Automatic Model-Based Segmentation of Medical Images

Transcription

1 Automatic Model-Based Segmentation of Medical Images Cristian Lorenz Jochen Peters, Fabian Wenzel, Jürgen Weese May 26 th 2014

2 Need Medical imaging systems produce a huge amount of patient images with a wealth of information. We need a technology that helps to inspect the data efficiently, derive quantitative information, and use the images for therapy.

3 Need Clinicians talk about anatomy, physiology and disease Imaging systems and applications provide images with gray values Tasks such as the generation of standard views or image-based measurements could be automated and examinations could become more easy and reproducible if the computer would know the anatomy.

4 Challenge The computer must accurately recognize the relevant organ structures.

5 Research Philips is working on image recognition techniques that are able to locate landmarks and identify the boundaries between different types of tissue.

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7 Approaches Deformable Models Determine continuous contour by energy minimization Enforce smoothness Active Shape Models Define parametric point distribution model (PDM) Iterate boundary detection and energy minimization Highly flexible Capability to approximate any shape Limited robustness - + Parametric Pre-defined and limited shape space Limited accuracy + - (Kass, Witkin, Terzopoulos IJCV 1988) 7 (Cootes, Taylor, Cooper, Graham CVIU 1995)

8 Shape-Constrained Deformable Models Embed point-distribution model into deformable model Detect boundary points along mesh normal Attract mesh to boundaries E ext T i 1 w i I(ˆ x I(ˆ x i i ) ) xˆ i c Penalize deviation from shape model E 8 int i v i V v i v j sr m i p m j p i 1 j N ( i) 2 2 J. Weese et al. Proc. IPMI 2001

9 Shape-Constrained Deformable Models Properties 1) Mesh topology is fixed. 2) Deviations from the reference shape are penalized. 3) The vertex distribution is preserved. Vertices and triangles can have labels 1) Anatomical information. 2) Triangle-specific boundary detectors. 3) 9

10 Exemplary Result Attraction towards detected boundary points (1.32 mm average error) 10 Attraction towards detected surfaces (0.93 mm average error) J. Weese et al. Proc. IPMI 2001

11 Mesh Generation Various tools and approaches Mesh generation from labeled images (triangulation) Meshes of simple objects (e.g. sphere, tube) and adaptation to binary image Fusion of different meshes Reduction of mesh resolution 11

12 Shape Variability & Complex Models 12 Multi-affine/linear models allow consistent shape parameterization of complex structures. J. Peters et al. Proc. SPIE MI 2008; O. Ecabert et al. MedIA 2011

13 Basic Boundary Detection Functions (Damped) gradient in mesh direction Clipping criteria Q k x to distinguish different boundaries gray-value to left and right gray-value slope across the boundary F i x n I Histogram normalization, if gray-value scale is not calibrated (e.g. MR or US) g if Q min,max i i x x x 0 limit J. Peters et al. Proc. CARS 05; J. Peters et al. MedIA k Q 2 x k otherwise ±n I x k Q 1 x k [0,1]

14 Boundary Detection Training Define huge set of boundary detectors by evaluating and analyzing clipping criteria on training images. Simulated Search For every triangle, training image, and boundary detector: Simulate boundary detection for various displacements Select detector with minimal Boundary Detection Error. Simulated triangle displacement Boundary detection error (Distance) Known correct tangent plane (training input) Experiments show that Simulated Search increases capture range and accuracy of model adaptation. J. Peters et al. Proc. CARS 05; J. Peters et al. MedIA

15 Model Adaptation Chain & Validation Localization Parametric Adaptation similarity multi-affine Deformable Adaptation Stage of the chain Mean (const. Surface-to-Surface) Error (mm) Localization (GHT) Similarity transformation Piecewise affine transform Deformable adaptation Leave-one-patient-out experiments using 28 images of 13 patients. 15 O. Ecabert et al.; IEEE TMI 2008

