midag@unc the uses of object shape from images in medicine stephen m. pizer kenan professor medical...

MIDAG@UNCMIDAG@UNC

The Uses of Object Shape The Uses of Object Shape from Images in Medicinefrom Images in Medicine The Uses of Object Shape The Uses of Object Shape from Images in Medicinefrom Images in Medicine

Stephen M. PizerStephen M. Pizer

Kenan ProfessorKenan ProfessorMedical Image Display & Analysis GroupMedical Image Display & Analysis Group

University of North CarolinaUniversity of North Carolina

Credits: Many on MIDAG, especiallyCredits: Many on MIDAG, especiallyDaniel Fritsch, Guido Gerig, Edward Chaney, Elizabeth Bullitt, Daniel Fritsch, Guido Gerig, Edward Chaney, Elizabeth Bullitt, Stephen Aylward, George Stetten, Gregg Tracton, Tom Fletcher, Stephen Aylward, George Stetten, Gregg Tracton, Tom Fletcher,

Andrew Thall, Paul Yushkevich, Nikki Levine, Greg Clary, Andrew Thall, Paul Yushkevich, Nikki Levine, Greg Clary, David ChenDavid Chen

Stephen M. PizerStephen M. Pizer

Kenan ProfessorKenan ProfessorMedical Image Display & Analysis GroupMedical Image Display & Analysis Group

University of North CarolinaUniversity of North Carolina

Credits: Many on MIDAG, especiallyCredits: Many on MIDAG, especiallyDaniel Fritsch, Guido Gerig, Edward Chaney, Elizabeth Bullitt, Daniel Fritsch, Guido Gerig, Edward Chaney, Elizabeth Bullitt, Stephen Aylward, George Stetten, Gregg Tracton, Tom Fletcher, Stephen Aylward, George Stetten, Gregg Tracton, Tom Fletcher,

Andrew Thall, Paul Yushkevich, Nikki Levine, Greg Clary, Andrew Thall, Paul Yushkevich, Nikki Levine, Greg Clary, David ChenDavid Chen

MIDAG@UNCMIDAG@UNC

Object Representation in Object Representation in Medical Image AnalysisMedical Image Analysis


Extract an object from image(s) [segmentation]Extract an object from image(s) [segmentation] RadiotherapyRadiotherapy

Tumor; plan to hit itTumor; plan to hit it Radiosensitive normal anatomy; Radiosensitive normal anatomy; plan to miss itplan to miss it

SurgerySurgery Plan to remove itPlan to remove it Plan to miss itPlan to miss it During surgery, view where it is During surgery, view where it is & effect of treatment& effect of treatment

RadiologyRadiology View it to judge its pathologyView it to judge its pathology

Extract an object from image(s) [segmentation]Extract an object from image(s) [segmentation] RadiotherapyRadiotherapy

Tumor; plan to hit itTumor; plan to hit it Radiosensitive normal anatomy; Radiosensitive normal anatomy; plan to miss itplan to miss it

SurgerySurgery Plan to remove itPlan to remove it Plan to miss itPlan to miss it During surgery, view where it is During surgery, view where it is & effect of treatment& effect of treatment

RadiologyRadiology View it to judge its pathologyView it to judge its pathology

PD

MRA T2

T1 Contrast

MIDAG@UNCMIDAG@UNC

Image Guided Planning of RadiotherapyImage Guided Planning of RadiotherapyImage Guided Planning of RadiotherapyImage Guided Planning of Radiotherapy

Planning in 3DPlanning in 3D Extracting normal anatomyExtracting normal anatomy Extracting tumorExtracting tumor Planning beam posesPlanning beam poses

Planning in 3DPlanning in 3D Extracting normal anatomyExtracting normal anatomy Extracting tumorExtracting tumor Planning beam posesPlanning beam poses

MIDAG@UNCMIDAG@UNC



Registration (find geometric transformation Registration (find geometric transformation that brings two images into alignment)that brings two images into alignment)RadiotherapyRadiotherapy

Fuse multimodality images (3D/3D) for planningFuse multimodality images (3D/3D) for planning Verify patient placement (3D/2D)Verify patient placement (3D/2D)

SurgerySurgery Fuse multimodality images (3D/3D or 2D) for planningFuse multimodality images (3D/3D or 2D) for planning Fuse preoperative (3D) & intraoperative (2D) imagesFuse preoperative (3D) & intraoperative (2D) images

