Lung Disease New Classification of IIP Prior classification Modifications for new classification Diagnosis of UIP/NSIP Clinical, radiologic, pathologic findings

166 views • 16 slides
Problem -based Scenarios in Practice E-learning for plant disease diagnosis Problem-based

Problem -based Scenarios in Practice E-learning for plant disease diagnosis Problem-based

Problem -based Scenarios in Practice E-learning for plant disease diagnosis Problem-based Scenarios for Plant Disease Diagnosis The why? Diagnosis is more than just identification of a suspected causal agent The diagnostician:

517 views • 25 slides
Interstitial lung disease: diagnosis and testing Professor Joanna Porter, Consultant in

Interstitial lung disease: diagnosis and testing Professor Joanna Porter, Consultant in

RCGP accredited until 1 st April 2020 RCN accredited until 2 nd July 2020 Accreditation applies only to the educational content and not to any product RCN cannot confirm competence of any practitioner Interstitial lung disease: diagnosis and

320 views • 12 slides
Decoding the informative content of brain activation maps: state of the art, challenges and

Decoding the informative content of brain activation maps: state of the art, challenges and

Decoding the informative content of brain activation maps: state of the art, challenges and future directions Bertrand Thirion, INRIA Saclay-le-de-France, Parietal team http://parietal.saclay.inria.fr bertrand.thirion@inria.fr Outline

723 views • 44 slides
Surface-based Analysis: Inter-subject Registration and Smoothing Outline Exploratory Spatial

Surface-based Analysis: Inter-subject Registration and Smoothing Outline Exploratory Spatial

Surface-based Analysis: Inter-subject Registration and Smoothing Outline Exploratory Spatial Analysis Coordinate Systems 3D (Volumetric) 2D (Surface-based) Inter-subject registration Volume-based Surface-based

289 views • 26 slides
IOL Subluxation Top 5 Pearls for Management David G. Hwang, MD, FACS Professor and Vice Chair

IOL Subluxation Top 5 Pearls for Management David G. Hwang, MD, FACS Professor and Vice Chair

IOL Subluxation Top 5 Pearls for Management David G. Hwang, MD, FACS Professor and Vice Chair Kimura Endowed Chair in Ophthalmology Director, Cornea Service and Refractive Surgery Services University of California, San Francisco December 2016

307 views • 15 slides
Immune Checkpoint Inhibition as a Rational Therapeutic Strategy for Advanced TNBC Hope S Rugo, MD

Immune Checkpoint Inhibition as a Rational Therapeutic Strategy for Advanced TNBC Hope S Rugo, MD

Immune Checkpoint Inhibition as a Rational Therapeutic Strategy for Advanced TNBC Hope S Rugo, MD Professor of Medicine Director, Breast Oncology and Clinical Trials Education University of California, San Francisco Medical Center UCSF Helen

801 views • 20 slides
Higher areas Modeling Extrastriate Areas Many higher areas beyond V1 Dr. James A. Bednar

Higher areas Modeling Extrastriate Areas Many higher areas beyond V1 Dr. James A. Bednar

Higher areas Modeling Extrastriate Areas Many higher areas beyond V1 Dr. James A. Bednar Selective for jbednar@inf.ed.ac.uk faces, http://homepages.inf.ed.ac.uk/jbednar self-motion, etc. Not as well understood Macaque visual

229 views • 7 slides
Modeling Extrastriate Areas Dr. James A. Bednar jbednar@inf.ed.ac.uk

Modeling Extrastriate Areas Dr. James A. Bednar jbednar@inf.ed.ac.uk

Modeling Extrastriate Areas Dr. James A. Bednar jbednar@inf.ed.ac.uk http://homepages.inf.ed.ac.uk/jbednar CNV Spring 2009: Extrastriate models 1 Higher areas Many higher areas beyond V1 Selective for faces, self-motion, etc.

646 views • 28 slides
LASS theory of conceptual processing Linguistic and conceptual systems What is a concept (in

LASS theory of conceptual processing Linguistic and conceptual systems What is a concept (in

Theories of knowledge representation Linguistically-motivated amodal Propositional structure via predicates, e.g. sing ( Maria,song ) Grounded cognition (Newell & Simon 1972, Fodor 1975, Pylyshyn 1984) Statistical distributions

170 views • 3 slides
Current Event The Interpretation of Motion Event perception embeds constraints on motion from

Current Event The Interpretation of Motion Event perception embeds constraints on motion from

Visual Experience and the Current Event The Interpretation of Motion Event perception embeds constraints on motion from how object move in the world Gravity and evolution & experience builds expectation on sort of movement to occur

370 views • 17 slides

More recommend