Fully automatized renal parenchyma volumetry using a support vector machine based recognition system for subject-specific probability map generation in native MR volume data

• Published in: Physics in medicine & biology Vol. 60; no. 22; pp. 8675 - 8693
• Main Authors: Gloger, Oliver, Tönnies, Klaus, Mensel, Birger, Völzke, Henry
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 21.11.2015
• Subjects: Algorithms; Bayesian learning; Computer Simulation; fourier descriptors; Humans; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional; Kidney - anatomy & histology; level set segmentation; Magnetic Resonance Imaging - methods; Models, Biological; Pattern Recognition, Automated; Probability; Sensitivity and Specificity; Support Vector Machine; support vector machines
• ISSN: 0031-9155; 1361-6560; 1361-6560
• DOI: 10.1088/0031-9155/60/22/8675

In epidemiological studies as well as in clinical practice the amount of produced medical image data strongly increased in the last decade. In this context organ segmentation in MR volume data gained increasing attention for medical applications. Especially in large-scale population-based studies organ volumetry is highly relevant requiring exact organ segmentation. Since manual segmentation is time-consuming and prone to reader variability, large-scale studies need automatized methods to perform organ segmentation. Fully automatic organ segmentation in native MR image data has proven to be a very challenging task. Imaging artifacts as well as inter- and intrasubject MR-intensity differences complicate the application of supervised learning strategies. Thus, we propose a modularized framework of a two-stepped probabilistic approach that generates subject-specific probability maps for renal parenchyma tissue, which are refined subsequently by using several, extended segmentation strategies. We present a three class-based support vector machine recognition system that incorporates Fourier descriptors as shape features to recognize and segment characteristic parenchyma parts. Probabilistic methods use the segmented characteristic parenchyma parts to generate high quality subject-specific parenchyma probability maps. Several refinement strategies including a final shape-based 3D level set segmentation technique are used in subsequent processing modules to segment renal parenchyma. Furthermore, our framework recognizes and excludes renal cysts from parenchymal volume, which is important to analyze renal functions. Volume errors and Dice coefficients show that our presented framework outperforms existing approaches.