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

Abstract

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.

Consensus-based technical recommendations for clinical translation of renal BOLD MRI

“…AI methods have been used for automatic segmentation of the cortex and medulla for renal volumetry based on T 1weighted imaging, with [50] and without gadolinium contrast enhancement [51][52][53]. The ROIs obtained by automatic segmentation of T 1 -weighted images can inform segmentation of the cortex and medulla in a multiparametric MRI protocol.…”

SHIP-MR and Radiology: 12 Years of Whole-Body Magnetic Resonance Imaging in a Single Center

“…Efforts were taken to develop tools. Gloger et al [ 70 ] proposed a probabilistic framework that generates subject-specific probability maps for renal parenchyma tissue. Support vector machines were trained on Fourier descriptors of ground truth segmentations and used as classifiers to recognize and segment characteristic parenchyma parts.…”

Reliable kidney size determination by magnetic resonance imaging in pathophysiological settings

“…This approach requires minimal user interaction and has made contributions to the field of physiology. Fully automated supervised and unsupervised machine learning algorithms have been explored for renal segmentation using convolutional neuronal network models 55,72‐76 …”

“…Fully automated supervised and unsupervised machine learning algorithms have been explored for renal segmentation using convolutional neuronal network models. 55,[72][73][74][75][76] Recent studies on machine learning-based renal segmentation using neural networks reported processing times as good as 1 to 10 s per subject. [76][77][78] Although these processing times are superior to our analytic approach, the effort needed to setup meticulously annotated imaging data that can be used to train, validate and test artificial intelligence algorithms must also be included in order to make a fair benchmarking of processing times.…”