Heckel, FrankBraunewell, StefanSoza, GrzegorzTietjen, ChristianHahn, Horst K.Timo Ropinski and Anders Ynnerman and Charl Botha and Jos Roerdink2013-11-082013-11-082012978-3-905674-38-52070-5778https://doi.org/10.2312/VCBM/VCBM12/073-080In the past years sophisticated automatic segmentation algorithms for various medical image segmentation problems have been developed. However, there are always cases where automatic algorithms fail to provide an acceptable segmentation. In these cases the user needs efficient segmentation correction tools, a problem which has not received much attention in research. Cases to be manually corrected are often particularly difficult and the image does often not provide enough information for segmentation, so we present an image-independent method for intuitive sketch-based editing of 3D tumor segmentations. It is based on an object reconstruction using variational interpolation and can be used in any 3D modality, such as CT or MRI. We also discuss sketch-based editing in 2D as well as a hole-correction approach for variational interpolation. Our manual correction algorithm has been evaluated on 89 segmentations of tumors in CT by 2 technical experts with 6+ years of experience in tumor segmentation and assessment. The experts rated the quality of our correction tool as acceptable or better in 92.1% of the cases. They needed a median number of 4 correction steps with one step taking 0.4s on average.I.4.6 [Image Processing and Computer Vision]SegmentationI.3.5 [Computer Graphics]Computational Geometry and Object ModelingCurvesurfacesolidand object representationsI.3.6 [Computer Graphics]Methodology and TechniquesInteraction techniques