Transcript National Alliance for Medical Image Computing

Applications of Machine Learning to
Medical Informatics
Daniela S. Raicu, PhD
Assistant Professor
Email: [email protected]
Lab URL: http://facweb.cs.depaul.edu/research/vc/
Intelligent Multimedia Processing
& Medical Imaging Labs
• Faculty:
– GM. Besana, L. Dettori, J. Furst,
G. Gordon, S. Jost, D. Raicu
• CTI Students:
– J. Cisneros, M. Doran, W.
Horsthemke, B. Malinga, R.
Susomboon, E. Varutbangkul,
S.G. Valencia, J. Zhang
• NSF REU Students (2006):
– A. Bashir, T. Disney, S.
Greenblum, J. Hasemann, M.O.
Krucoff, M. Lam, M. Pham, A.
Rogers, S. Talbot
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Intelligent Multimedia Processing
& Medical Imaging Labs
• IMP Collaborators & Funding Agencies
– National Science Foundation (NSF) - Research Experience for
Undergraduates (REU)
– Northwestern University - Department of Radiology, Imaging
Informatics Section
– University of Chicago – Medical Physics Department
– Argonne National Laboratory - Biochip Technology Center
– DePaul University, College of Law
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Outline
Part I: Introduction to Medical Informatics
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Medical Informatics
Medical Imaging
Imaging Modalities
Basic Concepts in Image Processing
Part II: Current Research Problems in Medical
Informatics
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Segmentation of soft tissues
Classification of pure patches
Visualization of pure patches
Content-based Image Retrieval and Annotation
Image Content-Driven Ontology for Chest interpretation
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What is Medical Informatics?
Simplistic definition:
Medical informatics is the application of computers,
communications and information technology and
systems to all fields of medicine - medical care, medical
education and medical research.
MF Collen, MEDINFO '80, Tokyo
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What is Medical Informatics?
Medical Informatics is the branch of science concerned
with the use of computers and communication
technology to acquire, store, analyze, communicate,
and display medical information and knowledge to
facilitate understanding and improve the accuracy,
timeliness, and reliability of decision-making.
Warner, Sorenson and Bouhaddou, Knowledge
Engineering in Health Informatics, 1997
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Subdomains of Medical Informatics
(by Wikipedia)
•
•
•
•
•
•
•
•
•
imaging informatics
clinical informatics
nursing informatics
Consumer health informatics
public health informatics
dental informatics
clinical research informatics
bioinformatics
pharmacy informatics
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What is Medical Imaging?
The study of medical imaging is concerned with the
interaction of all forms of radiation with tissue
and
the development of appropriate technology to extract clinically
useful information (usually displayed in an image format) from
observation of this technology.
Sources of Images:
• Structural/anatomical information (CT, MRI, US) - within each
elemental volume, tissue-differentiating properties are
measured.
• Information about function (PET, SPECT, fMRI).
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Understanding Visual Information: Technical, Cognitive and Social Factors
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Examples of Medical Images
X-ray Image of the hand
Computed Tomography
(CT) Image
of
Functional
Magnetic
plane through
liver and stomach Resonance Imaging (fMRI) of
the brain
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Single
Fluorescence
Computed
Microscopy:
of the tissue
heart culture cells.
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Ultrasound image
ofPhoton
a woman’s
abdomen Image of living
What is a Medical Image?
pixel
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slice thickness
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DICOM standard in Medical Imaging
DICOM: "Digital Imaging and Communication in Medicine”
The DICOM Standard allows to get pixel data for each produced
image and to associate specific information to them:
name of the patient,
type of examination, hospital,
date of examination,
type of acquisition etc...
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DICOM
Header:
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Computer Aided Diagnosis
• Computed Aided Diagnosis (CAD) is diagnosis made by a
radiologist when the output of computerized image analysis methods
has been incorporated into his or her medical decision-making
process.
• CAD may be interpreted broadly to incorporate both
• the detection of the abnormality task and
• the classification task: likelihood that the abnormality
represents a malignancy
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Motivation for CAD systems
The amount of image data acquired during a CT scan is
becoming overwhelming for human vision and the overload of
image data for interpretation may result in oversight errors.
Computed Aided Diagnosis for:
• Breast Cancer
• Lung Cancer
– A thoracic CT scan generates about 240 section images for
radiologists to interpret.
