Segmentation is a key task in medical image analysis because its accuracy significantly affects
successive steps. Automatic segmentation methods often produce inadequate segmentations,
which require the user to manually edit the produced segmentation slice by slice. Because editing
is time-consuming, an editing tool that enables the user to produce accurate segmentations by
only drawing a sparse set of contours would be needed. This paper describes such a framework
as applied to a single object. Constrained by the additional information enabled by the manually
segmented contours, the proposed framework utilizes object shape statistics to transform the
failed automatic segmentation to a more accurate version. Instead of modeling the object shape,
the proposed framework utilizes shape change statistics that were generated to capture the object
deformation from the failed automatic segmentation to its corresponding correct segmentation.
An optimization procedure was used to minimize an energy function that consists of two terms,
an external contour match term and an internal shape change regularity term. The high accuracy
of the proposed segmentation editing approach was confirmed by testing it on a simulated data
set based on 10 in-vivo infant magnetic resonance brain data sets using four similarity metrics.
Segmentation results indicated that our method can provide efficient and adequately accurate
segmentations (Dice segmentation accuracy increase of 10%), with very sparse contours (only
10%), which is promising in greatly decreasing the work expected from the user.
Mahmoud Mostapha, Jared Vicory, Martin Styner, and Stephen Pizer, "A segmentation editing framework based on shape change statistics," Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101331E (Presented at SPIE Medical Imaging: February 14, 2017; Published: 24 February 2017); https://doi.org/10.1117/12.2250023.
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