Whole body CT scanning is a common diagnosis technique for discovering early signs of metastasis or for
differential diagnosis. Automatic parsing and segmentation of multiple organs and semantic navigation inside
the body can help the clinician in efficiently obtaining accurate diagnosis. However, dealing with the large amount
of data of a full body scan is challenging and techniques are needed for the fast detection and segmentation of
organs, e.g., heart, liver, kidneys, bladder, prostate, and spleen, and body landmarks, e.g., bronchial bifurcation,
coccyx tip, sternum, lung tips. Solving the problem becomes even more challenging if partial body scans are
used, where not all organs are present. We propose a new approach to this problem, in which a network of 1D
and 3D landmarks is trained to quickly parse the 3D CT data and estimate which organs and landmarks are
present as well as their most probable locations and boundaries. Using this approach, the segmentation of seven
organs and detection of 19 body landmarks can be obtained in about 20 seconds with state-of-the-art accuracy
and has been validated on 80 CT full or partial body scans.
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