16 Extended Model Adaptation Chain Localization Heart chambers Parametric Adaptation similarity multi-affine Heart chambers Deformable Adaptation (freezing, activation, ) Heart Vascular chambers structures Low resolution model High resolution models Multi-linear transformations J. Peters et al. Proc. SPIE MI 2008; O. Ecabert et al. MedIA

17 Software Architecture Model controls generic adaptation engine. Technology can effectively be adapted to new tasks. 17 J. Peters et al. Proc. SPIE MI 2008; O. Ecabert et al. MedIA 2011

18 Different Modalities Examples Different Organs mid-diastole LV-LAX CT-65% MR-60% TEE-65% 18

19 Benchmarking Modality CT 3D SSFP MRI Error (mm) (Siemens) 19 J. Peters et al. MedIA 2010

20 Dynamic Heart Segmentation in CT Chamber Volumes Slow heart rate (53 bpm) Accurate boundary detection with sub-voxel accuracy in CTA image time-series using convolution-based interpolation. 20 J. Peters et al. Proc. FIMH 2009

21 Aortic Valve Stenosis & TAVI Aortic valve stenosis is an abnormal narrowing of the aortic valve that impedes blood flow. In patients with age over 65, aortic valve stenosis is most often caused by calcification. Since a few years, minimally invasive percutaneous valve implantation is feasible. Accurate pre-interventional assessment of the valve anatomy is essential for TAVI. 21 Normal AV stenosis TAVI procedure

22 Aortic Valve Segmentation 22 Integration of detailed aortic valve model into heart model. Exemplary segmentation results after training. I. Wächter et al. Proc. MICCAI 2010

23 AF Ablation Guidance using LAPV Models Atrial fibrillation (AF) is a common cardiac arrhythmia characterized by a chaotic contraction of the atrium. AF is commonly treated by catheter ablation, where tissue around the PVs of the LA is ablated to achieve electrical isolation. X-ray fluoroscopy is commonly used to visualize the catheters during mapping of electrical potentials and ablation. The LAPV anatomy is only clearly visible in x- ray fluoroscopy images, if CA is applied. 23

24 AF Ablation Guidance using LAPV Models Imaging LAPV model Intervention Guidance 24 R. Manzke et al. IEEE TMI 2010; J. Weese et al. Medica Mundi 2009

25 Outlook SCDM Technology - Increasing robustness, accuracy & reliability - More comprehensive, complex & detailed models - More efficient model generation - Less imag. protocol dependence (e.g. multi-vendor) New functionality - Failure detection - Recognition of anatomical variants - Recognition and classification of pathological changes Applications - New clinical applications - Virtual Physiologic Human-type simulations - Recognition & annotation for big data applications 25

26 Acknowledgements Philips Research O. Ecabert * I. Wächter -Stehle T. Stehle A. Groth R. Kneser * H. Lehmann * C. Meyer A. Saalbach H. Barschdorf J. von Berg C. Bürger R. C. Chan S.P.M. Dries D. Geller * M. Grass E. Hansis M. Kaus * D. Kutra H. Nickisch R. Manzke * F. M. Weber 26 Philips Research (cont.) V. Pekar Y.-C. Qian D. Schäfer H. Schmitt H. Schramm * S. Young L. Zagorchev Philips Healthcare N. H. Bakker R. v. d. Boomen T. Ivanc G. Lavi * J. Lessick * S. Lobregt * N. J. Noordhoek * M. E. Olszewski K. Subramanyan * R. Truyen M. Vembar M.J. Walker External R. Bekeredjian S. Bischoff J. Balzer E. Chorianopoulos O. Dössel G. Gitsioudis A. Goshtasby W. Hosch H. Katus H.-U. Kauczor M. Kelm L. Kobbelt G. Korosoglou M. W. Krüger U. Krumsdorf H. Kühl T. McAllister M. Neizel K. Paulsen A. Thiagalingam V. Y. Reddy