27.06.20093D contour based local manual correction of liver segmentations1Institute for Medical Image Computing 3D contour based local manual correction.

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Presentation transcript:

D contour based local manual correction of liver segmentations1Institute for Medical Image Computing 3D contour based local manual correction of liver segmentations in CT scans F. Heckel 1, J. H. Moltz 1, V. Dicken 1, B. Geisler 1, H.-C. Bauknecht 2, M. Fabel 3, S. Meier 4, H.-O. Peitgen 1 1 Fraunhofer MEVIS, Institute for Medical Image Computing, Bremen, Germany 2 Charité, Institute for Radiology, Berlin, Germany 3 Christian-Albrechts-University, Department of Diagnostic Radiology, Kiel, Germany 4 Johannes Gutenberg University, Clinic and Out-patients’ Clinic for Diagnostic and Interventional Radiology, Mainz, Germany

D contour based local manual correction of liver segmentations2Institute for Medical Image Computing Overview ›Motivation ›Algorithm ›Results ›Conclusion ›Outlook Motivation | Algorithm | Results | Conclusion | Outlook

D contour based local manual correction of liver segmentations3Institute for Medical Image Computing Motivation ›Liver segmentation is challenging ›Semi-automatic or manual segmentation is time-consuming ›Automatic algorithms not acceptable in all cases »But: most parts are typically correct ›Solution: ›Requirements: Motivation | Algorithm | Results | Conclusion | Outlook automatic segmentation + manual correction local, fast, 3D, intuitive, independent of segmentation algorithm

D contour based local manual correction of liver segmentations4Institute for Medical Image Computing Motivation ›Algorithm is based on manual correction of tumor segmentations [1] ›Challenges in correction of liver segmentations: »Shape is not as compact as a tumor »Surface is not as “smooth” as a tumor »Higher variety of surrounding structures »The liver is much larger than a tumor [1] Heckel, F, Moltz, J. H., Bornemann, L, Dicken, V., Bauknecht, H.-C., Fabel, M., Hittinger, M., Kießling, A., Meier, S., Püsken, M. and Peitgen, H.-O., “3D contour based local manual correction of tumor segmentations in CT scans”, Proceedings of SPIE Medical Imaging, Vol. 7259, 2009 Motivation | Algorithm | Results | Conclusion | Outlook

D contour based local manual correction of liver segmentations5Institute for Medical Image Computing Algorithm ›Simple user interaction: draw the actual border of the liver in one slice ›1 st Step: Live-Wire extrapolation of this user contour »Extract equidistant seed points »Connect seed points using Live-Wire (gradients underneath the user contour are preferred) »Replace part of initial contour with new one Live-Wire paths Seed points User contour Motivation | Algorithm | Results | Conclusion | Outlook

D contour based local manual correction of liver segmentations6Institute for Medical Image Computing Algorithm ›1 st Step continued: »Extrapolate user contour to neighboring slices for 3D correction -Search for seed points in a given search area using a block matching algorithm -Connect the seed points using Live-Wire -Continue until some termination criteria are met Motivation | Algorithm | Results | Conclusion | Outlook

D contour based local manual correction of liver segmentations7Institute for Medical Image Computing Algorithm ›2 nd Step: Morphological postprocessing(ensures coherent, smooth mask) »Opening  removes small artifacts »Connected component analysis  removes separated parts »Closing  closes small gaps Motivation | Algorithm | Results | Conclusion | Outlook

D contour based local manual correction of liver segmentations8Institute for Medical Image Computing Algorithm ›Modifications compared to correction of tumor segmentations: »Performance improvements -Liver VOI is resampled -Usage of multiple CPU-cores »Parameter optimizations -Distance between seed points -Size of reference block and search area for block matching -Live-Wire cost function »Additional termination conditions Motivation | Algorithm | Results | Conclusion | Outlook

D contour based local manual correction of liver segmentations9Institute for Medical Image Computing ›One correction step takes about 1 to 5 seconds (for  100³ VOIs) Slice 101 Slice 111 Results Slice 101 Slice 111 Motivation | Algorithm | Results | Conclusion | Outlook

D contour based local manual correction of liver segmentations10Institute for Medical Image Computing Results ›Evaluation: »4 radiologists, 1 technical expert »51 liver segmentations initially rated as insufficient (24) or acceptable (27) »Predefined upper limit for manual correction time: 5 minutes ›Results: »82% of the segmentations could be improved ›Overall correction times were rated as acceptable Before correction: Rating (count) After Correction: Rating (count / percentage) -- / - (24)0 / + / ++ (21 / 88%) 0 (27)+ / ++ (21 / 78%) Motivation | Algorithm | Results | Conclusion | Outlook

D contour based local manual correction of liver segmentations11Institute for Medical Image Computing Conclusion ›Method can effectively be used for correction of liver segmentations ›Local, fast, 3D, intuitive ›Independent of initial segmentation algorithm ›Works in any MPR ›General approach »Other objects or modalities, as long as -Visually separable -Coherent and compact ›Manual correction of liver segmentations is more challenging Motivation | Algorithm | Results | Conclusion | Outlook

D contour based local manual correction of liver segmentations12Institute for Medical Image Computing Outlook ›Further improvements to live-wire cost function »On the fly training strategies »More robust features ›Improvements to similarity measure for block matching algorithm ›Investigation of error introduced by downsampling ›Feasibility studies for other objects (e.g. other organs) and modalities »Adaptations and improvements if necessary ›Further performance improvements Motivation | Algorithm | Results | Conclusion | Outlook

D contour based local manual correction of liver segmentations13Institute for Medical Image Computing Thanks for your attention! Any Questions? Thanks to: