Pierrick Coupé

Segmentation of MR images via discriminative dictionary learning and sparse coding: Application to hippocampus labeling

We propose a novel method for the automatic segmentation of brain MRI images by using discriminative dictionary learning and sparse coding techniques. In the proposed method, dictionaries and classifiers are learned simultaneously from a set of brain atlases, which can then be used for the reconstruction and segmentation of an unseen target image. The proposed segmentation strategy is based on image reconstruction, which is in contrast to most existing atlas-based labeling approaches that rely on comparing image similarities between atlases and target images. In addition, we propose a Fixed Discriminative Dictionary Learning for Segmentation (F-DDLS) strategy, which can learn dictionaries offline and perform segmentations online, enabling a significant speed-up in the segmentation stage. The proposed method has been evaluated for the hippocampus segmentation of 80 healthy ICBM subjects and 202 ADNI images. The robustness of the proposed method, especially of our F-DDLS strategy, was validated by training and testing on different subject groups in the ADNI database. The influence of different parameters was studied and the performance of the proposed method was also compared with that of the nonlocal patch-based approach. The proposed method achieved a median Dice coefficient of 0.879 on 202 ADNI images and 0.890 on 80 ICBM subjects, which is competitive compared with state-of-the-art methods.

Volumetric analysis of medial temporal lobe structures in brain development from childhood to adolescence.

Puberty is an important stage of development as a childs sexual and physical characteristics mature because of hormonal changes. To better understand puberty-related effects on brain development, we investigated the magnetic resonance imaging (MRI) data of 306 subjects from 4 to 18 years of age. Subjects were grouped into before and during puberty groups according to their sexual maturity levels measured by the puberty scores. An appearance model-based automatic segmentation method with patch-based local refinement was employed to segment the MRI data and extract the volumes of medial temporal lobe (MTL) structures including the amygdala (AG), the hippocampus (HC), the entorhinal/perirhinal cortex (EPC), and the parahippocampal cortex (PHC). Our analysis showed age-related volumetric changes for the AG, HC, right EPC, and left PHC but only before puberty. After onset of puberty, these volumetric changes then correlate more with sexual maturity level, as measured by the puberty score. When normalized for brain volume, the volumes of the right HC decrease for boys; the volumes of the left HC increase for girls; and the volumes of the left and right PHC decrease for boys. These findings suggest that the rising levels of testosterone in boys and estrogen in girls might have opposite effects, especially for the HC and the PHC. Our findings on sex-specific and sexual maturity-related volumes may be useful in better understanding the MTL developmental differences and related learning, memory, and emotion differences between boys and girls during puberty.

An Optimized PatchMatch for multi-scale and multi-feature label fusion

Automatic segmentation methods are important tools for quantitative analysis of Magnetic Resonance Images (MRI). Recently, patch-based label fusion approaches have demonstrated state-of-the-art segmentation accuracy. In this paper, we introduce a new patch-based label fusion framework to perform segmentation of anatomical structures. The proposed approach uses an Optimized PAtchMatch Label fusion (OPAL) strategy that drastically reduces the computation time required for the search of similar patches. The reduced computation time of OPAL opens the way for new strategies and facilitates processing on large databases. In this paper, we investigate new perspectives offered by OPAL, by introducing a new multi-scale and multi-feature framework. During our validation on hippocampus segmentation we use two datasets: young adults in the ICBM cohort and elderly adults in the EADC-ADNI dataset. For both, OPAL is compared to state-of-the-art methods. Results show that OPAL obtained the highest median Dice coefficient (89.9% for ICBM and 90.1% for EADC-ADNI). Moreover, in both cases, OPAL produced a segmentation accuracy similar to inter-expert variability. On the EADC-ADNI dataset, we compare the hippocampal volumes obtained by manual and automatic segmentation. The volumes appear to be highly correlated that enables to perform more accurate separation of pathological populations.

Optimized PatchMatch for Near Real Time and Accurate Label Fusion

Automatic segmentation methods are important tools for quantitative analysis of magnetic resonance images. Recently, patch-based label fusion approaches demonstrated state-of-the-art segmentation accuracy. In this paper, we introduce a new patch-based method using the PatchMatch algorithm to perform segmentation of anatomical structures. Based on an Optimized PAtchMatch Label fusion (OPAL) strategy, the proposed method provides competitive segmentation accuracy in near real time. During our validation on hippocampus segmentation of 80 healthy subjects, OPAL was compared to several state-of-the-art methods. Results show that OPAL obtained the highest median Dice coefficient (89.3%) in less than 1 sec per subject. These results highlight the excellent performance of OPAL in terms of computation time and segmentation accuracy compared to recently published methods.

