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The Uses of Object Shape from Images in Medicine Stephen M. Pizer Kenan Professor Medical Image Display & Analysis Group University of North Carolina Credits: Many on MIDAG, especially Daniel Fritsch, Guido Gerig, Edward Chaney, Elizabeth Bullitt, Stephen Aylward, George Stetten, Gregg Tracton, Tom Fletcher, Andrew Thall, Paul Yushkevich, Nikki Levine, Greg Clary, David Chen MIDAG@UNC Object Representation in Medical Image Analysis Extract an object from image(s) [segmentation] Radiotherapy Tumor; plan to hit it Radiosensitive normal anatomy; plan to miss it PD MRA T2 T1 Contrast Surgery Plan to remove it Plan to miss it During surgery, view where it is & effect of treatment Radiology View it to judge its pathology MIDAG@UNC Image Guided Planning of Radiotherapy Planning in 3D Extracting normal anatomy Extracting tumor Planning beam poses MIDAG@UNC Object Representation in Medical Image Analysis Registration (find geometric transformation that brings two images into alignment) Radiotherapy Fuse multimodality images (3D/3D) for planning Verify patient placement (3D/2D) Surgery Fuse multimodality images (3D/3D or 2D) for planning Fuse preoperative (3D) & intraoperative (2D) images Radiology Fuse multimodality images (3D/3D) for diagnosis MIDAG@UNC Object Representation in Medical Image Analysis Shape & Volume Measurement Make physical measurement Radiotherapy Measure effect of therapy on tumor Radiology, Find Neurosciences Use measurement in science of object development how probable an object is Radiology, Neurosciences Use measurement as quantitative input to diagnosis Use measurement in science of object development Use as prior in object extraction E.g., extract the kidney shaped object MIDAG@UNC Object Shape & Volume Measurement: Neurofibromatosis (Gerig, Greenwood) Infant Ventricle from 3D U/S (Gerig, Gilmore) MIDAG@UNC Object Extraction (Segmentation) Approach 1: preanalyze, then fit to model Neurosurgery (MR Angiogram), Radiology (CT) Vessels, ribs, bronchi, bowel via tube skeletons Cardiology (3D Ultrasound) Geometry via clouds of medial atoms Fit appropriately labeled clouds to 3D LV model Cardiac Nuclear Medicine (2D Gated Blood Pool Cine) Extract LV, with previous frame providing model Extraction via deformable m-rep model Shape from extracted LV; analyze shape series Surgery, Radiation Oncology (Multimodality MRI) Extract tumor, using local shape characteristics MIDAG@UNC Extracting Trees of Vessels via Skeletons (Aylward, Bullitt) MIDAG@UNC Presenting Ribs via Tube Skeletons (Aylward) MIDAG@UNC Presenting Bronchi and Lung Vessels via Tube Skeletons (Aylward) MIDAG@UNC Presenting Small Bowel via Tube Skeletons (Aylward) MIDAG@UNC Presenting Blood Vessels Supplying a Tumor for Embolization (Bullitt) Full tree, 2D Subtree, 2D 3D, from 2 poses MIDAG@UNC Heart Model (G. Stetten) cap cylinder myocardium epicardium left ventricle slab mitral valve left atrium MIDAG@UNC Statistical Analysis of Medial Atom Clouds (G. Stetten) sphere cylinder slab MIDAG@UNC LV Tube Identified by Medial Atom Statistical Analysis (G. Stetten) sphere slab cylinder MIDAG@UNC Mitral Valve Slab Identified by Medial Atom Statistical Analysis (G. Stetten) sphere slab cylinder MIDAG@UNC Automatic LV Extraction via Mitral Valve/LV Tube Axis (G. Stetten) MIDAG@UNC Gated Blood Pool Cardiac LV Cine Shape Analysis (G. Clary) Example sequence 4-sided medial elliptical analysis MIDAG@UNC Object Extraction (Segmentation) Approach 2: deform model to optimize reward for image match + reward for shape normality Radiation Oncology (CT or MRI) Abdominal, pelvic organs Deform m-reps model Neurosciences (MRI or 3D Ultrasound) Internal brain structures Spherical harmonics boundary model Deformable m-reps model Neurosurgery (CT) Vertebrae MIDAG@UNC M- Reps for Medical Image Object Extraction and Presentation (Chen, Thall) MIDAG@UNC Displacements from Figurally Implied Boundary Boundary implied by figural model Boundary after displacements MIDAG@UNC Vertebral M-reps Model MIDAG@UNC Vertebral M-reps Model: Spinous Process Figure MIDAG@UNC Cerebral Ventricle M-reps Model MIDAG@UNC Extraction with Object Shape as a Prior Brain structures (Gerig) MIDAG@UNC Registration Registration (find geometric transformation that brings two images into alignment) Radiotherapy Fuse multimodality images (3D/3D) for planning Verify patient