Abstract
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.
Highlights
Segmentation is a common task in the processing of medical images
A great deal of effort has gone into interactive segmentation tools for 2D segmentations as well as 3D interpolations
There are many parameters that affect the result of the 2D segmentation, such as the size of the brush or the initialization step
Summary
Segmentation is a common task in the processing of medical images. It is often a pre-requisite for the further image analysis and can be used for therapy planning and guidance [1]. The spectrum of segmentation techniques available to the clinical applications is broad, ranging from manual slice by slice outlining to fully automatic segmentation. Manual segmentation is still widely used for complex segmentation tasks. Manual annotation of every image slice can be very cumbersome and time-consuming, considering the high resolution of the 3D image volumes [2]. A great deal of effort has gone into interactive segmentation tools for 2D segmentations as well as 3D interpolations. Many segmentation techniques have been developed such as Intelligent
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