Effect of different uncertainties in medical image segment error estimation
Interactive segmentation of 3D medical images
S. Kim (TU Delft - Electrical Engineering, Mathematics and Computer Science)
K.A. Hildebrandt – Mentor (TU Delft - Computer Graphics and Visualisation)
Nicolas F. Chaves-de-Plaza – Mentor (TU Delft - Computer Graphics and Visualisation)
T.E.P.M.F. Abeel – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Although automated segmentation of 3D medical images produce near-ideal results, they encounter limitations and occasional errors, necessitating manual intervention for error correction. Recent studies introduce an active learning pipeline as an efficient solution for this, requiring user corrections only on some of the most uncertain parts of
the automatically segmented image. It does so by combining different uncertainty fields, which are various ways to quantify possible errors. We investigate into its individual uncertainty fields and their combination scheme in attempt to validate its methods. Additionally, we replace its methods for estimating uncertainty with another common way to do so, called the ensemble method, to test possible improvements at uncertainty estimation. Results of this research validates the combination method of the active learning pipeline, and shows weak advantages but strong disadvantages of the ensemble method when compared to the combined method of the active learning pipeline.