RadiologyRadiology Fuse multimodality images (3D/3D) for diagnosisFuse multimodality images (3D/3D) for diagnosis





MIDAG@UNCMIDAG@UNC



Shape & Volume MeasurementShape & Volume Measurement Make physical measurementMake physical measurement

RadiotherapyRadiotherapy Measure effect of therapy on tumorMeasure effect of therapy on tumor

Radiology, NeurosciencesRadiology, Neurosciences Use measurement in science of object developmentUse measurement in science of object development

Find how probable an object isFind how probable an object is Radiology, NeurosciencesRadiology, Neurosciences

Use measurement as quantitative input to diagnosisUse measurement as quantitative input to diagnosis Use measurement in science of object developmentUse measurement in science of object development

Use as prior in object extractionUse as prior in object extraction E.g., extract the kidney shaped objectE.g., extract the kidney shaped object

Shape & Volume MeasurementShape & Volume Measurement Make physical measurementMake physical measurement

RadiotherapyRadiotherapy Measure effect of therapy on tumorMeasure effect of therapy on tumor

Radiology, NeurosciencesRadiology, Neurosciences Use measurement in science of object developmentUse measurement in science of object development

Find how probable an object isFind how probable an object is Radiology, NeurosciencesRadiology, Neurosciences

Use measurement as quantitative input to diagnosisUse measurement as quantitative input to diagnosis Use measurement in science of object developmentUse measurement in science of object development

Use as prior in object extractionUse as prior in object extraction E.g., extract the kidney shaped objectE.g., extract the kidney shaped object

MIDAG@UNCMIDAG@UNC

Object Shape & Volume Measurement:Object Shape & Volume Measurement:Object Shape & Volume Measurement:Object Shape & Volume Measurement:

Infant Ventricle from 3D U/S (Gerig, Gilmore)

Neurofibromatosis (Gerig, Greenwood)

MIDAG@UNCMIDAG@UNC

Object Extraction (Segmentation)Object Extraction (Segmentation)Object Extraction (Segmentation)Object Extraction (Segmentation)

Approach 1: preanalyze, then fit to modelApproach 1: preanalyze, then fit to model Neurosurgery (MR Angiogram), Radiology (CT)Neurosurgery (MR Angiogram), Radiology (CT)

Vessels, ribs, bronchi, bowel via tube skeletonsVessels, ribs, bronchi, bowel via tube skeletons Cardiology (3D Ultrasound)Cardiology (3D Ultrasound)

Geometry via clouds of medial atomsGeometry via clouds of medial atoms Fit appropriately labeled clouds to 3D LV modelFit appropriately labeled clouds to 3D LV model

Cardiac Nuclear Medicine (2D Gated Blood Pool Cine)Cardiac Nuclear Medicine (2D Gated Blood Pool Cine) Extract LV, with previous frame providing modelExtract LV, with previous frame providing model Extraction via deformable m-rep modelExtraction via deformable m-rep model Shape from extracted LV; analyze shape seriesShape from extracted LV; analyze shape series

Surgery, Radiation Oncology (Multimodality MRI)Surgery, Radiation Oncology (Multimodality MRI) Extract tumor, using local shape characteristicsExtract tumor, using local shape characteristics

Approach 1: preanalyze, then fit to modelApproach 1: preanalyze, then fit to model Neurosurgery (MR Angiogram), Radiology (CT)Neurosurgery (MR Angiogram), Radiology (CT)

Vessels, ribs, bronchi, bowel via tube skeletonsVessels, ribs, bronchi, bowel via tube skeletons Cardiology (3D Ultrasound)Cardiology (3D Ultrasound)

Geometry via clouds of medial atomsGeometry via clouds of medial atoms Fit appropriately labeled clouds to 3D LV modelFit appropriately labeled clouds to 3D LV model

Cardiac Nuclear Medicine (2D Gated Blood Pool Cine)Cardiac Nuclear Medicine (2D Gated Blood Pool Cine) Extract LV, with previous frame providing modelExtract LV, with previous frame providing model Extraction via deformable m-rep modelExtraction via deformable m-rep model Shape from extracted LV; analyze shape seriesShape from extracted LV; analyze shape series

