Kidney Segmentation in Ultrasound, Magnetic Resonance and Computed Tomography Images: A Systematic Review

BACKGROUND AND OBJECTIVE Segmentation is an essential step in computer-aided diagnosis and treatment planning of kidney diseases. In recent years, several researchers proposed multiple techniques to segment the kidney in medical images from distinct imaging acquisition systems, namely ultrasound, magnetic resonance, and computed tomography. This article aims to present a systematic review of the different methodologies developed for kidney segmentation. METHODS With this work, it is intended to analyze and categorize the different kidney segmentation algorithms, establishing a comparison between them and discussing the most appropriate methods for each modality. For that, articles published between 2010 and 2016 were analyzed. The search was performed in Scopus and Web of Science using the expressions "kidney segmentation" and "renal segmentation". RESULTS A total of 1528 articles were retrieved from the databases, and 95 articles were selected for this review. After analysis of the selected articles, the reviewed segmentation techniques were categorized according to their theoretical approach. CONCLUSIONS Based on the performed analysis, it was possible to identify segmentation approaches based on distinct image processing classes that can be used to accurately segment the kidney in images of different imaging modalities. Nevertheless, further research on kidney segmentation must be conducted to overcome the current drawbacks of the state-of-the-art methods. Moreover, a standardization of the evaluation database and metrics is needed to allow a direct comparison between methods.

[1]  L. Antiga,et al.  Kidney failure: aims for the next 10 years and barriers to success , 2013, The Lancet.

[2]  Ayman El-Baz,et al.  Segmenting Kidney DCE-MRI Using 1st-Order Shape and 5th-Order Appearance Priors , 2015, MICCAI.

[3]  Raja Muthupillai,et al.  Normal values for renal length and volume as measured by magnetic resonance imaging. , 2006, Clinical journal of the American Society of Nephrology : CJASN.

[4]  Petros Martirosian,et al.  Automated segmentation and volumetric analysis of renal cortex, medulla, and pelvis based on non-contrast-enhanced T1- and T2-weighted MR images , 2014, Magnetic Resonance Materials in Physics, Biology and Medicine.

[5]  Marius George Linguraru,et al.  Renal Segmentation From 3D Ultrasound via Fuzzy Appearance Models and Patient-Specific Alpha Shapes , 2016, IEEE Transactions on Medical Imaging.

[6]  Lin Li,et al.  Ultrasound image segmentation by Bhattacharyya distance with Rayleigh distribution , 2013, 2013 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA).

[7]  Erlend Hodneland,et al.  Segmentation-Driven Image Registration-Application to 4D DCE-MRI Recordings of the Moving Kidneys , 2014, IEEE Transactions on Image Processing.

[8]  Henry Rusinek,et al.  Assessment of renal function with dynamic contrast-enhanced MR imaging. , 2008, Magnetic resonance imaging clinics of North America.

[9]  Xinjian Chen,et al.  Incorporation of physical constraints in optimal surface search for renal cortex segmentation , 2012, Medical Imaging.

[10]  Oudom Somphone,et al.  Real-Time 3D Image Segmentation by User-Constrained Template Deformation , 2012, MICCAI.

[11]  Ayman El-Baz,et al.  A novel framework for automatic segmentation of kidney from DW-MRI , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[12]  Ronald M. Summers,et al.  Automatic segmentation of kidneys from non-contrast CT images using efficient belief propagation , 2013, Medical Imaging.

[13]  Claude Kauffmann,et al.  Kidney segmentation from a single prior shape in MRI , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[14]  Ying Li,et al.  A Watershed Method for MR Renography Segmentation , 2012, 2012 International Conference on Biomedical Engineering and Biotechnology.

[15]  Rémi Cuingnet,et al.  Fast kidney detection and segmentation with learned kernel convolution and model deformation in 3D ultrasound images , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[16]  Baowei Fei,et al.  Automatic 3D segmentation of the kidney in MR images using wavelet feature extraction and probability shape model , 2012, Medical Imaging.

[17]  Huazhong Shu,et al.  Automatic kidney segmentation in CT images based on multi-atlas image registration , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  Gao Yan,et al.  An automatic kidney segmentation from abdominal CT images , 2010, 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[19]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[20]  Ayman El-Baz,et al.  Kidney segmentation using graph cuts and pixel connectivity , 2013, Pattern Recognit. Lett..

[21]  Dwarikanath Mahapatra,et al.  MRF based joint registration and segmentation of dynamic renal MR images , 2010, International Conference on Digital Image Processing.

[22]  Tiexiang Wen,et al.  Multiscale Geometric Active Contour Model and Boundary Extraction in Kidney MR Images , 2014, HIS.

[23]  Xin Yang,et al.  Renal compartment segmentation in DCE-MRI images , 2016, Medical Image Anal..

[24]  Georgy L. Gimel'farb,et al.  A new deformable model-based segmentation approach for accurate extraction of the kidney from abdominal CT images , 2011, 2011 18th IEEE International Conference on Image Processing.

[25]  Ayman El-Baz,et al.  A level set-based framework for 3D kidney segmentation from diffusion MR images , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[26]  Jie Huang,et al.  Ultrasound kidney segmentation with a global prior shape , 2013, J. Vis. Commun. Image Represent..

[27]  Xinjian Chen,et al.  Fast Renal Cortex Localization by Combining Generalized Hough Transform and Active Appearance Models , 2013, Abdominal Imaging.

[28]  Jia Gu,et al.  Segmentation of Kidneys from Computed Tomography Using 3D Fast GrowCut Algorithm , 2013 .

[29]  R. Autorino,et al.  Ureteroscopy-assisted Percutaneous Kidney Access Made Easy: First Clinical Experience with a Novel Navigation System Using Electromagnetic Guidance (IDEAL Stage 1). , 2017, European urology.

[30]  Xinjian Chen,et al.  An automatic method for renal cortex segmentation on CT images: evaluation on kidney donors. , 2012, Academic radiology.

[31]  Arvid Lundervold,et al.  Segmentation of renal compartments in DCE-MRI of human kidney , 2011, 2011 7th International Symposium on Image and Signal Processing and Analysis (ISPA).

[32]  Carlos S. Mendoza,et al.  Automatic Analysis of Pediatric Renal Ultrasound Using Shape, Anatomical and Image Acquisition Priors , 2013, MICCAI.

[33]  Ayman El-Baz,et al.  Kidney segmentation from CT images using a 3D NMF-guided active contour model , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[34]  Evgin Göçeri,et al.  A Neural Network Based Kidney Segmentation from MR Images , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).

[35]  M. Uder,et al.  Semiautomatic segmentation of the kidney in magnetic resonance images using unimodal thresholding , 2016, BMC Research Notes.

[36]  Xinjian Chen,et al.  Renal Cortex Segmentation Using Optimal Surface Search with Novel Graph Construction , 2011, MICCAI.

[37]  Olivier Pietquin,et al.  Functional Segmentation of Renal DCE-MRI Sequences Using Vector Quantization Algorithms , 2011, Neural Processing Letters.

[38]  Yihua Song,et al.  Segmentation of renal parenchymal area from ultrasoundl images using level set evolution , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[39]  Konstantinos N. Plataniotis,et al.  Atlas-based segmentation of abdominal organs in 3D ultrasound, and its application in automated kidney segmentation , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[40]  Klaus D. Tönnies,et al.  Prior Shape Level Set Segmentation on Multistep Generated Probability Maps of MR Datasets for Fully Automatic Kidney Parenchyma Volumetry , 2012, IEEE Transactions on Medical Imaging.

[41]  Fei Yang,et al.  Automatic Renal Cortex Segmentation Using Implicit Shape Registration and Novel Multiple Surfaces Graph Search , 2012, IEEE Transactions on Medical Imaging.

[42]  Yaoqin Xie,et al.  A shape-optimized framework for kidney segmentation in ultrasound images using NLTV denoising and DRLSE , 2012, BioMedical Engineering OnLine.

[43]  Ronald M. Summers,et al.  Automatic 3D kidney segmentation based on shape constrained GC-OAAM , 2011, Medical Imaging.

[44]  L. Padma Suresh,et al.  Segmentation driven image application to 2D-MRI of kidney , 2015, 2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015].

[45]  Pavan Annangi,et al.  Automated kidney morphology measurements from ultrasound images using texture and edge analysis , 2016, SPIE Medical Imaging.

[46]  Arvid Lundervold,et al.  Local/non-local regularized image segmentation using graph-cuts , 2013, International Journal of Computer Assisted Radiology and Surgery.

[47]  Yu-Chi Hu,et al.  Interactive semiautomatic contour delineation using statistical conditional random fields framework. , 2012, Medical physics.

[48]  Irina Voiculescu,et al.  Automated 3D renal segmentation based on image partitioning , 2016, SPIE Medical Imaging.

