Automatic analysis of pediatric renal ultrasound using shape, anatomical and image acquisition priors.

Xin Kang, Nabile Safdar, Emmarie Myers, Aaron D. Martin, Enrico Grisan, Craig A Peters, Marius George Linguraru

Research output: Contribution to journalArticlepeer-review


In this paper we present a segmentation method for ultrasound (US) images of the pediatric kidney, a difficult and barely studied problem. Our method segments the kidney on 2D sagittal US images and relies on minimal user intervention and a combination of improvements made to the Active Shape Model (ASM) framework. Our contributions include particle swarm initialization and profile training with rotation correction. We also introduce our methodology for segmentation of the kidney's collecting system (CS), based on graph-cuts (GC) with intensity and positional priors. Our intensity model corrects for intensity bias by comparison with other biased versions of the most similar kidneys in the training set. We prove significant improvements (p < 0.001) with respect to classic ASM and GC for kidney and CS segmentation, respectively. We use our semi-automatic method to compute the hydronephrosis index (HI) with an average error of 2.67 +/- 5.22 percentage points similar to the error of manual HI between different operators of 2.31 +/- 4.54 percentage points.

Original languageEnglish (US)
Pages (from-to)259-266
Number of pages8
JournalMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Issue numberPt 3
StatePublished - Dec 1 2013

ASJC Scopus subject areas

  • Medicine(all)


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