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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

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)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
Pages259-266
Number of pages8
EditionPART 3
DOIs
StatePublished - Oct 24 2013
Event16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: Sep 22 2013Sep 26 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8151 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
CountryJapan
CityNagoya
Period9/22/139/26/13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Mendoza, C. S., Kang, X., Safdar, N., Myers, E., Martin, A. D., Grisan, E., Peters, C. A., & Linguraru, M. G. (2013). Automatic analysis of pediatric renal ultrasound using shape, anatomical and image acquisition priors. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings (PART 3 ed., pp. 259-266). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8151 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-40760-4_33