[49]  Georgios Sakas,et al.  Computer aided segmentation of kidneys using locally shape constrained deformable models on CT images , 2010, Medical Imaging.

[50]  Carlos S. Mendoza,et al.  Kidney segmentation in ultrasound via genetic initialization and Active Shape Models with rotation correction , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[51]  Xuantao Su,et al.  Automatic segmentation method for kidney using dual direction adaptive diffusion flow , 2016 .

[52]  A. Bessaid,et al.  MORPHOLOGICAL SEGMENTATION OF THE KIDNEYS FROM ABDOMINAL CT IMAGES , 2014 .

[53]  L. Cohen,et al.  Kidney Detection and Segmentation in Contrast-Enhanced Ultrasound 3D Images , 2014 .

[54]  Caroline Petitjean,et al.  A review of segmentation methods in short axis cardiac MR images , 2011, Medical Image Anal..

[55]  Marius George Linguraru,et al.  Segmentation of kidney in 3D-ultrasound images using Gabor-based appearance models , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[56]  Wei Kang,et al.  Kidney segmentation in CT sequences using SKFCM and improved GrowCut algorithm , 2015, BMC Systems Biology.

[57]  Arvid Lundervold,et al.  Wavelet-based segmentation of renal compartments in DCE-MRI of human kidney: Initial results in patients and healthy volunteers , 2012, Comput. Medical Imaging Graph..

[58]  Hossein Pourghassem,et al.  Kidney Extraction from Ultrasound Images Based on Multi-Scaling and Multi-directional Filters and Shape Model , 2013 .

[59]  Yanmei Liang,et al.  Kidney segmentation in CT sequences using graph cuts based active contours model and contextual continuity. , 2013, Medical physics.

[60]  Georgy L. Gimel'farb,et al.  Shape-Appearance Guided Level-Set Deformable Model for Image Segmentation , 2010, 2010 20th International Conference on Pattern Recognition.

[61]  Eko Supriyanto,et al.  Boundary detection of kidney ultrasound image based on vector graphic approach , 2015 .

[62]  Leo Joskowicz,et al.  Non-parametric Iterative Model Constraint Graph min-cut for Automatic Kidney Segmentation , 2010, MICCAI.

[63]  MRI of the kidney—state of the art , 2007, European Radiology.

[64]  Kurt P Spindler,et al.  How to Write a Systematic Review , 2007, Clinical orthopaedics and related research.

[65]  Laurent D. Cohen,et al.  Kidney detection and real-time segmentation in 3D contrast-enhanced ultrasound images , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[66]  Fereshteh Aalamifar,et al.  Classification of kidney and liver tissue using ultrasound backscatter data , 2015, Medical Imaging.

[67]  Evgin Goceri,et al.  Automatic Kidney Segmentation Using Gaussian Mixture Model on MRI Sequences , 2011 .

[68]  H. Pourghassem,et al.  Kidney Segmentation in Ultrasound Images Using Curvelet Transform and Shape Prior , 2013, 2013 International Conference on Communication Systems and Network Technologies.

[69]  Ying Li,et al.  A novel active contour model for image segmentation using distance regularization term , 2013, Comput. Math. Appl..

[70]  Henry Rusinek,et al.  Functional renal MR imaging. , 2004, Magnetic resonance imaging clinics of North America.

[71]  Peng Chen,et al.  A Watershed Method for MR Renography Segmentation based on Mathematical Morphology , 2011 .

[72]  Jan D'hooge,et al.  Kidney segmentation in 3D CT images using B-Spline Explicit Active Surfaces , 2016, 2016 IEEE International Conference on Serious Games and Applications for Health (SeGAH).

[73]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[74]  Xin Yang,et al.  Automatic Segmentation of Renal Compartments in DCE-MRI Images , 2015, MICCAI.

[75]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[76]  Estevão Lima,et al.  Collecting system percutaneous access using real-time tracking sensors: first pig model in vivo experience. , 2013, The Journal of urology.

[77]  V. Jha,et al.  Chronic kidney disease: global dimension and perspectives , 2013, The Lancet.

[78]  Laurent D. Cohen,et al.  Joint Co-segmentation and Registration of 3D Ultrasound Images , 2013, IPMI.

[79]  Xinjian Chen,et al.  3D automatic anatomy segmentation based on graph cut-oriented active appearance models , 2010, 2010 IEEE International Conference on Image Processing.