• Colon Cancer
– CT colonography (virtual colonoscopy) is being examined as a
potential screening device (400-700 images)
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CAD for Breast Cancer
A mammogram is an X-ray of breast tissue used as a screening
tool searching for cancer when there are no symptoms of anything
being wrong. A mammogram detects lumps, changes in breast
tissue or calcifications when they're too small to be found in a
physical exam.
• Abnormal tissue shows up a
dense white on
mammograms.
• The left scan shows a
normal breast while the
right one shows malignant
calcifications.
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CAD for Lung Cancer
• Identification of lung nodules in thoracic CT scan;
the identification is complicated by the blood vessels
• Once a nodule has been detected, it may be
quantitatively analyzed as follows:
• The classification of the nodule as
benign or malignant
• The evaluation of the temporal size in
the nodule size.
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CAD for Colon Cancer
• Virtual colonoscopy (CT colonography) is a minimally invasive
imaging technique that combines volumetrically acquired helical
CT data with advanced graphical software to create two and threedimensional views of the colon.
Three-dimensional endoluminal view of the colon showing the
appearance of normal haustral folds and a small rounded polyp.
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Role of Image Analysis & Machine
Learning for CAD
• An overall scheme for computed aided diagnosis systems
Lesion /
Abnormality
Segmentation
Organ
Segmentation
- Breast Images
- Thoracic Images
- Breast Boundary
- Lungs
- Colon
- Nodule
- Polyps
Classification
Feature
Extraction
- Texture
- Shape
- Geometrical
properties
Evaluation &
Interpretation
- Malignant
- Benign
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Texture Classification of Tissues
in CT Chest/Abdomen
A. Pixel-level Classification:
- tissue segmentation
- context-sensitive tools for radiology reporting
-
Pixel Level Texture
Extraction
Pixel Level
Classification
d1 , d 2 , d k 
Organ Segmentation
tissue _ label 
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Pixel-level Texture Extraction
• Consider texture around the pixel of interest.
• Capture texture characteristic based on
estimation of joint conditional probability
of pixel pair occurrences Pij(d,θ).
Neighborhood
of a pixel
– Pij denotes the normalized co-occurrence matrix of
specify by displacement vector (d) and angle (θ).
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Haralick Texture Features
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Haralick Texture Features
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Examples of Texture Images
Texture images: original image, energy and cluster tendency, respectively.
M. Kalinin, D. S. Raicu, J. D. Furst, D. S. Channin,, " A Classification Approach for Anatomical Regions Segmentation", The IEEE
International Conference on Image Processing (ICIP), Genoa, Italy, September 11-14, 2005.
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Texture Classification of Tissues
in CT Chest/Abdomen
Example of Liver Segmentation:
(J.D. Furst, R. Susomboon, and D.S. Raicu, "Single Organ
Segmentation Filters for Multiple Organ Segmentation", IEEE 2006 International Conference of the Engineering in
Medicine and Biology Society (EMBS'06))
Original Image
Initial Seed at 90%
Split & Merge at 85%
Split & Merge at 80%
Region growing at 70% Region growing at 60% Segmentation Result
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Texture Classification of Tissues
in CT Chest/Abdomen
B. Patch-level Classification:
- creation of an electronic handbook of normal tissues
in CT scans including visual and quantitative samples,
and tools to annotate, browse and retrieve samples.
Patch Samples
-
Ground truth:
tissue names
liver
liver
kidney
fat
muscle
trabecular bone
…
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…
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Texture Classification of Tissues
in CT Chest/Abdomen
B. Patch-level Classification (cont.):
Texture quantification
-
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Texture Classification of Tissues
in CT Chest/Abdomen
B. Patch-level Classification (cont.):
-
Supervised learning (classification) of the mappings between
texture features and type of pure patch
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IF F13<.2 and F16
>.8 THEN LIVER (p=.95)
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Evaluation & Interpretation
• Sensitivity: the ratio between true positives and total
positives
• Specificity: the ratio between true negatives and total
negatives
• Receiver Operator Characteristic (ROC)
A true positive is an abnormality classified as malignant
when it is actually malignant.
A true negative is an abnormality classified as benign when
it is actually benign.
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Evaluation & Interpretation
• Receiver Operator Characteristic (ROC) curves for distinction
between benign and malignant nodules on high-resolution CT.