Surgery, Radiation Oncology (Multimodality MRI)Surgery, Radiation Oncology (Multimodality MRI) Extract tumor, using local shape characteristicsExtract tumor, using local shape characteristics

MIDAG@UNCMIDAG@UNC

Extracting Trees of Vessels via Extracting Trees of Vessels via Skeletons (Aylward, Bullitt)Skeletons (Aylward, Bullitt)

Extracting Trees of Vessels via Extracting Trees of Vessels via Skeletons (Aylward, Bullitt)Skeletons (Aylward, Bullitt)

MIDAG@UNCMIDAG@UNC

Presenting Ribs via Tube Skeletons Presenting Ribs via Tube Skeletons (Aylward)(Aylward)

Presenting Ribs via Tube Skeletons Presenting Ribs via Tube Skeletons (Aylward)(Aylward)

MIDAG@UNCMIDAG@UNC

Presenting Bronchi and Lung Vessels Presenting Bronchi and Lung Vessels via Tube Skeletons (Aylward)via Tube Skeletons (Aylward)

Presenting Bronchi and Lung Vessels Presenting Bronchi and Lung Vessels via Tube Skeletons (Aylward)via Tube Skeletons (Aylward)

MIDAG@UNCMIDAG@UNC

Presenting Small Bowel via Tube Presenting Small Bowel via Tube Skeletons (Aylward)Skeletons (Aylward)

Presenting Small Bowel via Tube Presenting Small Bowel via Tube Skeletons (Aylward)Skeletons (Aylward)

MIDAG@UNCMIDAG@UNC

Presenting Blood Vessels Supplying a Presenting Blood Vessels Supplying a Tumor for Embolization (Bullitt)Tumor for Embolization (Bullitt)

Presenting Blood Vessels Supplying a Presenting Blood Vessels Supplying a Tumor for Embolization (Bullitt)Tumor for Embolization (Bullitt)

Full tree, 2D Subtree, 2D 3D, from 2 poses

MIDAG@UNCMIDAG@UNC

a

myocardium

left ventricle

left atrium

mitral valve

epicardium

slab

cylinder

cap

Heart Model (G. Stetten)

MIDAG@UNCMIDAG@UNC

Statistical Analysis of Medial Statistical Analysis of Medial Atom Clouds (G. Stetten)Atom Clouds (G. Stetten)

Statistical Analysis of Medial Statistical Analysis of Medial Atom Clouds (G. Stetten)Atom Clouds (G. Stetten)

a

sphere cylinder slab

MIDAG@UNCMIDAG@UNC

sphere

slab cylinder

LV Tube Identified by Medial Atom LV Tube Identified by Medial Atom Statistical Analysis (G. Stetten)Statistical Analysis (G. Stetten)

LV Tube Identified by Medial Atom LV Tube Identified by Medial Atom Statistical Analysis (G. Stetten)Statistical Analysis (G. Stetten)

MIDAG@UNCMIDAG@UNC

sphere

slab cylinder

Mitral Valve Slab Identified by Medial Mitral Valve Slab Identified by Medial Atom Statistical Analysis (G. Stetten)Atom Statistical Analysis (G. Stetten)

Mitral Valve Slab Identified by Medial Mitral Valve Slab Identified by Medial Atom Statistical Analysis (G. Stetten)Atom Statistical Analysis (G. Stetten)

MIDAG@UNCMIDAG@UNC

Automatic LV Extraction via Mitral Automatic LV Extraction via Mitral Valve/LV Tube Axis (G. Stetten)Valve/LV Tube Axis (G. Stetten)

Automatic LV Extraction via Mitral Automatic LV Extraction via Mitral Valve/LV Tube Axis (G. Stetten)Valve/LV Tube Axis (G. Stetten)

MIDAG@UNCMIDAG@UNC

Gated Blood Pool Cardiac LV Gated Blood Pool Cardiac LV Cine Shape Analysis (G. Clary)Cine Shape Analysis (G. Clary)Gated Blood Pool Cardiac LV Gated Blood Pool Cardiac LV

Cine Shape Analysis (G. Clary)Cine Shape Analysis (G. Clary)

Example sequence 4-sided medial elliptical analysis

MIDAG@UNCMIDAG@UNC

Object Extraction (Segmentation)Object Extraction (Segmentation)Object Extraction (Segmentation)Object Extraction (Segmentation)

