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Automated kidney detection and segmentation in 3D ultrasound

: Noll, Matthias; Li, Xin; Wesarg, Stefan


Erdt, Marius (Ed.); Linguraru, Marius George (Ed.); Oyarzun Laura, Cristina (Ed.); Shekhar, Raj (Ed.); Wesarg, Stefan (Ed.); González Ballester, Miguel Angel (Ed.); Drechsler, Klaus (Ed.):
Clinical image-based procedures : Translational research in medical imaging. Revised selected papers. Second international workshop, CLIP 2013, held in conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013
Cham: Springer International Publishing, 2014 (Lecture Notes in Computer Science (LNCS) 8361)
DOI: 10.1007/978-3-319-05666-1
ISBN: 978-3-319-05665-4
ISSN: 0302-9743
International Workshop on Clinical Image-Based Procedures (CLIP) <2, 2013, Nagoya>
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) <16, 2013, Nagoya>
Fraunhofer IGD ()
ultrasound; image analysis; shape priors; detection; segmentation

Ultrasound provides the physical capabilities for a fast and save disease diagnosis in various medical scenarios including renal exams and patient trauma assessment. However, the experience of the ultrasound operator is the key element in performing ultrasound diagnosis. Thus, we like to introduce our automatic kidney detection and segmentation algorithm for 3D ultrasound. The approach utilizes basic kidney shape information to detect the kidney position. Following, the Level Set algorithm is applied to segment the detection result. In combination this method may help physicians and inexperienced trainees to achieve kidney detection and segmentation for diagnostic purposes.