Machine Learning
MAP MRF Joint Segmentation and Registration
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cosegmentation revisited: models and optimization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Entangled decision forests and their application for semantic segmentation of CT images
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Globally optimal tumor segmentation in PET-CT images: a graph-based co-segmentation method
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Simultaneous registration and segmentation of anatomical structures from brain MRI
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
IEEE Transactions on Image Processing
Real-Time 3d image segmentation by user-constrained template deformation
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Joint classification-regression forests for spatially structured multi-object segmentation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
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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.