Kidney Detection and Segmentation in Contrast-Enhanced Ultrasound 3D Images



Contrast-enhanced ultrasound (CEUS) imaging has lately benefited of an increasing interest for diagnosis and intervention planning, as it allows to visualize blood flow in real-time harmlessly for the patient. It complements thus the anatomical information provided by conventional ultrasound (US). This chapter is dedicated to kidney segmentation methods in 3D CEUS images. First we present a generic and fast two-step approach to locate (via a robust ellipsoid estimation algorithm) and segment (using a template deformation framework) the kidney automatically. Then we show how user interactions can be integrated within the algorithm to guide or correct the segmentation in real-time. Finally, we develop a co-segmentation framework that generalizes the aforementioned method and allows the simultaneous use of multiple images (here the CEUS and the US images) to improve the segmentation result. The different approaches are evaluated on a clinical database of 64 volumes.


Transformation Model User Interaction Dice Coefficient Kidney Volume Decision Forest 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Philips Research MedisysSuresnesFrance
  2. 2.Adult Radiology DepartmentNecker HospitalParisFrance
  3. 3.Paris Dauphine UniversityParisFrance

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