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.
|Title of host publication||Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings|
|Editors||Yinghuan Shi, Heung-Il Suk, Kenji Suzuki, Qian Wang|
|Number of pages||9|
|State||Published - 2017|
|Event||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 - Quebec City, Canada|
Duration: 10 Sep 2017 → 10 Sep 2017
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||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|
|Period||10/09/17 → 10/09/17|
Bibliographical noteFunding Information:
Acknowledgments. This study was partially supported by the Israel Science Foundation (1638/16 T.R.R) and IDF Medical Corps (T.R.R.).
© 2017, Springer International Publishing AG.