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Opening Up the "Black Box" of Medical Image Segmentation with Statistical Shape Models

Landesberger, Tatiana von and Andrienko, Gennady and Andrienko, Natalia and Bremm, Sebastian and Kirschner, Matthias and Wesarg, Stefan and Kuijper, Arjan (2013):
Opening Up the "Black Box" of Medical Image Segmentation with Statistical Shape Models.
In: The Visual Computer, pp. 893-905, 29, (9), DOI: 10.1007/s00371-013-0852-y,
[Article]

Abstract

The importance of medical image segmentation increases in fields like treatment planning or computer aided diagnosis. For high quality automatic segmentations, algorithms based on statistical shape models (SSMs) are often used. They segment the image in an iterative way. However, segmentation experts and other users can only asses the final segmentation results, as the segmentation is performed in a "black box manner". Users cannot get deeper knowledge on how the (possibly bad) output was produced. Moreover, they do not see whether the final output is the result of a stabilized process. We present a novel Visual Analytics method, which offers this desired deeper insight into the image segmentation. Our approach combines interactive visualization and automatic data analysis. It allows the expert to assess the quality development (convergence) of the model both on global (full organ) and local (organ areas, landmarks) level. Thereby, local patterns in time and space, e.g., non-converging parts of the organ during the segmentation, can be identified. The localization and specifications of such problems helps the experts creating segmentation algorithms to identify algorithm drawbacks and thus it may point out possible ways how to improve the algorithms systematically. We apply our approach on real-world data showing its usefulness for the analysis of the segmentation process with statistical shape models.

Item Type: Article
Erschienen: 2013
Creators: Landesberger, Tatiana von and Andrienko, Gennady and Andrienko, Natalia and Bremm, Sebastian and Kirschner, Matthias and Wesarg, Stefan and Kuijper, Arjan
Title: Opening Up the "Black Box" of Medical Image Segmentation with Statistical Shape Models
Language: English
Abstract:

The importance of medical image segmentation increases in fields like treatment planning or computer aided diagnosis. For high quality automatic segmentations, algorithms based on statistical shape models (SSMs) are often used. They segment the image in an iterative way. However, segmentation experts and other users can only asses the final segmentation results, as the segmentation is performed in a "black box manner". Users cannot get deeper knowledge on how the (possibly bad) output was produced. Moreover, they do not see whether the final output is the result of a stabilized process. We present a novel Visual Analytics method, which offers this desired deeper insight into the image segmentation. Our approach combines interactive visualization and automatic data analysis. It allows the expert to assess the quality development (convergence) of the model both on global (full organ) and local (organ areas, landmarks) level. Thereby, local patterns in time and space, e.g., non-converging parts of the organ during the segmentation, can be identified. The localization and specifications of such problems helps the experts creating segmentation algorithms to identify algorithm drawbacks and thus it may point out possible ways how to improve the algorithms systematically. We apply our approach on real-world data showing its usefulness for the analysis of the segmentation process with statistical shape models.

Journal or Publication Title: The Visual Computer
Volume: 29
Number: 9
Uncontrolled Keywords: Forschungsgruppe Visual Search and Analysis (VISA), Medical imaging, Medical modeling, Visual analytics, Image segmentation, Statistical shape models (SSM), Spatio-temporal data
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1007/s00371-013-0852-y
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