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Comparative Local Quality Assessment of 3D Medical Image Segmentations with Focus on Statistical Shape Model-based Algorithms

Landesberger, Tatiana von and Basgier, Dennis and Becker, Meike (2016):
Comparative Local Quality Assessment of 3D Medical Image Segmentations with Focus on Statistical Shape Model-based Algorithms.
In: IEEE Transactions on Visualization and Computer Graphics, pp. 2537-2549Publishedonline, 22, (12), DOI: 10.1109/TVCG.2015.2501813, [Article]

Abstract

The quality of automatic 3D medical segmentation algorithms needs to be assessed on test datasets comprising several 3D images (i.e., instances of an organ). The experts need to compare the segmentation quality across the dataset in order to detect systematic segmentation problems. However, such comparative evaluation is not supported well by current methods. We present a novel system for assessing and comparing segmentation quality in a dataset with multiple 3D images. The data is analyzed and visualized in several views. We detect and show regions with systematic segmentation quality characteristics. For this purpose, we extended a hierarchical clustering algorithm with a connectivity criterion. We combine quality values across the dataset for determining regions with characteristic segmentation quality across instances. Using our system, the experts can also identify 3D segmentations with extraordinary quality characteristics. While we focus on algorithms based on statistical shape models, our approach can also be applied to cases, where landmark correspondences among instances can be established. We applied our approach to three real datasets: liver, cochlea and facial nerve. The segmentation experts were able to identify organ regions with systematic segmentation characteristics as well as to detect outlier instances.

Item Type: Article
Erschienen: 2016
Creators: Landesberger, Tatiana von and Basgier, Dennis and Becker, Meike
Title: Comparative Local Quality Assessment of 3D Medical Image Segmentations with Focus on Statistical Shape Model-based Algorithms
Language: English
Abstract:

The quality of automatic 3D medical segmentation algorithms needs to be assessed on test datasets comprising several 3D images (i.e., instances of an organ). The experts need to compare the segmentation quality across the dataset in order to detect systematic segmentation problems. However, such comparative evaluation is not supported well by current methods. We present a novel system for assessing and comparing segmentation quality in a dataset with multiple 3D images. The data is analyzed and visualized in several views. We detect and show regions with systematic segmentation quality characteristics. For this purpose, we extended a hierarchical clustering algorithm with a connectivity criterion. We combine quality values across the dataset for determining regions with characteristic segmentation quality across instances. Using our system, the experts can also identify 3D segmentations with extraordinary quality characteristics. While we focus on algorithms based on statistical shape models, our approach can also be applied to cases, where landmark correspondences among instances can be established. We applied our approach to three real datasets: liver, cochlea and facial nerve. The segmentation experts were able to identify organ regions with systematic segmentation characteristics as well as to detect outlier instances.

Journal or Publication Title: IEEE Transactions on Visualization and Computer Graphics
Volume: 22
Number: 12
Uncontrolled Keywords: Forschungsgruppe Visual Search and Analysis (VISA), Forschungsgruppe Medical Computing (MECO), Visual analytics, Comparison, Clustering, Statistical shape models (SSM), Medical image processing, Image segmentation
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Mathematical and Applied Visual Computing
Date Deposited: 03 May 2019 07:11
DOI: 10.1109/TVCG.2015.2501813
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