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The proposed method segments the kidney into four components: renal cortex, renal column, renal medulla, and renal pelvis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p>In this article, we have proposed a highly efficient approach which strategically combines random forests and random ferns methods to segment the kidney into four components: renal cortex, renal column, renal medulla, and renal pelvis. The proposed method is designed following a coarse\u2010to\u2010fine strategy. The initial segmentation applies random forests and random ferns with a variety of features, and combines their results to obtain a coarse renal cortex region. Then the fine segmentation of four kidney components is achieved using the weighted forests\u2010ferns approach with the well\u2010designed potential energy features which are calculated based on the initial segmentation result. The proposed method was validated on a dataset with 37 contrast\u2010enhanced <jats:styled-content style=\"fixed-case\">CT<\/jats:styled-content> images. Evaluation indices including Dice similarity coefficient (<jats:styled-content style=\"fixed-case\">DSC<\/jats:styled-content>), true positive volume fraction (<jats:styled-content style=\"fixed-case\">TPVF<\/jats:styled-content>), and false positive volume fraction (<jats:styled-content style=\"fixed-case\">FPVF<\/jats:styled-content>) are used to assess the segmentation accuracy. The proposed method was implemented and tested on a 64\u2010bit system computer (Intel Core i7\u20103770 <jats:styled-content style=\"fixed-case\">CPU<\/jats:styled-content>, 3.4 GHz and 8 GB <jats:styled-content style=\"fixed-case\">RAM<\/jats:styled-content>).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The experimental results demonstrated the high accuracy and efficiency for segmenting the kidney components: the mean Dice similarity coefficients were 89.85%, 80.60%, 86.63%, and 77.75% for renal cortex, column, medulla, and pelvis, respectively, for right and left kidneys. The computational time of segmenting the whole kidney into four components was about 3 s.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The experimental results showed the feasibility and efficacy of the proposed automatic kidney component segmentation method. The proposed method applied an efficient weighted strategy to combine random forests and ferns, making full use of the advantages of both methods. The novel potential energy features help random forests effectively segment the kidney components and the background. The high accuracy and efficiency of our method make it practicable in clinical applications.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.12594","type":"journal-article","created":{"date-parts":[[2017,9,22]],"date-time":"2017-09-22T10:48:16Z","timestamp":1506077296000},"page":"6353-6363","source":"Crossref","is-referenced-by-count":10,"title":["Fast segmentation of kidney components using random forests and ferns"],"prefix":"10.1002","volume":"44","author":[{"given":"Chao","family":"Jin","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering Soochow University Suzhou 215000 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering Soochow University Suzhou 215000 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dehui","family":"Xiang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering Soochow University Suzhou 215000 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lichun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering Soochow University Suzhou 215000 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinjian","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering Soochow University Suzhou 215000 China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2017,11,7]]},"reference":[{"key":"e_1_2_6_2_1","first-page":"1","volume-title":"Silva's Diagnostic Renal Pathology","author":"Clapp WL","year":"2009"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2007.04.027"},{"key":"e_1_2_6_4_1","first-page":"308","article-title":"Study on differential diagnosis of renal column hypertrophy and renal tumors by pulsed subtraction contrast\u2010enhanced ultrasonography, Chinese","volume":"22","author":"Jun L","year":"2006","journal-title":"J Ultras Med"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1136\/jmg.39.12.882"},{"key":"e_1_2_6_6_1","volume-title":"Tumors of the Kidney, Renal Pelvis, and Ureter","author":"Bennington J","year":"1975"},{"key":"e_1_2_6_7_1","doi-asserted-by":"crossref","unstructured":"FreimanM KronmanA EssesSJ JoskowiczL SosnaJ.Non\u2010parametric iterative model constraint graph min\u2010cut for automatic kidney segmentation. 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