Approach 2: deform model to optimize reward Approach 2: deform model to optimize reward for image match + reward for shape normalityfor image match + reward for shape normality Radiation Oncology (CT or MRI)Radiation Oncology (CT or MRI)

Abdominal, pelvic organsAbdominal, pelvic organs Deform m-reps modelDeform m-reps model

Neurosciences (MRI or 3D Ultrasound)Neurosciences (MRI or 3D Ultrasound) Internal brain structuresInternal brain structures Spherical harmonics boundary modelSpherical harmonics boundary model Deformable m-reps modelDeformable m-reps model

Neurosurgery (CT)Neurosurgery (CT) VertebraeVertebrae

Approach 2: deform model to optimize reward Approach 2: deform model to optimize reward for image match + reward for shape normalityfor image match + reward for shape normality Radiation Oncology (CT or MRI)Radiation Oncology (CT or MRI)

Abdominal, pelvic organsAbdominal, pelvic organs Deform m-reps modelDeform m-reps model

Neurosciences (MRI or 3D Ultrasound)Neurosciences (MRI or 3D Ultrasound) Internal brain structuresInternal brain structures Spherical harmonics boundary modelSpherical harmonics boundary model Deformable m-reps modelDeformable m-reps model

Neurosurgery (CT)Neurosurgery (CT) VertebraeVertebrae

MIDAG@UNCMIDAG@UNC

M- Reps for Medical Image Object M- Reps for Medical Image Object Extraction and Presentation (Chen, Extraction and Presentation (Chen,

Thall)Thall)

M- Reps for Medical Image Object M- Reps for Medical Image Object Extraction and Presentation (Chen, Extraction and Presentation (Chen,

Thall)Thall)

MIDAG@UNCMIDAG@UNC

Displacements from Displacements from Figurally Implied BoundaryFigurally Implied Boundary

Displacements from Displacements from Figurally Implied BoundaryFigurally Implied Boundary

Boundary implied by figural model Boundary after displacements

MIDAG@UNCMIDAG@UNC

Vertebral M-reps ModelVertebral M-reps ModelVertebral M-reps ModelVertebral M-reps Model

MIDAG@UNCMIDAG@UNC

Vertebral M-reps Model: Vertebral M-reps Model: Spinous Process FigureSpinous Process Figure

Vertebral M-reps Model: Vertebral M-reps Model: Spinous Process FigureSpinous Process Figure

MIDAG@UNCMIDAG@UNC

Cerebral Ventricle M-reps ModelCerebral Ventricle M-reps ModelCerebral Ventricle M-reps ModelCerebral Ventricle M-reps Model

MIDAG@UNCMIDAG@UNC

Extraction with Object Shape as a Prior

Extraction with Object Shape as a Prior

Brain structures (Gerig)Brain structures (Gerig)Brain structures (Gerig)Brain structures (Gerig)

MIDAG@UNCMIDAG@UNC

RegistrationRegistrationRegistrationRegistration









MIDAG@UNCMIDAG@UNC

Image Guided Delivery of RadiotherapyImage Guided Delivery of RadiotherapyImage Guided Delivery of RadiotherapyImage Guided Delivery of Radiotherapy

Patient placementPatient placement Verification of plan via portal imageVerification of plan via portal image Calculation of new treatment poseCalculation of new treatment pose

Patient placementPatient placement Verification of plan via portal imageVerification of plan via portal image Calculation of new treatment poseCalculation of new treatment pose

MIDAG@UNCMIDAG@UNC

Finding Treatment Pose from Finding Treatment Pose from Portal Radiograph and Planning DRRPortal Radiograph and Planning DRR

Finding Treatment Pose from Finding Treatment Pose from Portal Radiograph and Planning DRRPortal Radiograph and Planning DRR

Planning CT Scan

Patient Setup

Planning DRR

Candidate DRRs Portal Image

Planning pose Candidate poses Treatment pose

MIDAG@UNCMIDAG@UNC

Medial Net Shape ModelsMedial Net Shape ModelsMedial Net Shape ModelsMedial Net Shape Models

Medial nets, positions onlyMedial net

MIDAG@UNCMIDAG@UNC

Image Match Measurment of M-repImage Match Measurment of M-repImage Match Measurment of M-repImage Match Measurment of M-rep

