An Efficient Segmentation Method for Ultrasound Images based on a Semi-supervised Approach and Patch-based Features

Segmenting ultrasound images is a challenging problem where standard unsupervised segmentation methods such as the well-known Chan-Vese method fail. We propose in this paper an efficient segmentation method for this class of images. Our proposed algorithm is based on a semi-supervised approach (user labels) and the use of image patches as data features. We also consider the Pearson distance between patches, which has been shown to be robust w.r.t speckle noise present in ultrasound images. Our results on phantom and clinical data show a very high similarity agreement with the ground truth provided by a medical expert.

Semi-Supervised Segmentation of Ultrasound Images Based on Patch Representation and Continuous Min Cut

Meritxell Bach Cuadra, Xavier Bresson, Anca-Ramona Ciurte, Olivier Cuisenaire, Nawal Houhou, Jean-Philippe Thiran

Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature.

Public Library Science2014

A Cross Validation Study of Deep Brain Stimulation Targeting: From Experts to Atlas-Based, Segmentation-Based and Automatic Registration Algorithms

Meritxell Bach Cuadra, Olivier Cuisenaire, Reto Meuli, Francisco Javier Sanchez Castro, Jean-Philippe Thiran

Validation of image registration algorithms is a difficult task and open-ended problem, usually application-dependent. In this paper we focus on deep brain stimulation (DBS) targeting for the treatment of movement disorders like Parkinson's disease and essential tremor. DBS involves implantation of an electrode deep inside the brain to electrically stimulate specific areas shutting down the disease's symptoms. The subthalamic nucleus (STN) has turned out to be the optimal target for this kind of surgery. Unfortunately, the STN is in general not clearly distinguishable in common medical imaging modalities. Usual techniques to infer its location are the use of anatomical atlases and visible surrounding landmarks. Surgeons have to adjust the electrode intra-operatively using electrophysiological recordings and macrostimulation tests. We constructed a ground truth derived from specific patients whose STNs are clearly visible on MR T2-weighted images. A patient is chosen as atlas both for the right and left sides. Then, by registering each patient with the atlas using different methods, several estimations of the STN location are obtained. Two studies are driven using our proposed validation scheme. First, a comparison between different atlas-based and non-rigid registration algorithms with a evaluation of their performance and usability to locate the STN automatically. Second, a study of which visible surrounding structures influence the STN location. The two studies are cross-validated between them and against expert's variability. Using this scheme we evaluated the expert's ability against the estimation error provided by the tested algorithms and we demonstrated that automatic STN targeting is possible and as accurate as the expert-driven techniques currently used. We also show which structures have to be taken into account to accurately estimate the STN location.

2006

Multidimensional, Multistage Wavelet Footprints: A New Tool for Image Segmentation and Feature Extraction in Medical Ultrasound

Muthuvel Arigovindan, Michael Sühling, Michaël Unser

We present a new wavelet-based strategy for autonomous feature extraction and segmentation of cardiac structures in dynamic ultrasound images. Image sequences subjected to a multidimensional (2D plus time) wavelet transform yield a large number of individual subbands, each coding for partial structural and motion information of the ultrasound sequence. We exploited this fact to create an analysis strategy for autonomous analysis of cardiac ultrasound that builds on shape- and motion specific wavelet subband flters. Subband selection was in an automatic manner based on subband statistics. Such a collection of predefined subbands corresponds to the so-called footprint of the target structure and can be used as a multidimensional multiscale filter to detect and localize the target structure in the original ultrasound sequence. Autonomous, unequivocal localization by the autonomous algorithm is then done using a peak finding algorithm, allowing to compare the findings with a reference standard. Image segmentation is then possible using standard region growing operations. To test the feasibility of this multiscale footprint algorithm, we tried to localize, enhance and segment the mitral valve autonomously in 182 non-selected clinical cardiac ultrasound sequences. Correct autonomous localization by the algorithm was feasible in 165 of 182 reconstructed ultrasound sequences, using the experienced echocardiographer as reference. This corresponds to a 91% accuracy of the proposed method in unselected clinical data. Thus, multidimensional multiscale wavelet footprints allow successful autonomous detection and segmentation of the mitral valve with good accuracy in dynamic cardiac ultrasound sequences which are otherwise difficult to analyse due to their high noise level.

SPIE2003

A Cross Validation Study of Deep Brain Stimulation Targeting: From Experts to Atlas-Based, Segmentation-Based and Automatic Registration Algorithms

Meritxell Bach Cuadra, Olivier Cuisenaire, Reto Meuli, Francisco Javier Sanchez Castro, Jean-Philippe Thiran

2006