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Robust Registration of Statistical Shape Models for Unsupervised Pathology Annotation

Rahbani, Dana and Morel-Forster, Andreas and Madsen, Dennis and Lüthi, Marcel and Vetter, Thomas. (2019) Robust Registration of Statistical Shape Models for Unsupervised Pathology Annotation. In: LABELS 2019, HAL-MICCAI 2019, CuRIOUS 2019: Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention, 11851. pp. 13-21.

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Official URL: https://edoc.unibas.ch/75214/

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Abstract

We present a method to automatically label pathologies in volumetric medical data. Our solution makes use of a healthy statistical shape model to label pathologies in novel targets during model fitting. We achieve this using an EM algorithm: the E-step classifies surface points into pathological or healthy classes based on outliers in predicted correspondences, while the M-step performs probabilistic fitting of the statistical shape model to the healthy region. Our method is indepen- dent of pathology type or target anatomy, and can therefore be used for labeling different types of data. The method is able to detect pathologies with higher accuracy than standard robust detection algorithms, which we show using true positive rate and F1 scores. Furthermore, the method provides an estimate of the uncertainty of the synthesized label. The detection also directly improves surface reconstruction results, as shown by a decrease in the average and Hausdorff distances to ground truth. The method can be used for automated diagnosis or as a pre-processing step to accurately label large amounts of images.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Computergraphik Bilderkennung (Vetter)
UniBasel Contributors:Rahbani, Dana G and Madsen, Dennis and Morel, Andreas and Lüthi, Marcel and Vetter, Thomas
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Publisher:Springer Nature LNCS proceedings
ISBN:978-3-030-33641-7
e-ISBN:978-3-030-33642-4
Series Name:Lecture notes in Computer Science
Note:Publication type according to Uni Basel Research Database: Conference paper
Language:English
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Last Modified:06 Apr 2020 13:24
Deposited On:29 Jan 2020 16:58

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