Mauricio Orbes-Arteaga

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PURPOSE Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segmentation method works best for certain applications. Segmentation challenges are a good approach for unbiased(More)
We propose a new Kernel-based Atlas Image Selection computed in the Embedding Representation space (termed KAISER) aiming to support labeling of brain tissue on 3D magnetic resonance (MR) images. KAISER approach provides efficient feature extraction from MR volumes based on an introduced inter-slice kernel (ISK). Thus, using the ISK matrix(More)
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