TY - GEN
T1 - Atlas of classifiers for brain MRI segmentation
AU - Kodner, Boris
AU - Gordon, Shiri
AU - Goldberger, Jacob
AU - Riklin Raviv, Tammy
N1 - Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - We present a conceptually novel framework for brain tissue segmentation based on an Atlas of Classifiers (AoC). The AoC allows a statistical summary of the annotated datasets taking into account both the imaging data and the corresponding labels. It is therefore more informative than the classical probabilistic atlas and more economical than the popular multi-atlas approaches, which require large memory consumption and high computational complexity for each segmentation. Specifically, we consider an AoC as a spatial map of voxel-wise multinomial logistic regression (LR) functions learned from the labeled data. Upon convergence, the resulting fixed LR weights (a few for each voxel) represent the training dataset, which might be huge. Segmentation of a new image is therefore immediate and only requires the calculation of the LR outputs based on the respective voxel-wise features. Moreover, the AoC construction is independent of the test images, providing the flexibility to train it on the available labeled data and use it for the segmentation of images from different datasets and modalities. The proposed method has been applied to publicly available datasets for the segmentation of brain MRI tissues and is shown to outreach commonly used methods. Promising results were obtained also for multi-modal, cross-modality MRI segmentation.
AB - We present a conceptually novel framework for brain tissue segmentation based on an Atlas of Classifiers (AoC). The AoC allows a statistical summary of the annotated datasets taking into account both the imaging data and the corresponding labels. It is therefore more informative than the classical probabilistic atlas and more economical than the popular multi-atlas approaches, which require large memory consumption and high computational complexity for each segmentation. Specifically, we consider an AoC as a spatial map of voxel-wise multinomial logistic regression (LR) functions learned from the labeled data. Upon convergence, the resulting fixed LR weights (a few for each voxel) represent the training dataset, which might be huge. Segmentation of a new image is therefore immediate and only requires the calculation of the LR outputs based on the respective voxel-wise features. Moreover, the AoC construction is independent of the test images, providing the flexibility to train it on the available labeled data and use it for the segmentation of images from different datasets and modalities. The proposed method has been applied to publicly available datasets for the segmentation of brain MRI tissues and is shown to outreach commonly used methods. Promising results were obtained also for multi-modal, cross-modality MRI segmentation.
UR - http://www.scopus.com/inward/record.url?scp=85029725663&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-67389-9_5
DO - 10.1007/978-3-319-67389-9_5
M3 - Conference contribution
AN - SCOPUS:85029725663
SN - 9783319673882
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 36
EP - 44
BT - Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings
A2 - Shi, Yinghuan
A2 - Suk, Heung-Il
A2 - Suzuki, Kenji
A2 - Wang, Qian
PB - Springer Verlag
T2 - 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
Y2 - 10 September 2017 through 10 September 2017
ER -