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

2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)

Bacchuwar

et al. 2015

Self Cite

We present a method to segment kidneys in 3D ultrasound images. The main challenges are the high variability in kidney appearance, the frequent presence of artifacts (shadows, speckle noise, etc.) and a strong constraint on computation time for clinical acceptance (less than 10 seconds). Our algorithm leverages a database of 480 3D images through a support vector machine(SVM)-based detection algorithm followed by a model-based deformation technique. Since severe pathologies induce strong deformations of kidneys, the proposed method encompasses intuitive interaction functions allowing the user to refine the result with a few clicks. Validation has been performed by learning on 120 cases and testing on 360; a perfect segmentation was reached automatically in 50% of the cases, and in 90% of the cases in less than 3 clicks.

Section: Kidney Segmentationmentioning

confidence: 99%

“…The first term of Eq. (4) stands for the data attachment and aims to maximize the image gradient flow (see [6]). As for the second term, it limits the local deformations of the model to stay close to a kidney shape.…”

Section: Kidney Segmentationmentioning

confidence: 99%

Fast kidney detection and segmentation with learned kernel convolution and model deformation in 3D ultrasound images

Ardon

2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)

Bacchuwar

et al. 2015

Self Cite

“…In [13], we proposed a method to detect and segment kidneys in 3D CEUS images. While we provided an automated pipeline, failures were reported in several cases and user interactions were needed to obtain a satisfying result.…”

Section: Clinical Settingmentioning

confidence: 99%

Joint Co-segmentation and Registration of 3D Ultrasound Images

Prevost

Lecture Notes in Computer Science

Mory

et al. 2013

Self Cite

Contrast-enhanced ultrasound (CEUS) allows a visualization of the vascularization and complements the anatomical information provided by conventional ultrasound (US). However, these images are inherently subject to noise and shadows, which hinders standard segmentation algorithms. In this paper, we propose to use simultaneously the different information coming from 3D US and CEUS images to address the problem of kidney segmentation. To that end, we introduce a generic framework for joint co-segmentation and registration that seeks objects having the same shape in several images. From this framework, we derive both an ellipsoid co-detection and a model-based co-segmentation algorithm. These methods rely on voxel-classification maps that we estimate using random forests in a structured way. This yields a fast and fully automated pipeline, in which an ellipsoid is first estimated to locate the kidney in both US and CEUS volumes and then deformed to segment it accurately. The proposed method outperforms state-of-the-art results (by dividing the kidney volume error by two) on a clinically representative database of 64 images.

“…However, in previous works the initial template was either set as a synthetic model (e.g. ellipsoid for a kidney [2][3][4]) or as a segmented organ from a single arbitrary image [1]. Despite the consensus that learning shape priors is a powerful approach to improve robustness [5,6], this has never been proposed in the context of segmentation by implicit template deformation.…”

Section: Introductionmentioning

confidence: 99%

Incorporating Shape Variability in Image Segmentation via Implicit Template Deformation

Prevost

Advanced Information Systems Engineering

Mory

et al. 2013

Self Cite

Abstract. Implicit template deformation is a model-based segmentation framework that was successfully applied in several medical applications. In this paper, we propose a method to learn and use prior knowledge on shape variability in such framework. This shape prior is learnt via an original and dedicated process in which both an optimal template and principal modes of variations are estimated from a collection of shapes. This learning strategy requires neither a pre-alignment of the training shapes nor one-to-one correspondences between shape sample points. We then generalize the implicit template deformation formulation to automatically select the most plausible deformation as a shape prior. This novel framework maintains the two main properties of implicit template deformation: topology preservation and computational efficiency. Our approach can be applied to any organ with a possibly complex shape but fixed topology. We validate our method on myocardium segmentation from cardiac magnetic resonance short-axis images and demonstrate segmentation improvement over standard template deformation.