TY - GEN
T1 - Comparison of traditional brain segmentation tools with 3D self-organizing maps
AU - Dean, David
AU - Subramanyan, Krishnamurthy
AU - Kamath, Janardhan
AU - Bookstein, Fred
AU - Wilson, David
AU - Kwon, David
AU - Buckley, Peter
PY - 1997/1/1
Y1 - 1997/1/1
N2 - Algorithm-assisted 3D MR brain segmentation may be significantly faster than manual methods and produce visually pleasing results. We tested two- and three-dimensional region growing (2DRG and 3DRG) and selforganizing map (SOM) algorithms for segmentation of the cerebral ventricles. The SOM algorithm provides the greatest times savings, 12:1, over manual segmentation. Concern for reproducibility of algorithm-assisted segmentation motivated an intra-operator comparative study of these and manual segmentation methods. One of us, DK, segmented the cerebral ventricles from 5 3D MR-scan data sets three times manually and with the three algorithms. When variability is measured as the shape variance of derived landmarks sets, the three algorithm-assisted methods show less intra-operator variability than manual segmentation. The 2DRG and 3DRG segmentations show more variability than SOM. Of the 4 methods, SOM segmentation requires the fewest operator decisions.
AB - Algorithm-assisted 3D MR brain segmentation may be significantly faster than manual methods and produce visually pleasing results. We tested two- and three-dimensional region growing (2DRG and 3DRG) and selforganizing map (SOM) algorithms for segmentation of the cerebral ventricles. The SOM algorithm provides the greatest times savings, 12:1, over manual segmentation. Concern for reproducibility of algorithm-assisted segmentation motivated an intra-operator comparative study of these and manual segmentation methods. One of us, DK, segmented the cerebral ventricles from 5 3D MR-scan data sets three times manually and with the three algorithms. When variability is measured as the shape variance of derived landmarks sets, the three algorithm-assisted methods show less intra-operator variability than manual segmentation. The 2DRG and 3DRG segmentations show more variability than SOM. Of the 4 methods, SOM segmentation requires the fewest operator decisions.
UR - http://www.scopus.com/inward/record.url?scp=21744439205&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=21744439205&partnerID=8YFLogxK
U2 - 10.1007/3-540-63046-5_32
DO - 10.1007/3-540-63046-5_32
M3 - Conference contribution
AN - SCOPUS:21744439205
SN - 3540630465
SN - 9783540630463
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 393
EP - 398
BT - Information Processing in Medical Imaging - 15th International Conference, IPMI 1997, Proceedings
A2 - Duncan, James
A2 - Gindi, Gene
PB - Springer Verlag
T2 - 15th International Conference on Information Processing in Medical Imaging, IPMI 1997
Y2 - 9 June 1997 through 13 June 1997
ER -