TY - GEN
T1 - Valmet
T2 - 4th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2001
AU - Gerig, Guido
AU - Jomier, Matthieu
AU - Chakos, Miranda
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2001.
PY - 2001
Y1 - 2001
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84958182494&partnerID=8YFLogxK
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U2 - 10.1007/3-540-45468-3_62
DO - 10.1007/3-540-45468-3_62
M3 - Conference contribution
AN - SCOPUS:84958182494
SN - 3540426973
SN - 9783540454687
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 516
EP - 523
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2001 - 4th International Conference, Proceedings
A2 - Niessen, Wiro J.
A2 - Viergever, Max A.
PB - Springer Verlag
Y2 - 14 October 2001 through 17 October 2001
ER -