[80]  Victor M Montori,et al.  Conducting systematic reviews of diagnostic studies: didactic guidelines , 2002, BMC medical research methodology.

[81]  Xavier Pennec,et al.  Application of a Probabilistic Statistical Shape Model to Automatic Segmentation , 2009 .

[82]  Jules R. Tapamo,et al.  Fast Chan-Vese without edges and connected component analysis for kidney segmentation in MRI images , 2015, AFRICON 2015.

[83]  Stefan Wesarg,et al.  Automated Kidney Detection and Segmentation in 3D Ultrasound , 2013, CLIP.

[84]  Henry Rusinek,et al.  A semi-automated “blanket” method for renal segmentation from non-contrast T1-weighted MR images , 2016, Magnetic Resonance Materials in Physics, Biology and Medicine.

[85]  Yihua Song,et al.  An Improved Level Set Method for Segmentation of Renal Parenchymal Area from Ultrasound Images , 2015 .

[86]  Estevão Lima,et al.  Kidney targeting and puncturing during percutaneous nephrolithotomy: recent advances and future perspectives. , 2013, Journal of endourology.

[87]  Yanmei Liang,et al.  Contextual information-aided kidney segmentation in CT sequences , 2013 .

[88]  Eko Supriyanto,et al.  Automatic ultrasound kidney's centroid detection system , 2011 .

[89]  Christopher Joseph Pal,et al.  Convolutional networks for kidney segmentation in contrast-enhanced CT scans , 2018, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[90]  Xinjian Chen,et al.  3D Fast Automatic Segmentation of Kidney Based on Modified AAM and Random Forest , 2016, IEEE Transactions on Medical Imaging.

[91]  Xinjian Chen,et al.  A Fully Automated Framework for Renal Cortex Segmentation , 2012, Abdominal Imaging.

[92]  Marius George Linguraru,et al.  Positive Delta Detection for Alpha Shape Segmentation of 3D Ultrasound Images of Pathologic Kidneys , 2015, MICCAI.

[93]  Klaus D. Tönnies,et al.  Fully automatized renal parenchyma volumetry using a support vector machine based recognition system for subject-specific probability map generation in native MR volume data , 2015, Physics in medicine and biology.

[94]  Konstantinos N. Plataniotis,et al.  Shape-based kidney detection and segmentation in three-dimensional abdominal ultrasound images , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[95]  Stefan Wesarg,et al.  Automated kidney detection for 3D ultrasound using scan line searching , 2016, SPIE Medical Imaging.

[96]  Xinjian Chen,et al.  Renal cortex localization by combining 3D Generalized Hough Transform and 3D Active Appearance Models , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[97]  Ayman El-Baz,et al.  A random forest-based framework for 3D kidney segmentation from dynamic contrast-enhanced CT images , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[98]  Ayman El-Baz,et al.  3D Kidney Segmentation from CT Images Using a Level Set Approach Guided by a Novel Stochastic Speed Function , 2011, MICCAI.

[99]  Marius George Linguraru,et al.  Quantification of kidneys from 3D ultrasound in pediatric hydronephrosis , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[100]  Mohammad Shorif Uddin,et al.  Speckle noise reduction and segmentation of kidney regions from ultrasound image , 2013, 2013 International Conference on Informatics, Electronics and Vision (ICIEV).

[101]  Klaus D. Tönnies,et al.  Fully Automatic Renal Parenchyma Volumetry in LDA-based Probability Maps Using Variational Outer Cortex Edge Alignment Forces , 2014, IWBBIO.

[102]  Laurent D. Cohen,et al.  Automatic Detection and Segmentation of Kidneys in 3D CT Images Using Random Forests , 2012, MICCAI.

[103]  Konstantinos N. Plataniotis,et al.  An Automated Approach for Kidney Segmentation in Three-Dimensional Ultrasound Images , 2017, IEEE Journal of Biomedical and Health Informatics.

[104]  Hong Li,et al.  A renal vascular compartment segmentation method based on dynamic contrast-enhanced images. , 2016, Technology and health care : official journal of the European Society for Engineering and Medicine.

[105]  Yan Kang,et al.  Semi-automatic segmentation of renal cortex and medulla based on dynamic magnetic resonance images , 2010, 2010 3rd International Conference on Biomedical Engineering and Informatics.

[106]  Xavier Pennec,et al.  Coupled level set segmentation using a point-based statistical shape model relying on correspondence probabilities , 2010, Medical Imaging.