Early Fiber Number Ratio Is a Surrogate of Corticospinal Tract Integrity and Predicts Motor Recovery After Stroke

Background and Purpose— The contribution of imaging metrics to predict poststroke motor recovery needs to be clarified. We tested the added value of early diffusion tensor imaging (DTI) of the corticospinal tract toward predicting long-term motor recovery. Methods— One hundred seventeen patients were prospectively assessed at 24 to 72 hours and 1 year after ischemic stroke with diffusion tensor imaging and motor scores (Fugl-Meyer). The initial fiber number ratio (iFNr) and final fiber number ratio were computed as the number of streamlines along the affected corticospinal tract normalized to the unaffected side and were compared with each other. The prediction of motor recovery (&Dgr;Fugl-Meyer) was first modeled using initial Fugl-Meyer and iFNr. Multivariate ordinal logistic regression models were also used to study the association of iFNr, initial Fugl-Meyer, age, and stroke volume with Fugl-Meyer at 1 year. Results— The iFNr correlated with the final fiber number ratio at 1 year (r=0.70; P<0.0001). The initial Fugl-Meyer strongly predicted motor recovery (≈73% of initial impairment) for all patients except those with initial severe stroke (Fugl-Meyer<50). For these severe patients (n=26), initial Fugl-Meyer was not correlated with motor recovery (R2=0.13; p=ns), whereas iFNr showed strong correlation (R2=0.56; P<0.0001). In multivariate analysis, the iFNr was an independent predictor of motor outcome (&bgr;=2.601; 95% confidence interval=0.304–5.110; P=0.031), improving prediction compared with using only initial Fugl-Meyer, age, and stroke volume (P=0.026). Conclusions— Early measurement of FNr at 24 to 72 hours poststroke is a surrogate marker of corticospinal tract integrity and provides independent prediction of motor outcome at 1 year especially for patients with severe initial impairment.

Nonlocal Regularization for Active Appearance Model: Application to Medial Temporal Lobe Segmentation

The human medial temporal lobe (MTL) is an important part of the limbic system, and its substructures play key roles in learning, memory, and neurodegeneration. The MTL includes the hippocampus (HC), amygdala (AG), parahippocampal cortex (PHC), entorhinal cortex, and perirhinal cortex—structures that are complex in shape and have low between‐structure intensity contrast, making them difficult to segment manually in magnetic resonance images. This article presents a new segmentation method that combines active appearance modeling and patch‐based local refinement to automatically segment specific substructures of the MTL including HC, AG, PHC, and entorhinal/perirhinal cortex from MRI data. Appearance modeling, relying on eigend‐ecomposition to analyze statistical variations in image intensity and shape information in study population, is used to capture global shape characteristics of each structure of interest with a generative model. Patch‐based local refinement, using nonlocal means to compare the image local intensity properties, is applied to locally refine the segmentation results along the structure borders to improve structure delimitation. In this manner, nonlocal regularization and global shape constraints could allow more accurate segmentations of structures. Validation experiments against manually defined labels demonstrate that this new segmentation method is computationally efficient, robust, and accurate. In a leave‐one‐out validation on 54 normal young adults, the method yielded a mean Dice κ of 0.87 for the HC, 0.81 for the AG, 0.73 for the anterior parts of the parahippocampal gyrus (entorhinal and perirhinal cortex), and 0.73 for the posterior parahippocampal gyrus. Hum Brain Mapp 35:377–395, 2014.

Hippocampal microstructural damage correlates with memory impairment in clinically isolated syndrome suggestive of multiple sclerosis

Objective: We investigated whether diffusion tensor imaging (DTI) could reveal early hippocampal damage and clinically relevant correlates of memory impairment in persons with clinically isolated syndrome (CIS) suggestive of multiple sclerosis (MS). Methods: A total of 37 persons with CIS, 32 with MS and 36 controls prospectively included from 2011 to 2014 were tested for cognitive performances and scanned with 3T-magnetic resonance imaging (MRI) to assess volumetric and DTI changes within the hippocampus, whole brain volume and T2-lesion load. Results: While there was no hippocampal atrophy in the CIS group, hippocampal fractional anisotropy (FA) was significantly decreased compared to controls. Decrease in hippocampal FA together with increased mean diffusivity (MD) was even more prominent in MS patients. In CIS, hippocampal MD was correlated with episodic verbal memory performance (r = −0.57, p = 0.0002 and odds ratio (OR) = 0.058, 95% confidence interval (CI) = 0.0057–0.59, p = 0.016 adjusted for age, gender, depression and T2-lesion load), but not with cognitive tasks unrelated to hippocampal functions. Hippocampal MD was the only variable discriminating memory-impaired from memory-preserved persons with CIS (area under the curve (AUC) = 0.77, sensitivity = 90.0%, specificity = 70.3%, positive predictive value (PPV) = 52.9%, negative predictive value (NPV) = 95.0%). Conclusion: DTI alterations within the hippocampus might reflect early neurodegenerative processes that are correlated with episodic memory performance, discriminating persons with CIS according to their memory status.

Automatic thalamus and hippocampus segmentation from MP2RAGE: comparison of publicly available methods and implications for DTI quantification

PurposeIn both structural and functional MRI, there is a need for accurate and reliable automatic segmentation of brain regions. Inconsistent segmentation reduces sensitivity and may bias results in clinical studies. The current study compares the performance of publicly available segmentation tools and their impact on diffusion quantification, emphasizing the importance of using recently developed segmentation algorithms and imaging techniques.MethodsFour publicly available, automatic segmentation methods (volBrain, FSL, FreeSurfer and SPM) are compared to manual segmentation of the thalamus and hippocampus imaged with a recently proposed T1-weighted MRI sequence (MP2RAGE). We evaluate morphometric accuracy on 22 healthy subjects and impact on diffusivity measurements obtained from aligned diffusion-weighted images on a subset of 10 subjects.ResultsCompared to manual segmentation, the highest Dice similarity index of the thalamus is obtained with volBrain using a local library (

CERES: A new cerebellum lobule segmentation method

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