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.
Being able to automatically determine which portion of the human body is shown by a CT volume image offers
various possibilities like automatic labeling of images or initializing subsequent image analysis algorithms. This
paper presents a method that takes a CT volume as input and outputs the vertical body coordinates of its top
and bottom slice in a normalized coordinate system whose origin and unit length are determined by anatomical
landmarks. Each slice of a volume is described by a histogram of visual words: Feature vectors consisting of
an intensity histogram and a SURF descriptor are first computed on a regular grid and then classified into
the closest visual words to form a histogram. The vocabulary of visual words is a quantization of the feature
space by offline clustering a large number of feature vectors from prototype volumes into visual words (or cluster
centers) via the K-Means algorithm. For a set of prototype volumes whose body coordinates are known the
slice descriptions are computed in advance. The body coordinates of a test volume are computed by a 1D rigid
registration of the test volume with the prototype volumes in axial direction. The similarity of two slices is
measured by comparing their histograms of visual words. Cross validation on a dataset of 44 volumes proved
the robustness of the results. Even for test volumes of ca. 20cm height, the average error was 15.8mm.
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