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Texture Classification of Tissues
in CT Chest/Abdomen
Organ -, patch -, and pixel - level classification of spinal cord, liver, heart,
kidneys and spleen using decision trees:
Organ Level
Organ
Pure Patch Level
Pixel-level (9 x 9)
Pixel level (13 x 13)
Sensitivity
Specificity
Sensitivity
Specificity
Sensitivity
Specificity
Sensitivity
Specificity
100.0%
97.6%
97.7%
99.3%
100.0%
96.3%
100.0%
99.2%
Liver (259)
73.8%
95.9%
91.9%
97.9%
100.0%
99.0%
100.0%
98.4%
Heart (77)
73.6%
97.2%
79.2%
98.3%
81.1%
99.5%
66.7%
100.0%
Kidney
(225)
86.2%
97.8%
91.6%
97.1%
78.9%
98.0%
96.6%
93.0%
Spleen
(98)
70.5%
95.1%
65.3%
98.5%
94.4%
95.5%
100.0%
97.6%
Backbone
(44)
-
• D. Xu, J. Lee, D.S. Raicu, J.D. Furst, D. Channin. "Texture Classification of Normal Tissues in Computed
Tomography", The 2005 Annual Meeting of the Society for Computer Applications in Radiology, Florida.
• D. Channin, D. S. Raicu, J. D. Furst, D. H. Xu, L. Lilly, C. Limpsangsri, "Classification of Tissues in Computed
Tomography using Decision Trees", Poster and Demo, The 90th Scientific Assembly and Annual Meeting
of Radiology Society of North America (RSNA04), Chicago, 2004.
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Texture Classification of Tissues
in CT Chest/Abdomen
Low-level
features: texture
CT images
Classification &
Association
Techniques
Image &
Textual
Feature
Extraction
Patient
demographics &
Radiologist
Annotations
High-level
features:
diagnosis, tissue
labels
Classification,
Segmentation &
Annotation
Interpretation &
evaluation:
sensitivity,
specificity
Diagram of a Classification System
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Classification models: challenges
(a) Optimal selection of an adequate set of textural features
is a challenge, especially with the limited data we often have
to deal with in clinical problems. Consequently, the
effectiveness of any classification system will always be
conditional on two things:
(i) how well the selected features describe the tissues
(ii) how well the study group reflects the overall target
patient population for the corresponding diagnosis
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Classification models: challenges
(b) how other type of information can be incorporated into the
classification models:
- metadata
- image features from other imaging modalities
(need of image fusion)
(c) how stable and general the classification models are
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Content-based medical image
retrieval (CBMS) systems
Definition of Content-based Image Retrieval:
Content-based image retrieval is a technique for retrieving
images on the basis of automatically derived image
features such as texture and shape.
-
Applications of Content-based Image Retrieval:
• Teaching
• Research
• Diagnosis
• PACS and Electronic Patient Records
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Diagram of a CBIR
Image Database
Image Features
Feature Extraction
[D1, D2,…Dn]
Similarity Retrieval
Query Image
Feedback
Algorithm
User Evaluation
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http://viper.unige.ch/~muellerh/demoCLEFmed/index.php
Query Results
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CBIR as a Diagnosis Aid
An image retrieval system can help when the diagnosis
depends strongly on direct visual properties of images in the
context of evidence-based medicine or case-based
reasoning.
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CBIR as a Teaching Tool
An image retrieval system will allow students/teachers to browse
available data themselves in an easy and straightforward fashion by
clicking on “show me similar images”.