placement (3D/2D) Surgery Fuse multimodality images (3D/3D or 2D) for planning Fuse preoperative (3D) & intraoperative (2D) images Radiology Fuse multimodality images (3D/3D) for diagnosis MIDAG@UNC Image Guided Delivery of Radiotherapy Patient placement Verification of plan via portal image Calculation of new treatment pose MIDAG@UNC Finding Treatment Pose from Portal Radiograph and Planning DRR Patient Setup Planning CT Scan Planning pose Planning DRR Candidate poses Treatment pose Candidate DRRs Portal Image MIDAG@UNC Medial Net Shape Models Medial net Medial nets, positions only MIDAG@UNC Image Match Measurment of M-rep MIDAG@UNC Registration Using Lung Medial Object Model : Reference Radiograph (Levine) Medial net Medial nets, positions only MIDAG@UNC Radiograph/Portal Image Registration (Levine) Intensity Matching Relative to Medial Model Medial net MIDAG@UNC Shape & Volume Measurement Find how probable an object is Training images; Principal components Global vs. global and local Correspondence Hippocampi (Gerig) MIDAG@UNC Modes of Global Deformation Training set: Mode 1: x = xmean + b1p1 Mode 2: x = xmean + b2p2 Mode 3: x = xmean + b3p3 MIDAG@UNC Shape & Volume Measurement Shape Measurement Modes of shape variation across patients Measurement = amount of each mode Hippocampi (Gerig) MIDAG@UNC Multiscale Medial Model From larger scale medial net, interpolate smaller scale medial net and represent medial displacements b. MIDAG@UNC Summary: What shape representation is for in medicine Analysis from images Extract the “anatomic object”-shaped object Register based on the objects Diagnose based on shape and volume Medical science via shape Shape and biology Shape-based diagnostic approaches Shape-based therapy planning and delivery approaches MIDAG@UNC Shape Sciences Medicine Biology Geometry Statistics Image Analysis Computer Graphics MIDAG@UNC The End MIDAG@UNC Options for Primitives Space: xi for grid elements Landmarks: xi described by local geometry Boundary: (xi ,normali) spaced along boundary Figural: nets of diatoms sampling figures MIDAG@UNC Figural Models Figures: successive medial involution o Main figure Protrusions Indentations Separate figures Hierarchy of figures o Relative position Relative width Relative orientation o o o o o o o o MIDAG@UNC Figural Models with Boundary Deviations o Hypothesis o At a global level, a figural model is the most intuitive o o o o o At a local level, boundary deviations are most intuitive o o o MIDAG@UNC Medial Atoms Imply boundary segments with tolerance b rR(- )b x rR( )b Similarity transform equivariant Zoom invariance implies width-proportionality of tolerance of implied boundary boundary curvature distribution spacing along net interrogation aperture for image MIDAG@UNC Need for Special End Primitives Represent non-blobby objects angulated edges, corners, creases still allow rounded edges , corners, creases allow bent edges But Avoid infinitely fine medial sampling Maintain tangency, symmetry principles MIDAG@UNC Coarse-to-fine representation For each of three levels Figural hierarchy For each figure, net chain, successively smaller tolerance For each net tile, boundary displacement chain MIDAG@UNC Multiscale 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 MIDAG@UNC Multiscale 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 MIDAG@UNC Shape Repres’n in Image Analysis Segmentation Find the most probable deformed mean model, given the image Probability involves Probability of the deformed model Probability of the image, given the deformed model MIDAG@UNC Medialness: medial strength of a medial primitive in an image Probability of image | deformed model Sum of boundariness values at implied boundary positions in implied normal directions with apertures proportional to tolerance b rR(- )b x rR( )b Boundariness value Intensity profile distance from mean (at scale) MIDAG@UNC Shape Rep’n in Image Analysis Segmentation Find the most probable deformed mean model, given the image Registration Find the most probable deformation, given the image Shape Measurement Find how probable a deformed model is MIDAG@UNC Object Shape Representations for Medicine to Manufacturing Figural models, at successive levels of tolerance Boundary displacements Work in progress Segmentation and registration tools Statistical analysis of object populations CAD tools, incl. direct rendering … MIDAG@UNC