Rémi Giraud

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.

CERES: A new cerebellum lobule segmentation method

ABSTRACT The human cerebellum is involved in language, motor tasks and cognitive processes such as attention or emotional processing. Therefore, an automatic and accurate segmentation method is highly desirable to measure and understand the cerebellum role in normal and pathological brain development. In this work, we propose a patch‐based multi‐atlas segmentation tool called CERES (CEREbellum Segmentation) that is able to automatically parcellate the cerebellum lobules. The proposed method works with standard resolution magnetic resonance T1‐weighted images and uses the Optimized PatchMatch algorithm to speed up the patch matching process. The proposed method was compared with related recent state‐of‐the‐art methods showing competitive results in both accuracy (average DICE of 0.7729) and execution time (around 5 minutes). HIGHLIGHTSWe present a novel method for cerebellum lobule segmentation on MRI.The method consists of a fast multi‐atlas non‐local patch‐based label fusion.Our proposed method was shown to improve the state‐of‐the‐art methods with a reduced temporal cost (5 minutes).The pipeline presented in this work will be made available to scientific community through our web ‐based platform volBrain.

SuperPatchMatch: An Algorithm for Robust Correspondences Using Superpixel Patches

Superpixels have become very popular in many computer vision applications. Nevertheless, they remain underexploited, since the superpixel decomposition may produce irregular and nonstable segmentation results due to the dependency to the image content. In this paper, we first introduce a novel structure, a superpixel-based patch, called SuperPatch. The proposed structure, based on superpixel neighborhood, leads to a robust descriptor, since spatial information is naturally included. The generalization of the PatchMatch method to SuperPatches, named SuperPatchMatch, is introduced. Finally, we propose a framework to perform fast segmentation and labeling from an image database, and demonstrate the potential of our approach, since we outperform, in terms of computational cost and accuracy, the results of state-of-the-art methods on both face labeling and medical image segmentation.

SCALP: Superpixels with Contour Adherence using Linear Path

Superpixel decomposition methods are generally used as a pre-processing step to speed up image processing tasks. They group the pixels of an image into homogeneous regions while trying to respect existing contours. For all state-of-the-art superpixel decomposition methods, a trade-off is made between 1) computational time, 2) adherence to image contours and 3) regularity and compactness of the decomposition. In this paper, we propose a fast method to compute Superpixels with Contour Adherence using Linear Path (SCALP) in an iterative clustering framework. The distance computed when trying to associate a pixel to a superpixel during the clustering is enhanced by considering the linear path to the superpixel barycenter. The proposed framework produces regular and compact superpixels that adhere to the image contours. We provide a detailed evaluation of SCALP on the standard Berkeley Segmentation Dataset. The obtained results outperform state-of-the-art methods in terms of standard superpixel and contour detection metrics.

Patch-Based DTI Grading: Application to Alzheimer’s Disease Classification

Early diagnosis is one of the most important challenges related to Alzheimer’s disease (AD). To address this issue, numerous studies proposed biomarkers based on anatomical MRI. Among them, patch-based grading demonstrated state-of-the-art results when applied to T1-weighted MRI. In this work, we propose to use a similar framework on different diffusion parameters extracted from DTI. We also propose to use a fast patch-based search strategy to provide novel biomarkers for the early detection of AD. We intensively compare our new grading-based DTI features with basic MRI/DTI biomarkers and evaluate our method within a cross validation classification framework. Finally, we demonstrate that the proposed biomarkers obtain competitive results for the identification of the different stages of AD.

Robust Superpixels using Color and Contour Features along Linear Path

Superpixel decomposition methods are widely used in computer vision and image processing frameworks. By reducing the set of pixels to process, the computational burden can be drastically reduced. For most superpixel methods, a trade-off is computed between 1) color homogeneity, 2) adherence to the image contours and 3) shape regularity of the decomposition. In this paper, we propose an iterative framework that jointly enforces all these aspects and provides accurate and robust Superpixels with Contour Adherence using Linear Path (SCALP). The resulting superpixels adhere to the image contours while their regularity is enforced. During the decomposition process , we propose to compute the color distance along the linear path between the pixel and the corresponding superpixel barycenter. A contour prior is also considered on this path to prevent the crossing of image boundaries when associating a pixel to a superpixel. Finally, in order to improve the clustering accuracy and the robustness to noise, we integrate the pixel neighborhood information in the decomposition, while preserving the same computational complexity. SCALP is extensively evaluated on the standard Berkeley segmentation dataset, and the obtained results outperform the ones of state-of-the-art methods in terms of superpixel and contour detection metrics. The method is also extended to generate volume supervoxels, and evaluated on 3D MRI segmentation.

Evaluation Framework of Superpixel Methods with a Global Regularity Measure

Abstract. In the superpixel literature, the comparison of state-of-the-art methods can be biased by the nonrobustness of some metrics to decomposition aspects, such as the superpixel scale. Moreover, most recent decomposition methods allow setting a shape regularity parameter, which can have a substantial impact on the measured performances. We introduce an evaluation framework that aims to unify the comparison process of superpixel methods. We investigate the limitations of existing metrics and propose to evaluate each of the three core decomposition aspects: color homogeneity, respect of image objects, and shape regularity. To measure the regularity aspect, we propose a global regularity (GR) measure, which addresses the nonrobustness of state-of-the-art metrics. We evaluate recent superpixel methods with these criteria, at several superpixel scales and regularity levels. The proposed framework reduces the bias in the comparison process of state-of-the-art superpixel methods. Finally, we demonstrate that the proposed GR measure is correlated with the performances of various applications.