Fully Automated Renal Tissue Volumetry in MR Volume Data Using Prior-Shape-Based Segmentation in Subject-Specific Probability Maps.

Abstract

Organ segmentation in magnetic resonance (MR) volume data is of increasing interest in epidemiological studies and clinical practice. 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 automatic methods to perform organ segmentation. In this paper, we present an automated framework for renal tissue segmentation that computes renal parenchyma, cortex, and medulla volumetry in native MR volume data without any user interaction. We introduce a novel strategy of subject-specific probability map computation for renal tissue types, which takes inter- and intra-MR-intensity variability into account. Several kinds of tissue-related 2-D and 3-D prior-shape knowledge are incorporated in modularized framework parts to segment renal parenchyma in a final level set segmentation strategy. Subject-specific probabilities for medulla and cortex tissue are applied in a fuzzy clustering technique to delineate cortex and medulla tissue inside segmented parenchyma regions. The novel subject-specific computation approach provides clearly improved tissue probability map quality than existing methods. Comparing to existing methods, the framework provides improved results for parenchyma segmentation. Furthermore, cortex and medulla segmentation qualities are very promising but cannot be compared to existing methods since state-of-the art methods for automated cortex and medulla segmentation in native MR volume data are still missing.