Automatic gallbladder segmentation using combined 2D and 3D shape features to perform volumetric analysis in native and secretin-enhanced MRCP sequences
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We aimed to develop the first fully automated 3D gallbladder segmentation approach to perform volumetric analysis in volume data of magnetic resonance (MR) cholangiopancreatography (MRCP) sequences. Volumetric gallbladder analysis is performed for non-contrast-enhanced and secretin-enhanced MRCP sequences.
Materials and methods
Native and secretin-enhanced MRCP volume data were produced with a 1.5-T MR system. Images of coronal maximum intensity projections (MIP) are used to automatically compute 2D characteristic shape features of the gallbladder in the MIP images. A gallbladder shape space is generated to derive 3D gallbladder shape features, which are then combined with 2D gallbladder shape features in a support vector machine approach to detect gallbladder regions in MRCP volume data. A region-based level set approach is used for fine segmentation. Volumetric analysis is performed for both sequences to calculate gallbladder volume differences between both sequences.
The approach presented achieves segmentation results with mean Dice coefficients of 0.917 in non-contrast-enhanced sequences and 0.904 in secretin-enhanced sequences.
This is the first approach developed to detect and segment gallbladders in MR-based volume data automatically in both sequences. It can be used to perform gallbladder volume determination in epidemiological studies and to detect abnormal gallbladder volumes or shapes. The positive volume differences between both sequences may indicate the quantity of the pancreatobiliary reflux.
KeywordsSupport vector machines Fourier descriptors Principal component analysis Non-contrast-enhanced and secretin-enhanced magnetic resonance cholangiopancreatography volume data
This work was funded by the German Research Foundation under grant number GL 785/1-1. Study of Health in Pomerania is part of the Research Network of Community Medicine at the Ernst Moritz Arndt University of Greifswald, which is funded by the German state of Mecklenburg–West Pomerania.
OG developed, implemented, and tested the gallbladder segmentation framework presented, including all modules. He managed the manuscript development and wrote most parts of the manuscript. RB performed manual gallbladder segmentations that were used for training and testing of the framework. He gave helpful advice as a radiologist concerning medical and radiological details. He corrected the manuscript concerning the radiological parts. KT assisted in the revision of the manuscript and corrected several drafts. He gave helpful advice for the manuscript and framework development for the improved approach resulting from his expert knowledge of machine learning and medical image analysis. HV assisted in the development of the manuscript, including corrections from the epidemiological point of view. He gave helpful advice for manuscript development as an epidemiologist and as leader of the Study of Health in Pomerania.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
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