Efficient classifier generation and weighted voting for atlas-based segmentation: two small steps faster and closer to the combination oracle

Xabier Artaechevarria; Arrate Muñoz-Barrutia; Carlos Ortiz-de-Solórzano

doi:10.1117/12.769401

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11 March 2008 Efficient classifier generation and weighted voting for atlas-based segmentation: two small steps faster and closer to the combination oracle

Xabier Artaechevarria, Arrate Muñoz-Barrutia, Carlos Ortiz-de-Solórzano

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Proceedings Volume 6914, Medical Imaging 2008: Image Processing; 69141W (2008) https://doi.org/10.1117/12.769401
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

Atlas-based segmentation has proven effective in multiple applications. Usually, several reference images are combined to create a representative average atlas image. Alternatively, a number of independent atlas images can be used, from which multiple segmentations of the image of interest are derived and later combined. One of the major drawbacks of this approach is its large computational burden caused by the high number of required registrations. To address this problem, we introduce One Registration, Multiple Segmentations (ORMS), a procedure to obtain multiple segmentations with a single online registration. This can be achieved by pre-computing intermediate transformations from the initial atlas images to an average image. We show that, compared to the usual approach, our method reduces time considerably with little or no loss in accuracy. On the other hand, optimum combination of these segmentations remains an unresolved problem. Different approaches have been adopted, but they are all far from the upper bound of any combination strategy. This is given by the Combination Oracle, which classifies a voxel correctly if any individual segmentation coincides with the ground truth. We present here a novel combination approach, based on weighting the different segmentations according to the mutual information between the test image and the atlas image after registration. We compare this method with other existing combination strategies using microscopic MR images of mouse brains, achieving statistically significant improvement in segmentation accuracy.

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Xabier Artaechevarria, Arrate Muñoz-Barrutia, and Carlos Ortiz-de-Solórzano "Efficient classifier generation and weighted voting for atlas-based segmentation: two small steps faster and closer to the combination oracle", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69141W (11 March 2008); https://doi.org/10.1117/12.769401

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