Abstract
We present a robust algorithm for organ localization from 3D volumes in the presence of large anatomical and contextual variations. The 3D spatial search space is decomposed into two components: slice and pixel, both are modeled in 2D space. For each component, we adopt different learning architectures to leverage respective modeling power on global and local context at three orthogonal orientations. Unlike conventional patch-based scanning schemes in learning-based object detection algorithms, slice scanning along each orientation is applied, which significantly reduces the number of model evaluations. Object search evidence obtained from three orientations and different learning architectures is consolidated through fusion schemes to lead to the target organ location. Experiments conducted using 499 patient CT body scans show promise and robustness of the proposed approach.
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Lu, X., Xu, D., Liu, D. (2016). Robust 3D Organ Localization with Dual Learning Architectures and Fusion. In: Carneiro, G., et al. Deep Learning and Data Labeling for Medical Applications. DLMIA LABELS 2016 2016. Lecture Notes in Computer Science(), vol 10008. Springer, Cham. https://doi.org/10.1007/978-3-319-46976-8_2
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DOI: https://doi.org/10.1007/978-3-319-46976-8_2
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