Real-Time Active Shape Models for Segmentation of 3D Cardiac - PowerPoint PPT Presentation

Real-Time Active Shape Models for Segmentation of 3D Cardiac Ultrasound Jøger Hansegård 1 , Fredrik Orderud 2 , and Stein Inge Rabben 3 1 University of Oslo, Norway 2 Norwegian University of Science and Technology, Norway 3 GE Vingmed Ultrasound, Horten, Norway

Background and aim Background • Rapid global function • Intraoperative monitoring/ trending • Lack of real-time 3D segmentation methods Aim • Real-time 3D segmentation of left ventricle. Jøger Hansegård, Dept. of Informatics

3D Ultrasound data Characteristics • Displayed in real-time • 15-20 frames/sec • ECG-gating over 4 heartbeats Challenges: • Shadows, drop-outs, noise, speckle, reverberations. Jøger Hansegård, Dept. of Informatics

Previous work Traditional deformable models • Level sets, simplex mesh, FEM, statistical shape models • May require hundreds of iterations • Not suitable for real-time operation Kalman filter based methods • Single iteration - Ideal for real-time operation • Blake, Jacob, Comaniciu: 2D contours • Orderud: 3D rigid ellipsoid model – Fast, not physiologically realistic. • Orderud: 3D deformable spline model – Better regional accuracy, not limited to physiologically realistic shapes. Jøger Hansegård, Dept. of Informatics

Kalman filter based segmentation • Parametric deformable model, e.g. spline model, active shape model • Segmentation as estimation – Sequential state estimation techniques to track the model parameters – Computational efficient algorithms, e.g. Kalman- filter Jøger Hansegård, Dept. of Informatics

Processing overview ^ x,P - For each frame: x,P predict correct • Predict contour shape and measure position, using a kinematic - x,P H T R -1 v, model for each model H T R -1 H parameter • Measure edges in proximity of predicted surface • Use measurements to Three-step process for each frame. correct prediction Di r ect cl os ed f or m s ol ut i on i ns t ead of i t er at i ve r ef i nem ent ! Jøger Hansegård, Dept. of Informatics

Deformable model (1/2) 3D active shape model (ASM) • Assume deformation in normal direction n i to reduce Linear model consisting of: computational cost to 1/3. • Average shape • Deformation modes A i Built by PCA on training set Shape controlled by state x l 20 states explains 98% of variation in training set (31 patients). Jøger Hansegård, Dept. of Informatics

Deformable model (2/2) Local transformation Global transformations Deformation + Interpolation Rotation + Scaling + Position Combined state vector Jøger Hansegård, Dept. of Informatics

Local edge detection n Measured • Perform edge detection in p obs surface normal direction of surface. • Use normal displacement n p from predicted to measured Predicted p surface surface p obs • Detected edge maximizes intensity transition Jøger Hansegård, Dept. of Informatics

Experiments Setup Initialization • Average shape/fixed Reference: Manually verified position. surfaces from off-line semi- automated tool, (N=21) • Track for a couple of cycles to get lock. ASM trained on separate population (N=31) Key parameters • End diastolic volume (EDV) Machine: 2.16 GHz Intel Core 2 Duo. • End systolic volume (ESV) • Ejection fraction (EF) Jøger Hansegård, Dept. of Informatics

Initialization Jøger Hansegård, Dept. of Informatics

Examples (1/2) Jøger Hansegård, Dept. of Informatics

Results 2.2 ±1.1 mm point-to-surface error Good agreement in volumes and EF 22% CPU load (video rate) Jøger Hansegård, Dept. of Informatics

Discussion • Real-time • Manual correction difficult • Physiologically realistic • Missing data problematic surfaces – Narrow imaging sector • No user input – Drop-outs • Robust to ultrasound artifacts Jøger Hansegård, Dept. of Informatics

Conclusion We have developed a fully automatic algorithm for real-time segmentation of the left ventricle in 3D cardiac ultrasound. Initial evaluation is promising. A larger scale trial is required to evaluate clinical potential. Jøger Hansegård, Dept. of Informatics

THANKS! Jøger Hansegård, Dept. of Informatics

Measurement sequence 1. Create contour template. 2. Calculate deformed contour, and associated Jacobi matrix based on predicted Measure: Deform: p 0 ,n 0 p, n v, r state. D(p 0 ,X) - X 3. Measure normal n T J x D(...) h displacements based on deformed contour. Jøger Hansegård, Dept. of Informatics

Kalman Filter Implementation Using an extended Kalman Measurement update in filter for tracking information space • Assumption of independent • Enables usage of nonlinear measurements allow deformation models. efficient implementation • Linearizes model around • Create information-vector predicted state. and -matrix from Kinematic prediction measurements • Augment state vector to • Use information filter contain state from last two formulation of Kalman filter for measurement update. successive frames. • Models motion, in addition to state/position Jøger Hansegård, Dept. of Informatics

Real-Time Active Shape Models for Segmentation of 3D Cardiac - PowerPoint PPT Presentation

Real-Time Active Shape Models for Segmentation of 3D Cardiac Ultrasound Jger Hansegrd 1 , Fredrik Orderud 2 , and Stein Inge Rabben 3 1 University of Oslo, Norway 2 Norwegian University of Science and Technology, Norway 3 GE Vingmed

ACTIVE SHAPE MODELS Yogesh Singh Rawat SOC NUS September 19, 2012 Active Shape Models

Last time Fitting an arbitrary shape with active deformable contours Segmentation

A Variational Model for Interactive Shape Prior Segmentation and Real-Time Tracking Manuel

BIL 717 Image Processing Active Contours Variational Segmentation Models Mar. 7, 2016

Shape Segmentation and Applications Shape Segmentation and Applications in Sensor Networks in

Shape Analysis Lasse Riis stergaard Dept. of Health Science and Technology Shape Analysis

Shape Statistics for Image Segmentation with Prior Guillaume Charpiat PhD Defense - 2006,

CS452/652 Registers g Segmentation g Real-Time Global Descriptor Table g Programming

Statistical Shape Models Eigenpatches model regions Assume shape is fixed What if it

Y2K $ 134 billion Traditional Analytics context account real-time rules do not take into

Real-Time Object Detection and Semantic Segmentation Geetank Raipuria Wei Li Corporate

ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time Rudra PK Poudel

Image Segmentation with Gated Shape CNN for Autonomous Driving Jeanine Liebold Intelligent

A Comparative Evaluation of Foreground/Background Sketch-based Mesh Segmentation Algorithms Min

Learning Deep Structured Models for Semantic Segmentation Guosheng Lin Semantic Segmentation

Thank you 1. Pro-active leadership 2. Real leadership take times to give time to further a cause

Project iRAD Integrated Real-time Active Data Interoperability in Action Map of NSW Primary

Texture Analysis and Segmentation Texture Analysis and Segmentation using Modulation Models

Shape Outlier Detection Using Pose Preserving Dynamic Shape Models Chan-Su Lee and Ahmed

Real- Real -Time Systems Time Systems Real- -Time Systems Time Systems Real

RTOS Real-Time Operating Systems Chenyang Lu OS Support for Real-Time Real-Time OS

Real Real- -Time Systems Time Systems Designing a real- Designing a real -time system time

Real-time raise alerts Real-Time Real-time with historical Dashboard Correlate

Segmentation of Counting Processes and Dynamical Models PhD Thesis Defense Mokhtar Zahdi Alaya