Advantages:
- stimulate self-learning and a comparison of similar cases
- find optimal cases for teaching
Teaching files:
• Casimage: http://www.casimage.com
• myPACS: http://www.mypacs.net
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CBIR as a Research Tool
Image retrieval systems can be used:
• to complement text-based retrieval methods
• for visual knowledge management whereby the images
and associated textual data can be analyzed together
• multimedia data mining can be applied to
learn the unknown links between visual
features and diagnosis or other patient
information
• for quality control to find images that might have been
misclassified
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CBIR as a tool for lookup and
reference in CT chest/abdomen
• Case Study: lung nodules retrieval
– Lung Imaging Database Resource for Imaging Research
http://imaging.cancer.gov/programsandresources/Inf
ormationSystems/LIDC/page7
– 29 cases, 5,756 DICOM images/slices, 1,143 nodule images
– 4 radiologists annotated the images using 9 nodule
characteristics: calcification, internal structure, lobulation,
malignancy, margin, sphericity, spiculation, subtlety, and texture
• Goals:
– Retrieve nodules based on image features:
• Texture, Shape, and Size
– Find the correlations between the image features and the
radiologists’ annotations
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CBIR as a tool for lung nodule
lookup and reference
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Choose a nodule
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Choose an image
feature& a similarity
measure
M. Lam, T. Disney, M. Pham, D. Raicu, J. Furst, “Content-Based Image Retrieval for
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2006
Pulmonary
Tomography Nodule Images”, SPIE Medical Imaging 42
Conference,
San Diego, CA, February 2007
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Retrieved
Images
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CBIR systems: challenges
•Type of features
• image features:
- texture features: statistical, structural,
model and filter-based
- shape features
• textual features (such as physician annotations)
• Similarity measures
-point-based and distribution based metrics
• Retrieval performance:
• precision and recall
• clinical evaluation
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Image features and physician
annotations correlations
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Malignancy regression model
Characteristics
Regression
Coefficients
Calcification
InternalStructure
Lobulation
Adj_R2 = 0.990
Malignancy
Margin
Sphericity
F-value = 963.560
p-value = 0.000
(Constant)
gabormean_1_2
MinIntensityBG
Energy
gabormean_0_1
IntesityDifference
inverseVariance
gabormean_1_1
gabormean_2_1
Correlation
clusterTendency
ConvexPerimeter
5.377275
-0.02069
0.003819
-28.5314
-0.00315
0.000272
6.317133
0.009743
-0.00667
-0.39183
5.16E-06
-0.00291
p-value
1.64E-54
7.80E-07
3.30E-82
3.31E-12
5.80E-14
0.003609
3.41E-05
0.000259
5.79E-05
5.67E-05
0.000131
0.023032
Spiculation
Subtlety
Texture
Estimated Malignancy = 5.377275 - 0.02069 gabormean_1_2 + 0.003819 MinIntensityBG
- 28.5314 energy - 0.00315 gabormean_0_1
+ 0.000272 IntesityDifference + 6.317133 inverseVariance
+ 0.009743 gabormean_1_1 - 0.00667 gabormean_2_1
- 0.39183 correlation + 5.16E-06 clusterTendency
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- 0.00291 ConvexPerimeter
Multiple Regression Models
E. Varutbangkul, J. G. Cisneros, D. S. Raicu, J. D. Furst, D. S. Channin, S. G. Armato III, "Semantics and Image Content Integration for
Pulmonary Nodule Interpretation in Thoracic Computed Tomography", SPIE Medical Imaging Conference, San Diego, CA, February 2007
Characteristics
Entire dataset
(1106)
At least 2
radiologists agreed
At least 3
radiologists agreed
Calcification
0.397
0.578 (884)
0.645 (644)
Internal Structure
0.417
- (855)
- (659)
Lobulation
0.282
0.559 (448)
0.877 (137)
Malignancy
0.310
0.641 (489)
0.990 (107)
Margin
0.403
0.376 (519)
- (245)
Sphericity
0.239
0.481 (575)
0.682 (207)
Spiculation
0.320
0.563 (621)
0.840 (228)
Subtlety
0.301
0.282 (659)
0.491 (360)
Texture
0.181
0.473 (736)
0.843 (437)
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Image-Driven Ontologies for CT
chest interpretation
Texture definition:
Margin definition:
Nodule internal texture, e.g., nonsolid,
part solid, or solid texture
How well defined the margin of the
nodule is (poorly or sharp)
Circularity definition:
Solidity definition:
division of ‘area of the nodule’ by
‘area of a circle with the same convex
perimeter of the nodule’
The proportion of the pixels in the
convex hull that are also in the region
(Area/ConvexArea)
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Image-based Ontology: challenges
• Identify type of features and their values for certain
physician annotations
Example: What is the “Gaborness” image
representation for a particular annotation?
• Build a CT- RADS visual atlas to standardize chest
reporting
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Ideal CAD Workstation?
It will have the human abilities
• to transfer acquired knowledge to new tasks,
• to adapt to the diagnostic problem,
• to choose image features that are relevant to the clinical task
and to analyze the image
• to offer diagnostic suggestions, and, finally,
• to justify the suggestions on the basis of available reference
data.
That CAD system will be a true partner to the diagnostic radiologist.
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uestions ?
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