 Interactive Segmentation For Image Guided Therapy Ohad Shitrit & Tsachi Hershkovich Superviser: Dr. Tammy Riklin Raviv Ben-Gurion University of the Negev.

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Presentation transcript:

 Interactive Segmentation For Image Guided Therapy Ohad Shitrit & Tsachi Hershkovich Superviser: Dr. Tammy Riklin Raviv Ben-Gurion University of the Negev

What are we going to speak about? Tsachi H. & Ohad S.  Computed Tomography  Motivation  Mathematical introduction  Problem definition  Energy  Gradient Descent  The system  Results  Conclusion & Future work

Tsachi H. & Ohad S. Computed Tomography (CT) Spiral Cone-Beam Scanning for Computed Tomography. Ge Wang, 2003 (All rights reserved)

Tsachi H. & Ohad S. Computed Tomography (CT) Spiral Cone-Beam Scanning for Computed Tomography. Ge Wang, 2003 (All rights reserved)  X-Ray Projection using radon transform

Tsachi H. & Ohad S. Computed Tomography (CT)  Radon transform as one dimensional Fourier transform  Reconstructing the image with the inverse Fourier transform

Why is there any need for interactive segmentation ? Tsachi H. & Ohad S.

Why is there any need for interactive segmentation?  Volume estimation is critical for further treatment  Therapist knowledge is essential for final decisions  Fast and accurate analysis might save life Tsachi H. & Ohad S.

“Active Contour” But first things first… Tsachi H. & Ohad S.

Probabilistic Model Based on Gaussian Mixture(GM) Tsachi H. & Ohad S.  We will define the Probability of a Voxel (3D pixel) to belong to the Object Or to the Background:

A CT scan Histogram of a brain with Cerebral hemorrhage Tsachi H. & Ohad S.

Level Set Function -

Tsachi H. & Ohad S. Mathematical Issues

Tsachi H. & Ohad S. [Riklin Raviv, Van Leemput, Menze, Wells, Golland, Medical Image Analysis, 2011]

Tsachi H. & Ohad S. To achieve the optimized segmentation we maximize the joint distribution: [Riklin Raviv, Van Leemput, Menze, Wells, Golland, Medical Image Analysis, 2011]

Tsachi H. & Ohad S. Energy Functional  Using the following relationship :  Allows us to formulate our problem as an energy minimization problem  Summing all contributions from each voxel

Tsachi H. & Ohad S. Image Likelihood Term  Assuming Gaussian Mixtures Model (GMM)

Tsachi H. & Ohad S. Smoothness Term  Objects in nature are continuous  Trade off between smoothness and sensitivity  Fine tuning is needed

Tsachi H. & Ohad S. User Interaction Term

Tsachi H. & Ohad S. Gradient Descent The gradient descent is an iterative process which leads to the minimum of the Energy term.

Tsachi H. & Ohad S. Block Diagram – Entire System [Riklin Raviv, Van Leemput, Menze, Wells, Golland, Medical Image Analysis, 2011]

Entire System 3D

Tsachi H. & Ohad S. Entire System 3D

Tsachi H. & Ohad S. Entire System 3D

Tsachi H. & Ohad S. Entire System 3D

Tsachi H. & Ohad S. Entire System 3D

Tsachi H. & Ohad S. Entire System 3D

Tsachi H. & Ohad S. Entire System 3D

Tsachi H. & Ohad S. Entire System 3D

Tsachi H. & Ohad S. Entire System 3D

Tsachi H. & Ohad S. Entire System 3D

Tsachi H. & Ohad S. Entire System 3D

Tsachi H. & Ohad S. Entire System 3D

Tsachi H. & Ohad S. Entire System 3D

Tsachi H. & Ohad S. Entire System 3D

Tsachi H. & Ohad S. Entire System 3D

Tsachi H. & Ohad S. Performance DiceSensitivitySpecificityAccuracy Automatic 0.874± ± ± ± With user interaction 0.905± ± ± ±0.0022

Tsachi H. & Ohad S. Data  Provided by Dr. Ilan Shelef, Department of Radiology, Soroka University Medical Center and Zlotowski Center for Neuroscience, Ben- Gurion University of the Negev.  Modality: CT Brilliance 64  Resolution: 512x512xZ (Z = ) X x Y x Z = 0.48 x 0.48 x 3 [mm]  Z axis with 1.5[mm] overlap

Conclusion  Semi-automatic segmentation tool  Probabilistic model  User Interface  Collaboration with Soroka Medical Center Tsachi H. & Ohad S.

Future work  Adjustments to other modalities (MRI)  Handle with various of structures  User-Machine dialog in the medical world Tsachi H. & Ohad S.

User Interface

Questions? Thank you. Demo -