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Proceedings Paper

Quantitative and qualitative methods for efficient evaluation of multiple 3D organ segmentations
Author(s): Volker Dicken; Annika Hänsch; Jan Moltz; Benjamin Haas; Thomas Coradi; Tomasz Morgas; Jan Klein
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Paper Abstract

Quantitative comparison of automatic results for multi-organ segmentation by means of Dice scores often does not yield satisfactory results. It is especially challenging, when reference contours may be prone to errors. We developed a novel approach that analyzes regions of high mismatch between automatic and reference segmentations. We extract various metrics characterizing these mismatch clusters and compare them to other metrics derived from volume overlap and surface distance histograms by correlating them with qualitative ratings from clinical experts. We show that some novel features based on the mismatch sets or surface distance histograms performed better than the Dice score. We also show how the mismatch clusters can be used to generate visualizations to reduce the workload for visual inspection of segmentation results. The visualizations directly compare reference to automatic result at locations of high mismatch in orthogonal 2D views and 3D scenes zoomed to the appropriate positions. This can make it easier to detect systematic problems of an algorithm or to compare recurrent error patterns for different variants of segmentation algorithms, such as differently parameterized or trained CNN models.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094914 (15 March 2019); doi: 10.1117/12.2512750
Show Author Affiliations
Volker Dicken, Fraunhofer MEVIS (Germany)
Annika Hänsch, Fraunhofer MEVIS (Germany)
Jan Moltz, Fraunhofer MEVIS (Germany)
Benjamin Haas, Varian Medical Systems, Inc. (Switzerland)
Thomas Coradi, Varian Medical Systems, Inc. (Switzerland)
Tomasz Morgas, Varian Medical Systems, Inc. (United States)
Jan Klein, Fraunhofer MEVIS (Germany)

Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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