MIDAG@UNCMIDAG@UNC

Registration Using Lung Medial Object Registration Using Lung Medial Object Model : Reference Radiograph (Levine)Model : Reference Radiograph (Levine)Registration Using Lung Medial Object Registration Using Lung Medial Object Model : Reference Radiograph (Levine)Model : Reference Radiograph (Levine)

Medial nets, positions onlyMedial net

MIDAG@UNCMIDAG@UNC

Radiograph/Portal Image Registration (Levine)Radiograph/Portal Image Registration (Levine) Intensity Matching Relative to Medial ModelIntensity Matching Relative to Medial Model

Radiograph/Portal Image Registration (Levine)Radiograph/Portal Image Registration (Levine) Intensity Matching Relative to Medial ModelIntensity Matching Relative to Medial Model

Medial net

MIDAG@UNCMIDAG@UNC

Shape & Volume MeasurementShape & Volume MeasurementShape & Volume MeasurementShape & Volume Measurement

Find how probable an object isFind how probable an object isTraining images; Principal componentsTraining images; Principal componentsGlobal vs. global and localGlobal vs. global and localCorrespondenceCorrespondence

Find how probable an object isFind how probable an object isTraining images; Principal componentsTraining images; Principal componentsGlobal vs. global and localGlobal vs. global and localCorrespondenceCorrespondence

Hippocampi (Gerig)

MIDAG@UNCMIDAG@UNC

Modes of Global Modes of Global DeformationDeformationModes of Global Modes of Global DeformationDeformation

Training set:

Mode 1:

Mode 2:

Mode 3:

x = xmean + b1p1

x = xmean + b2p2

x = xmean + b3p3

MIDAG@UNCMIDAG@UNC

Shape & Volume MeasurementShape & Volume MeasurementShape & Volume MeasurementShape & Volume Measurement

Shape MeasurementShape MeasurementModes of shape variation across patientsModes of shape variation across patientsMeasurement = amount of each modeMeasurement = amount of each mode

Shape MeasurementShape MeasurementModes of shape variation across patientsModes of shape variation across patientsMeasurement = amount of each modeMeasurement = amount of each mode

Hippocampi (Gerig)

MIDAG@UNCMIDAG@UNC

Multiscale Medial ModelMultiscale Medial ModelMultiscale Medial ModelMultiscale Medial Model

From larger scale medial net, interpolate smaller scale medial net

and represent medial displacements

From larger scale medial net, interpolate smaller scale medial net

and represent medial displacementsb.

MIDAG@UNCMIDAG@UNC

Summary: What shape Summary: What shape representation is for in representation is for in

medicinemedicine

Summary: What shape Summary: What shape representation is for in representation is for in

medicinemedicine Analysis from imagesAnalysis from images

Extract the “anatomic object”-shaped objectExtract the “anatomic object”-shaped object Register based on the objectsRegister based on the objects Diagnose based on shape and volumeDiagnose based on shape and volume

Medical science via shapeMedical science via shape Shape and biologyShape and biology Shape-based diagnostic approachesShape-based diagnostic approaches Shape-based therapy planning and delivery Shape-based therapy planning and delivery

approachesapproaches

Analysis from imagesAnalysis from images Extract the “anatomic object”-shaped objectExtract the “anatomic object”-shaped object Register based on the objectsRegister based on the objects Diagnose based on shape and volumeDiagnose based on shape and volume

Medical science via shapeMedical science via shape Shape and biologyShape and biology Shape-based diagnostic approachesShape-based diagnostic approaches Shape-based therapy planning and delivery Shape-based therapy planning and delivery

approachesapproaches

MIDAG@UNCMIDAG@UNC

Shape SciencesShape SciencesShape SciencesShape Sciences

MedicineMedicineBiologyBiologyGeometryGeometryStatisticsStatisticsImage AnalysisImage AnalysisComputer GraphicsComputer Graphics

MedicineMedicineBiologyBiologyGeometryGeometryStatisticsStatisticsImage AnalysisImage AnalysisComputer GraphicsComputer Graphics

MIDAG@UNCMIDAG@UNC

The EndThe EndThe EndThe End

MIDAG@UNCMIDAG@UNC

Options for Primitives Options for Primitives Options for Primitives Options for Primitives

