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J. Imaging 2017, 3(4), 56;

Rapid Interactive and Intuitive Segmentation of 3D Medical Images Using Radial Basis Function Interpolation

Pattern Recognition Lab, FAU Erlangen-Nuremberg, 91058 Erlangen, Germany
Siemens Healthcare GmbH, 91301 Forchheim, Germany
This paper is an extended version of our paper published in Annual Conference on Medical Image Understanding and Analysis, Edinburgh, UK, 11–13 July 2017.
Author to whom correspondence should be addressed.
Received: 18 October 2017 / Revised: 25 November 2017 / Accepted: 28 November 2017 / Published: 30 November 2017
(This article belongs to the Special Issue Selected Papers from “MIUA 2017”)
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Segmentation is one of the most important parts of medical image analysis. Manual segmentation is very cumbersome, time-consuming, and prone to inter-observer variability. Fully automatic segmentation approaches require a large amount of labeled training data and may fail in difficult or abnormal cases. In this work, we propose a new method for 2D segmentation of individual slices and 3D interpolation of the segmented slices. The Smart Brush functionality quickly segments the region of interest in a few 2D slices. Given these annotated slices, our adapted formulation of Hermite radial basis functions reconstructs the 3D surface. Effective interactions with less number of equations accelerate the performance and, therefore, a real-time and an intuitive, interactive segmentation of 3D objects can be supported effectively. The proposed method is evaluated on 12 clinical 3D magnetic resonance imaging data sets and are compared to gold standard annotations of the left ventricle from a clinical expert. The automatic evaluation of the 2D Smart Brush resulted in an average Dice coefficient of 0.88 ± 0.09 for the individual slices. For the 3D interpolation using Hermite radial basis functions, an average Dice coefficient of 0.94 ± 0.02 is achieved. View Full-Text
Keywords: smart brush, segmentation, 3D interpolation, Hermite radial basis function smart brush, segmentation, 3D interpolation, Hermite radial basis function

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Kurzendorfer, T.; Fischer, P.; Mirshahzadeh, N.; Pohl, T.; Brost, A.; Steidl, S.; Maier, A. Rapid Interactive and Intuitive Segmentation of 3D Medical Images Using Radial Basis Function Interpolation. J. Imaging 2017, 3, 56.

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