Local appearance features for robust MRI brain structure segmentation across scanning protocols
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Local appearance features for robust MRI brain structure segmentation across scanning protocols. / Achterberg, H.C.; Poot, Dirk H. J.; van der Lijn, Fedde; Vernooij, Meike W.; Ikram, M. Arfan; Niessen, Wiro J.; de Bruijne, Marleen.
Medical Imaging 2013: image processing . ed. / Sebastien Ourselin; David R. Haynor. SPIE - International Society for Optical Engineering, 2013. 866905 (Progress in Biomedical Optics and Imaging; No. 36, Vol. 14).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Local appearance features for robust MRI brain structure segmentation across scanning protocols
AU - Achterberg, H.C.
AU - Poot, Dirk H. J.
AU - van der Lijn, Fedde
AU - Vernooij, Meike W.
AU - Ikram, M. Arfan
AU - Niessen, Wiro J.
AU - de Bruijne, Marleen
PY - 2013
Y1 - 2013
N2 - Segmentation of brain structures in magnetic resonance images is an important task in neuro image analysis. Several papers on this topic have shown the benefit of supervised classification based on local appearance features, often combined with atlas-based approaches. These methods require a representative annotated training set and therefore often do not perform well if the target image is acquired on a different scanner or with a different acquisition protocol than the training images. Assuming that the appearance of the brain is determined by the underlying brain tissue distribution and that brain tissue classification can be performed robustly for images obtained with different protocols, we propose to derive appearance features from brain-tissue density maps instead of directly from the MR images. We evaluated this approach on hippocampus segmentation in two sets of images acquired with substantially different imaging protocols and on different scanners. While a combination of conventional appearance features trained on data from a different scanner with multiatlas segmentation performed poorly with an average Dice overlap of 0.698, the local appearance model based on the new acquisition-independent features significantly improved (0.783) over atlas-based segmentation alone (0.728).
AB - Segmentation of brain structures in magnetic resonance images is an important task in neuro image analysis. Several papers on this topic have shown the benefit of supervised classification based on local appearance features, often combined with atlas-based approaches. These methods require a representative annotated training set and therefore often do not perform well if the target image is acquired on a different scanner or with a different acquisition protocol than the training images. Assuming that the appearance of the brain is determined by the underlying brain tissue distribution and that brain tissue classification can be performed robustly for images obtained with different protocols, we propose to derive appearance features from brain-tissue density maps instead of directly from the MR images. We evaluated this approach on hippocampus segmentation in two sets of images acquired with substantially different imaging protocols and on different scanners. While a combination of conventional appearance features trained on data from a different scanner with multiatlas segmentation performed poorly with an average Dice overlap of 0.698, the local appearance model based on the new acquisition-independent features significantly improved (0.783) over atlas-based segmentation alone (0.728).
UR - http://www.scopus.com/inward/record.url?scp=84878296192&partnerID=8YFLogxK
U2 - 10.1117/12.2006038
DO - 10.1117/12.2006038
M3 - Article in proceedings
AN - SCOPUS:84878296192
SN - 9780819494436
T3 - Progress in Biomedical Optics and Imaging
BT - Medical Imaging 2013
A2 - Ourselin, Sebastien
A2 - Haynor, David R.
PB - SPIE - International Society for Optical Engineering
Y2 - 10 February 2013 through 12 February 2013
ER -
ID: 169381109