Space: Space: xxii for grid elements for grid elements

Landmarks: Landmarks: xxii described by local geometry described by local geometry

Boundary: (Boundary: (xxii ,normal ,normalii) spaced along boundary) spaced along boundary

Figural: nets of diatoms sampling figuresFigural: nets of diatoms sampling figures

Space: Space: xxii for grid elements for grid elements

Landmarks: Landmarks: xxii described by local geometry described by local geometry

Boundary: (Boundary: (xxii ,normal ,normalii) spaced along boundary) spaced along boundary

Figural: nets of diatoms sampling figuresFigural: nets of diatoms sampling figures

MIDAG@UNCMIDAG@UNC

Figural ModelsFigural ModelsFigural ModelsFigural Models

Figures: successive medial involutionFigures: successive medial involution Main figureMain figure ProtrusionsProtrusions IndentationsIndentations Separate figuresSeparate figures

Hierarchy of figuresHierarchy of figures Relative positionRelative position Relative widthRelative width Relative orientationRelative orientation

Figures: successive medial involutionFigures: successive medial involution Main figureMain figure ProtrusionsProtrusions IndentationsIndentations Separate figuresSeparate figures

Hierarchy of figuresHierarchy of figures Relative positionRelative position Relative widthRelative width Relative orientationRelative orientation

o

o

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o

o

o

oo

MIDAG@UNCMIDAG@UNC

Figural Models Figural Models with Boundary Deviationswith Boundary Deviations

Figural Models Figural Models with Boundary Deviationswith Boundary Deviations

HypothesisHypothesisAt a global level, a figural At a global level, a figural

model is the most intuitivemodel is the most intuitive

At a local level, boundary At a local level, boundary deviations are most intuitivedeviations are most intuitive

HypothesisHypothesisAt a global level, a figural At a global level, a figural

model is the most intuitivemodel is the most intuitive

At a local level, boundary At a local level, boundary deviations are most intuitivedeviations are most intuitive

o

o

ooo

o

o

o

oo

MIDAG@UNCMIDAG@UNC

Medial AtomsMedial AtomsMedial AtomsMedial Atoms

Imply boundary segmentsImply boundary segments

with tolerancewith tolerance

Similarity transform equivariantSimilarity transform equivariant Zoom invariance implies width-proportionality ofZoom invariance implies width-proportionality of

tolerance of implied boundarytolerance of implied boundary boundary curvature distributionboundary curvature distribution spacing along netspacing along net interrogation aperture for imageinterrogation aperture for image

Imply boundary segmentsImply boundary segments

with tolerancewith tolerance

Similarity transform equivariantSimilarity transform equivariant Zoom invariance implies width-proportionality ofZoom invariance implies width-proportionality of

tolerance of implied boundarytolerance of implied boundary boundary curvature distributionboundary curvature distribution spacing along netspacing along net interrogation aperture for imageinterrogation aperture for image

b

rR(- )bx

rR()b

MIDAG@UNCMIDAG@UNC

Need for Special End PrimitivesNeed for Special End Primitives

Represent Represent non-blobby objectsnon-blobby objects

angulated edges, corners, creasesangulated edges, corners, creases still allow rounded edges , corners, creasesstill allow rounded edges , corners, creases allow bent edgesallow bent edges

ButBut Avoid infinitely fine medial samplingAvoid infinitely fine medial sampling Maintain tangency, symmetry principlesMaintain tangency, symmetry principles

MIDAG@UNCMIDAG@UNC

Coarse-to-fine representationCoarse-to-fine representationCoarse-to-fine representationCoarse-to-fine representation

For each of three levelsFigural hierarchyFor each figure,

net chain, successively smaller tolerance

For each net tile, boundary displacement

chain

For each of three levelsFigural hierarchyFor each figure,

net chain, successively smaller tolerance

For each net tile, boundary displacement

chain

MIDAG@UNCMIDAG@UNC

Multiscale Medial ModelMultiscale Medial ModelMultiscale Medial ModelMultiscale Medial Model

From larger scale medial net Coarsely sampled Smooother figurally implied boundary Larger tolerance

Interpolate smaller scale medial net Finer sampled More detail in figurally implied boundary Smaller tolerance

Represent medial displacements

From larger scale medial net Coarsely sampled Smooother figurally implied boundary Larger tolerance

