Laurent Peyrodie

Expanded Disability Status Scale (EDSS) estimation in multiple sclerosis from posturographic data

Expanded Disability Status Scale (EDSS) is the most widely used clinical scale to evaluate levels of multiple sclerosis (MS). As MS can lead to disruptions in the regulation of balance and the disability can be evaluated by force platform posturography, we have developed in this study a new strategy to estimate EDSS from posturographic data. 118 volunteers with EDSS ranging from 0 to 4.5 participated in this study, with eyes closed. By using second-order polynomial regression models, EDSS was estimated from two postural sway parameters, respectively, the length and the surface and four recurrence quantification analysis (RQA) parameters: percentage of recurrence (%Rec), Shannon entropy (Ent), mean diagonal line length (LL) and trapping time (TT). In addition, all four RQA parameters were calculated for position, instantaneous velocity and acceleration of the center of pressure. In order to select the most accurate method for estimating EDSS, four statistical indices (percentage of agreement, underestimation and overestimation, as well as Mean error) were calculated comparing clinical and estimated EDSS scores. The results demonstrate that estimations of EDSS from surface, %Rec and LL of position, best agreed with clinical scores. This study emphasizes the possibility of distinguishing EDSS scores using postural sway and RQA parameters.

Improvements of Adaptive Filtering by Optimal Projection to filter different artifact types on long duration EEG recordings

Adaptive Filtering by Optimal Projection (AFOP) is an automatic method for reducing ocular and muscular artifacts on electro-encephalographic (EEG) recordings. This paper presents two additions to this method: an improvement of the stability of ocular artifact filtering and an adaptation of the method for filtering electrode artifacts. With these improvements, it is possible to reduce almost all the current types of artifacts, while preserving brain signals, particularly those characterising epilepsy. This generalised method consists of dividing the signal into several time-frequency windows, and in applying different spatial filters to each. Two steps are required to define one of these spatial filters: the first step consists of defining artifact spatial projection using the Common Spatial Pattern (CSP) method and the second consists of defining EEG spatial projection via regression. For this second step, a progressive orthogonalisation process is proposed to improve stability. This method has been tested on long-duration EEG recordings of epileptic patients. A neurologist quantified the ratio of removed artifacts and the ratio of preserved EEG. Among the 330 artifacted pages used for evaluation, readability was judged better for 78% of pages, equal for 20% of pages, and worse for 2%. Artifact amplitudes were reduced by 80% on average. At the same time, brain sources were preserved in amplitude from 70% to 95% depending on the type of waves (alpha, theta, delta, spikes, etc.). A blind comparison with manual Independent Component Analysis (ICA) was also realised. The results show that this method is competitive and useful for routine clinical practice.

Three-dimensional skeletonization and symbolic description in vascular imaging: preliminary results

ObjectiveA general method was developed to analyze and describe tree-like structures needed for evaluation of complex morphology, such as the cerebral vascular tree. Clinical application of the method in neurosurgery includes planning of the surgeon’s intraoperative gestures.MethodWe have developed a 3D skeletonization method adapted to tubular forms with symbolic description. This approach implements an iterative Dijkstra minimum cost spanning tree, allowing a branch-by-branch skeleton extraction. The proposed method was implemented using the laboratory software platform (ArtiMed). The 3D skeleton approach was tested on simulated data and preliminary trials on clinical datasets mainly based on magnetic resonance image acquisitions.ResultsA specific experimental evaluation plan was designed to test the skeletonization and symbolic description methods. Accuracy was tested by calculating the positioning error, and robustness was verified by comparing the results on a series of 18 rotations of the initial volume. Accuracy evaluation showed a Haussdorff’s distance always smaller than 17 voxels and Dice’s similarity coefficient greater than 70xa0%.ConclusionOur method of symbolic description enables the analysis and interpretation of a vascular network obtained from angiographic images. The method provides a simplified representation of the network in the form of a skeleton, as well as a description of the corresponding information in a tree-like view.

Cervicocephalic relocation test to evaluate cervical proprioception in adolescent idiopathic scoliosis.

PurposeAdolescent idiopathic scoliosis (AIS) is a three-dimensional deformity of the spine associated with disturbed postural control. Cervical proprioception participates in controlling orthostatic posture via its influence on head stabilization. We hypothesized that patients with AIS exhibit altered cervical proprioception.MethodsWe conducted a case–control study to evaluate cervical proprioception using the cervicocephalic relocation test (CRT) in 30 adolescents with AIS (15.5xa0±xa01.5xa0years; Cobb 24.8°xa0±xa09.5°) versus 14 non-scoliotic controls (14.6xa0±xa02.0xa0years). CRT evaluates cervical proprioception by measuring the capacity to relocate the head on the trunk after active rotation of the head in the transversal plane without visual control. Each subject performed ten right and then ten left head rotations.ResultsThe CRT results were pathological in 12 AIS patients (40xa0%). The CRT mean was significantly different between AIS patients with a pathological CRT (5°xa0±xa01.4° for right rotation; 4.2°xa0±xa00.9° for left rotation) compared with AIS patients with a normal CRT (2.7°xa0±xa00.6° for right rotation; 2.9°xa0±xa00.8° for left rotation) or with the control group (3.5°xa0±xa02.1° for right rotation; 3.1°xa0±xa01.2° for left rotation).ConclusionCervical proprioception is impaired in certain AIS patients. This anomaly may worsen the prognosis of AIS (headache; balance disorders; worsened spinal deformity; complication after spinal fusion). We recommend systematic screening for altered cervical proprioception in AIS patients.

