caa a22 4500 605540985 CHVBK 20210128100914.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s00371-015-1113-z doi (NATIONALLICENCE)springer-10.1007/s00371-015-1113-z Extended surface distance for local evaluation of 3D medical image segmentations [Elektronische Daten] [Roman Getto, Arjan Kuijper, Tatiana von Landesberger] The evaluation of 3D medical image segmentation quality requires a reliable detailed comparison of a reference segmentation with an automatic segmentation. It should be able to measure the quality accurately and, thus, to reveal problematic regions. While several (global) measures, providing a single quality value, are available, the only widely used local measure is the Surface Distance (i.e., point-to-surface distance). This measure, however, has significant drawbacks such as asymmetry and underestimation in distant and differently formed regions. Other available measures have limited suitability for 3D medical segmentation evaluation. We present a more reliable distance measure for assessing and analyzing local differences between automatic and reference (i.e., ground truth) 3D segmentations. We identify and overcome Surface Distance drawbacks, esp. in regions with larger dissimilarities. We evaluated our approach on four real medical image datasets. The results indicate that our measure provides more accurate local distance values. Springer-Verlag Berlin Heidelberg, 2015 Evaluation nationallicence Distance measure nationallicence 3D medical image segmentation nationallicence Segmentation quality nationallicence Local distance nationallicence Mesh distance nationallicence Getto Roman TU Darmstadt, Darmstadt, Germany aut Kuijper Arjan TU Darmstadt and Fraunhofer IGD, Darmstadt, Germany aut von Landesberger Tatiana TU Darmstadt, Darmstadt, Germany aut The Visual Computer Springer Berlin Heidelberg 31/6-8(2015-06-01), 989-999 0178-2789 31:6-8<989 2015 31 371 https://doi.org/10.1007/s00371-015-1113-z text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00371-015-1113-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Getto Roman TU Darmstadt, Darmstadt, Germany aut NATIONALLICENCE 700 1- Kuijper Arjan TU Darmstadt and Fraunhofer IGD, Darmstadt, Germany aut NATIONALLICENCE 700 1- von Landesberger Tatiana TU Darmstadt, Darmstadt, Germany aut NATIONALLICENCE 773 0- The Visual Computer Springer Berlin Heidelberg 31/6-8(2015-06-01), 989-999 0178-2789 31:6-8<989 2015 31 371