Abstract
Extracting 3D structures from volumetric images like MRI or CT is becoming a routine process for diagnosis based on quantitation, for radiotherapy planning, for surgical planning and image-guided intervention, for studying neurodevelopmental and neurodegenerative aspects of brain diseases, and for clinical drug trials. Key issues for segmenting anatomical objects from 3D medical images are validity and reliability. We have developed VALMET, a new tool for validation and comparison of object segmentation. New features not available in commercial and public-domain image processing packages are the choice between different metrics to describe differences between segmentations and the use of graphical overlay and 3D display for visual assessment of the locality and magnitude of segmentation variability. Input to the tool are an original 3D image (MRI, CT, ultrasound), and a series of segmentations either generated by several human raters and/or by automatic methods (machine). Quantitative evaluation includes intra-class correlation of resulting volumes and four different shape distance metrics, a) percentage overlap of segmented structures (R intersect S)/(R union S), b) probabilistic overlap measure for non-binary segmentations, c) mean/median absolute distances between object surfaces, and maximum (Hausdorff) distance. All these measures are calculated for arbitrarily selected 2D cross-sections and full 3D segmentations. Segmentation results are overlaid onto the original image data for visual comparison. A 3D graphical display of the segmented organ is color-coded depending on the selected metric for measuring segmentation difference. The new tool is in routine use for intra- and inter-rater reliability studies and for testing novel automatic machine-segmentation versus a gold standard established by human experts. Preliminary studies showed that the new tool could significantly improve intra- and inter-rater reliability of hippocampus segmentation to achieve intra-class correlation coefficients significantly higher than published elsewhere.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Bibliography
Bowyer, K.W., Phillips, P., Empirical Evaluation Techniques in Computer Vision, IEEE Computer Society, 1998
Chalana V and Kim Y: A methodology for evaluation of boundary detection algorithms on medical images, IEEE Trans. Med. Imaging 16: 642–652 (1997)
Hogan, R.E., Mark, K.E., Wang, L., Joshi, S., Miller, M.I. and Bucholz, R.D., Mesial Temporal Sclerosis and Temporal Lobe Epilepsy: MR Imaging Deformation-based Segmentation of the Hippocampus in Five Patients, Radiology 216, pp. 291–297, July 2000
IRIS (1999): Interactive Rendering and Image Segmentation, UNC student project spring 1999, Gregg, D., Larsen, E., Neelamkavil, A., Sthapit, S. and Wynn, Chris, Dave Stotts and Guido Gerig, supervisors, http://www.cs.unc.edu/~stotts/COMP145/homes/iris/
Kapur, T., Grimson, E.L., Wells, W.M., and Kikinis, R., Segmentation of brain tissue from magnetic resonance images, Medical Image Analysis, 1(2);109–127, 1996
Klette R, Stiehl SH, Viergever MA, and Vincken KL, eds: Performance Characterization in Computer Vision, Kluwer Academic Publishers (2000)
Niessen, W.J., Bouma, C.J., Vincken, K.L., Viergever, M.A., Error Metrics for Quantitative Evaluation of Medical Image Segmentation, in Performance Characterization in Computer Vision, Kluwer Academic Publishers, pp. 299–311, 2000
Remiejer P, Rasch C, Lebesque JV, and van Herk M: A general methodology for three-dimensional analysis of variation in target volume delineation. Med. Phys. 27: 1961–1970 (1999)
Vincken, K.L., Koster, A.S.E., De Graaf, C.N. and Viergever, M.A., Model-based evaluation of image segmentation methods, in Performance Characterization in Computer Vision, Kluwer Academic Publishers, pp. 299–311, 2000
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gerig, G., Jomier, M., Chakos, M. (2001). Valmet: A New Validation Tool for Assessing and Improving 3D Object Segmentation. In: Niessen, W.J., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001. MICCAI 2001. Lecture Notes in Computer Science, vol 2208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45468-3_62
Download citation
DOI: https://doi.org/10.1007/3-540-45468-3_62
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42697-4
Online ISBN: 978-3-540-45468-7
eBook Packages: Springer Book Archive