Content: FEM intro FE workflow improvements with MITK Results Retrospection
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1 Supporting the finite element modelling workflow with MITK Workbench Thomas Fitze Zurich University of Applied Sciences, Winterthur, Switzerland MITK User Meeting 2015, Heidelberg Content: FEM intro FE workflow improvements with MITK Results Retrospection
2 Finite Element Method (FEM) Images Clinical CT, resolution approximately mm Segmentation INPUT: DICOM OUTPUT: STL Meshing INPUT: STL or STEP OUTPUT: INP Material mapping INPUT: INP, DICOM OUTPUT: Simulation files Boundary conditions INPUT: E.g. DIGITIZER DATA OUTPUT: Simulation input files FE Solution INPUT: SIMULATION INPUT FILES OUTPUT: TEXT FILES
3 Traditional FE pipeline Problems: Time consuming segmentation Lots of steps in different applications User interaction after each step Results vary
4 Goals Integrate all steps into a single application Automate the data flow Use human input where it counts and reduce interaction time to a minimum Speed up segmentation with graph cut
5 Workflow illustrated (1/4) Load image
6 Workflow illustrated (2/4) Draw foreground and background masks
7 Workflow illustrated (3/4) Run GraphCut
8 Workflow illustrated (4/4) Generate surface
9 Plugins Plugins we use from the workbench: DICOM Browser Segmentation Tools Image Cropper Measurement Tools Plugins we added 3D Graph cut Image resampler Surface generation Add padding
10 Summary Interaction reduced significantly Results are comparable to manual segmentation 1 Free and open source (GPLv3) Currently in use at the Institute for Biomechanics (ETH Zurich) and Institute for Mechanical Systems (ZHAW) Available once reviewed or on request: fitz@zhaw.ch 1 Y. Pauchard, T. Fitze, D. Browarnik, A. Eskandari, W. Enns-Bray, H. Pálsson, S. Sigurdsson, Stephen J. Ferguson, Tamara B. Harris, V. Gudnason and B. Helgason. "Interactive Graph-Cut Segmentation for Fast Creation of Finite Element Models from Clinical CT Data for Hip Fracture Prediction." Computer Methods in Biomechanics and Biomedical Engineering, in review.
11 Feedback My personal impressions Did I just miss a feature? Let me know!
12 Feedback (1/3) Data management The data manager is awesome! MITK to ITK adaptors are complicated by nature, but: Did you ever call AccessByItk_n with wrong arguments? I did
13 Feedback on MITK (2/3) Look and feel Some inconsistencies: Changing the color property of a binary image via Data Manager will not update the display. Undo in the segmentation tool will not always (but sometimes) trigger a display update. Display property (outline vs. overlay) of binary images changes depending state and does not obey the global setting found under Preferences. There s a global progress bar, but very few plugins use it.
14 Feedback on MITK (3/3) Build system and deployment The Workbench superbuild is very nice and easy to get started! A way to enable / disable default plugins on the superbuild level would make it more convenient to setup new developers. Deployment of standalone plugins to existing MITK Workbenches would be a helpful feature to establish the Workbench as a platform.
15 Acknowledgements Ben Helgason 1 at ETH (FE modelling) Yves Pauchard 2 at ZHAW (Graph cut) School of Engineering at ZHAW (Funding) 1 Institute for Biomechanics, ETH Zurich, Zurich, Switzerland 2 Visual Computing Lab, Institute of Applied Information Technology, Zurich University of Applied Sciences ZHAW, Winterthur, Switzerland
16 Q & A Got anything else? Don t hesitate to contact me: Thomas Fitze fitz@zhaw.ch
17 Mesh similarity Dataset N=48 femur segmentations Average surface to surface distance 0.21mm Absolute maximal difference (Hausdorff distance) 3.75mm mm Mean overlap (Dice coefficient)
18 Graph cut algorithm Minimize energy function composed of regional, boundary and hard constraints Graph contains Source (=object) and Terminal (=background) nodes, plus on node for each voxel Edge weights encode energy function Finds the global minimum using discrete optimization (max flow / min cut)
19 MITK Graph Cut 3D Plugin Implemented as ITK filter Uses V. Kolmogorovs implementation of the maxflow algorithm 1 Memory intensive ~28GB on an image Thrashing is a huge issue 1. Y. Boykov, V. Kolmogorov. An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Computer Vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, September 2004
20 Workflow with MITK Workbench
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