3D contour based local manual correction of tumor segmentations in CT scans

Frank Heckel; Jan Hendrik Moltz; Lars Bornemann; Volker Dicken; Hans-Christian Bauknecht; Michael Fabel; Markus Hittinger; Andreas Kießling; Stephan Meier; Michael Püsken; Heinz-Otto Peitgen

doi:10.1117/12.812248

27 March 2009 3D contour based local manual correction of tumor segmentations in CT scans

Frank Heckel, Jan Hendrik Moltz, Lars Bornemann, Volker Dicken, Hans-Christian Bauknecht, Michael Fabel, Markus Hittinger, Andreas Kießling, Stephan Meier, Michael Püsken, Heinz-Otto Peitgen

Author Affiliations +

Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 72593L (2009) https://doi.org/10.1117/12.812248
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Segmentation is an essential task in medical image analysis. For example measuring tumor growth in consecutive CT scans based on the volume of the tumor requires a good segmentation. Since manual segmentation takes too much time in clinical routine automatic segmentation algorithms are typically used. However there are always cases where an automatic segmentation fails to provide an acceptable segmentation for example due to low contrast, noise or structures of the same density lying close to the lesion. These erroneous segmentation masks need to be manually corrected. We present a novel method for fast three-dimensional local manual correction of segmentation masks. The user needs to draw only one partial contour which describes the lesion's actual border. This two-dimensional interaction is then transferred into 3D using a live-wire based extrapolation of the contour that is given by the user in one slice. Seed points calculated from this contour are moved to adjacent slices by a block matching algorithm. The seed points are then connected by a live-wire algorithm which ensures a segmentation that passes along the border of the lesion. After this extrapolation a morphological postprocessing is performed to generate a coherent and smooth surface corresponding to the user drawn contour as well as to the initial segmentation. An evaluation on 108 lesions by six radiologists has shown that our method is both intuitive and fast. Using our method the radiologists were able to correct 96.3% of lesion segmentations rated as insufficient to acceptable ones in a median time of 44s.

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Frank Heckel, Jan Hendrik Moltz, Lars Bornemann, Volker Dicken, Hans-Christian Bauknecht, Michael Fabel, Markus Hittinger, Andreas Kießling, Stephan Meier, Michael Püsken, and Heinz-Otto Peitgen "3D contour based local manual correction of tumor segmentations in CT scans", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72593L (27 March 2009); https://doi.org/10.1117/12.812248

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