Interpolate smaller scale medial net Finer sampled More detail in figurally implied boundary Smaller tolerance

Represent medial displacements

MIDAG@UNCMIDAG@UNC

Multiscale Medial/Boundary ModelMultiscale Medial/Boundary ModelMultiscale Medial/Boundary ModelMultiscale Medial/Boundary Model

From medial net Coarsely sampled, smoother implied

boundary Larger tolerance

Represent boundary displacements along implied normals Finer sampled, more detail in boundary Smaller tolerance

From medial net Coarsely sampled, smoother implied

boundary Larger tolerance

Represent boundary displacements along implied normals Finer sampled, more detail in boundary Smaller tolerance

MIDAG@UNCMIDAG@UNC

Shape Repres’n in Image AnalysisShape Repres’n in Image AnalysisShape Repres’n in Image AnalysisShape Repres’n in Image Analysis

SegmentationSegmentation

Find the most probable deformed Find the most probable deformed mean model, given the imagemean model, given the image

Probability involvesProbability involvesProbability of the deformed modelProbability of the deformed modelProbability of the image, given the Probability of the image, given the

deformed modeldeformed model

SegmentationSegmentation

Find the most probable deformed Find the most probable deformed mean model, given the imagemean model, given the image

Probability involvesProbability involvesProbability of the deformed modelProbability of the deformed modelProbability of the image, given the Probability of the image, given the

deformed modeldeformed model

MIDAG@UNCMIDAG@UNC

Medialness: medial strength of Medialness: medial strength of a medial primitive in an imagea medial primitive in an imageMedialness: medial strength of Medialness: medial strength of a medial primitive in an imagea medial primitive in an image

Probability of image | deformed modelProbability of image | deformed model Sum of boundariness values Sum of boundariness values

at implied boundary positionsat implied boundary positions in implied normal directionsin implied normal directions with apertures proportional to with apertures proportional to

tolerancetolerance

Boundariness value Boundariness value Intensity profile distance from mean (at scale)Intensity profile distance from mean (at scale)

Probability of image | deformed modelProbability of image | deformed model Sum of boundariness values Sum of boundariness values

at implied boundary positionsat implied boundary positions in implied normal directionsin implied normal directions with apertures proportional to with apertures proportional to

tolerancetolerance

Boundariness value Boundariness value Intensity profile distance from mean (at scale)Intensity profile distance from mean (at scale)

b

rR(- )bx

rR()b

MIDAG@UNCMIDAG@UNC

Shape Rep’n in Image AnalysisShape Rep’n in Image AnalysisShape Rep’n in Image AnalysisShape Rep’n in Image Analysis

SegmentationSegmentation Find the most probable deformed mean Find the most probable deformed mean

model, given the imagemodel, given the image RegistrationRegistration

Find the most probable deformation, given the Find the most probable deformation, given the imageimage

Shape MeasurementShape Measurement Find how probable a deformed model isFind how probable a deformed model is

SegmentationSegmentation Find the most probable deformed mean Find the most probable deformed mean

model, given the imagemodel, given the image RegistrationRegistration

Find the most probable deformation, given the Find the most probable deformation, given the imageimage

Shape MeasurementShape Measurement Find how probable a deformed model isFind how probable a deformed model is

MIDAG@UNCMIDAG@UNC

Object ShapeObject ShapeRepresentations for Medicine to Representations for Medicine to

ManufacturingManufacturing

Object ShapeObject ShapeRepresentations for Medicine to Representations for Medicine to

ManufacturingManufacturing Figural models, at successive levels of Figural models, at successive levels of

tolerancetolerance Boundary displacementsBoundary displacements

Work in progressWork in progressSegmentation and registration toolsSegmentation and registration toolsStatistical analysis of object populationsStatistical analysis of object populationsCAD tools, incl. direct renderingCAD tools, incl. direct rendering……

Figural models, at successive levels of Figural models, at successive levels of tolerancetolerance

Boundary displacementsBoundary displacements

Work in progressWork in progressSegmentation and registration toolsSegmentation and registration toolsStatistical analysis of object populationsStatistical analysis of object populationsCAD tools, incl. direct renderingCAD tools, incl. direct rendering……

midag@unc the uses of object shape from images in medicine stephen m. pizer kenan professor medical...

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