Automatized spleen segmentation in non-contrast-enhanced MR volume data using subject-specific shape priors

• Published in: Physics in medicine & biology Vol. 62; no. 14; pp. 5861 - 5883
• Main Authors: Gloger, Oliver, Tönnies, Klaus, Bülow, Robin, Völzke, Henry
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 26.06.2017
• Subjects: Algorithms; Automation; Humans; Imaging, Three-Dimensional - methods; Magnetic Resonance Imaging; non-contrast-enhanced; prior shape level set segmentation; Probability; Spleen - diagnostic imaging; subject-specific prior shape knowledge; Support Vector Machine; support vector machines; T1-weighted MR imaging
• ISSN: 0031-9155; 1361-6560; 1361-6560
• DOI: 10.1088/1361-6560/aa766e

To develop the first fully automated 3D spleen segmentation framework derived from T1-weighted magnetic resonance (MR) imaging data and to verify its performance for spleen delineation and volumetry. This approach considers the issue of low contrast between spleen and adjacent tissue in non-contrast-enhanced MR images. Native T1-weighted MR volume data was performed on a 1.5 T MR system in an epidemiological study. We analyzed random subsamples of MR examinations without pathologies to develop and verify the spleen segmentation framework. The framework is modularized to include different kinds of prior knowledge into the segmentation pipeline. Classification by support vector machines differentiates between five different shape types in computed foreground probability maps and recognizes characteristic spleen regions in axial slices of MR volume data. A spleen-shape space generated by training produces subject-specific prior shape knowledge that is then incorporated into a final 3D level set segmentation method. Individually adapted shape-driven forces as well as image-driven forces resulting from refined foreground probability maps steer the level set successfully to the segment the spleen. The framework achieves promising segmentation results with mean Dice coefficients of nearly 0.91 and low volumetric mean errors of 6.3%. The presented spleen segmentation approach can delineate spleen tissue in native MR volume data. Several kinds of prior shape knowledge including subject-specific 3D prior shape knowledge can be used to guide segmentation processes achieving promising results.