Automated segmentation and volumetric analysis of renal cortex, medulla, and pelvis based on non-contrast-enhanced T1- and T2-weighted MR images

• Published in: Magma (New York, N.Y.) N.Y.), 2014-10, Vol.27 (5), p.445-454
• Main Authors: Will, Susanne, Martirosian, Petros, Würslin, Christian, Schick, Fritz
• Format: Article
• Language: English
• Subjects: Algorithms; Biomedical Engineering and Bioengineering; Computer Appl. in Life Sciences; Feasibility Studies; Health Informatics; Healthy Volunteers; Humans; Image Enhancement - methods; Image Processing, Computer-Assisted - methods; Imaging; Kidney - anatomy & histology; Magnetic Resonance Imaging - methods; Medicine; Medicine & Public Health; Pattern Recognition, Automated; Radiology; Reproducibility of Results; Research Article; Solid State Physics
• ISSN: 0968-5243; 1352-8661
• DOI: 10.1007/s10334-014-0429-4

Object The aim of our study was to enable automatic volumetry of the entire kidneys as well as their internal structures (cortex, medulla, and pelvis) from native magnetic resonance imaging (MRI) data sets. Materials and methods Segmentation of the entire kidneys and differentiation of their internal structures were performed in 12 healthy volunteers based on non-contrast-enhanced T1- and T2-weighted MR images. Two data sets (each acquired in one breath-hold) were co-registered using a rigid registration algorithm compensating for possible breathing-related displacements. An automatic algorithm based on thresholding and shape detection segmented the kidneys into their compartments and was compared to a manual labeling procedure. Results The resulting kidney volumes of the automated segmentation correlated well with those created manually ( R 2  = 0.96). Average volume errors were determined to be 4.97 ± 4.08 % (entire kidney parenchyma), 7.03 ± 5.56 % (cortex), 12.33 ± 7.35 % (medulla), and 17.57 ± 14.47 % (pelvis). The variation of the kidney volume resulting from the automatic algorithm was found to be 4.76 % based on the measuring of one volunteer with three independent examinations. Conclusion The results demonstrate the feasibility of an accurate and repeatable automatic segmentation of the kidneys and their internal structures from non-contrast-enhanced magnetic resonance images.