Kidney detection and real-time segmentation in 3D contrast-enhanced ultrasound images

Raphaël Prevost (1, 2) , Benoît Mory (2) , Jean-Michel Corréas (3) , Laurent D. Cohen (1) , Roberto Ardon (2)

1 CEREMADE - CEntre de REcherches en MAthématiques de la DEcision
2 Medisys - MedisysResearch Lab
3 Department of Adult Radiology

In this paper, we present an automatic method to segment the kidney in 3D contrast-enhanced ultrasound (CEUS) images. This modality 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. Our method is composed of two steps: first, the kidney is automatically localized by a novel robust ellipsoid detector; then, segmentation is obtained through the deformation of this ellipsoid with a model-based approach. To cope with low image quality and strong organ variability induced by pathologies, the algorithm allows the user to refine the result by real-time interactions. Our method has been validated on a representative clinical database. (c) 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

Mots clés: Kidney; Detection; Segmentation; 3D Ultrasound; Contrast; CEUS

Domaines: Imagerie médicale; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Raphaël Prevost, Benoît Mory, Jean-Michel Corréas, Laurent D. Cohen, Roberto Ardon. Kidney detection and real-time segmentation in 3D contrast-enhanced ultrasound images. Biomedical Imaging: Nano to Macro, 2012. 9th IEEE International Symposium on (ISBI 2012), May 2012, Barcelone, Spain. pp.1559-1562. ⟨hal-00703134⟩