Texture segmentation using globally active contours model and Cauchy-Schwarz distance

We present a new unsupervised segmentation based active contours model and local region texture descriptor. The proposed local region texture descriptor intrinsically describes the geometry of textural regions using the shape operator defined in Beltrami framework. The local texture descriptor is incorporated in the active contours using the Cauchy-Schwarz distance. The texture is discriminated by maximizing distance between the probability density functions which leads to distinguish textural objects of interest and background. We propose a fast Bregman split implementation of our segmentation algorithm based on the dual formulation of the Total Variation norm. Finally, we show results on some challenging images to illustrate segmentations that are possible.

Fast Unsupervised Texture Segmentation Using Active Contours Model Driven by Bhattacharyya Gradient Flow

We present a new unsupervised segmentation based active contours model and texture descriptor. The proposed texture descriptor intrinsically describes the geometry of textural regions using the shape operator defined in Beltrami framework. We use Bhattacharyya distance to discriminate textures by maximizing distance between the probability density functions which leads to distinguish textural objects of interest and background. We propose a fast Bregman split implementation of our segmentation algorithm based on the dual formulation of the Total Variation norm. Finally, we show results on some challenging images to illustrate segmentations that are possible.

Joint variational segmentation of CT/PET data using non-local active contours and belief functions

In this paper, we have proposed a new framework to use both PET and CT images simultaneously for tumor segmentation. Our method combines the strength of each imaging modality: the superior contrast of PET and the superior spatial resolution of CT. We formulate this problem as a Non-Local Active Contours (NL-AC) based-variational segmentation framework incorporating Belief Functions (BFs). The proposed method used all features issued from both modalities (CT and PET) as a descriptor to drive the NL-AC curve evolution. The new segmentation framework allows us to incorporate in the same framework heterogeneous knowledge in order to reduce the imprecision due to noise poor contrast, weak or missing boundaries of objects, inhomogeneities, etc. The proposed method was evaluated on relevant tumor segmentation problems. The results showed that our method can effectively make use of both PET and CT image information, yielding segmentation accuracy of 81.52% in Dice Similarity Coefficient (DSC) and the Average Symmetric Surface Distance (ASSD) of 1.2 ± 0.8 mm, which is 10% (resp., 16%) improvement compared to two state of art segmentation methods using the PET (resp., CT) images.

Prostate contours delineation using interactive directional active contours model and parametric shape prior model

Prostate contours delineation on Magnetic Resonance (MR) images is a challenging and important task in medical imaging with applications of guiding biopsy, surgery and therapy. While a fully automated method is highly desired for this application, it can be a very difficult task due to the structure and surrounding tissues of the prostate gland. Traditional active contours-based delineation algorithms are typically quite successful for piecewise constant images. Nevertheless, when MR images have diffuse edges or multiple similar objects (e.g. bladder close to prostate) within close proximity, such approaches have proven to be unsuccessful. In order to mitigate these problems, we proposed a new framework for bi-stage contours delineation algorithm based on directional active contours (DAC) incorporating prior knowledge of the prostate shape. We first explicitly addressed the prostate contour delineation problem based on fast globally DAC that incorporates both statistical and parametric shape prior model. In doing so, we were able to exploit the global aspects of contour delineation problem by incorporating a user feedback in contours delineation process where it is shown that only a small amount of user input can sometimes resolve ambiguous scenarios raised by DAC. In addition, once the prostate contours have been delineated, a cost functional is designed to incorporate both user feedback interaction and the parametric shape prior model. Using data from publicly available prostate MR datasets, which includes several challenging clinical datasets, we highlighted the effectiveness and the capability of the proposed algorithm. Besides, the algorithm has been compared with several state-of-the-art methods.

Fast globally supervised segmentation by active contours with shape and texture descriptors

We present a new globally supervised segmentation method in the characteristic function framework based on an active contours (AC) model incorporating both shape prior and texture descriptors. The shape prior descriptor is formulated as the traditional Legendre moment and the texture descriptor as a linear combination of local inside/outside texture descriptor. Using these two descriptors, the AC energy incorporates both learned textures and training shapes. This formulation has two main advantages: 1) by discriminating independently the foreground/background textures. 2) by incorporating both the learned inside/outside texture and the training shape. The trade-off between inside and outside texture descriptor is ensured by balancing descriptor. We illustrate the performance of our segmentation algorithm using some challenging textured images.

Fast Unsupervised Segmentation Using Active Contours and Belief Functions

In this paper, we study Active Contours AC based globally segmentation for vector valued images using evidential Kullback-Leibler KL distance. We investigate the evidential framework to fuse multiple features issued from vector-valued images. This formulation has two main advantages: 1 by the combination of foreground/background issued from the multiple channels in the same framework. 2 the incorporation of the heterogeneous knowledge and the reduction of the imprecision due to the noise. The statistical relation between the image channels is ensured by the Dempster-Shafer rule. We illustrate the performance of our segmentation algorithm using some challenging color and textured images.