Razmig Kéchichian

A web interface for 3D visualization and interactive segmentation of medical images

We propose a web-accessible image visualization and processing framework well-suited for medical applications. Exploiting client-side HTML5 and WebGL technologies, our proposal allows the end-user to efficiently browse and visualize volumic images in an Out-Of-Core (OOC) manner, annotate and apply server-side image processing algorithms and interactively visualize 3D medical models. Server-side implementation is driven by a file-based, simple, robust and flexible Remote Procedure Call (RPC) scheme well suited for heterogeneous applications. We demonstrate the efficiency of our approach with both an interactive medical image segmentation and a 3D rendering of segmented anatomical structures. As a secondary contribution, we improve the segmentation algorithm with the introduction of user-defined anatomical priors.

Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms: VISCERAL Anatomy Benchmarks

Variations in the shape and appearance of anatomical structures in medical images are often relevant radiological signs of disease. Automatic tools can help automate parts of this manual process. A cloud-based evaluation framework is presented in this paper including results of benchmarking current state-of-the-art medical imaging algorithms for anatomical structure segmentation and landmark detection: the VISCERAL Anatomy benchmarks. The algorithms are implemented in virtual machines in the cloud where participants can only access the training data and can be run privately by the benchmark administrators to objectively compare their performance in an unseen common test set. Overall, 120 computed tomography and magnetic resonance patient volumes were manually annotated to create a standard Gold Corpus containing a total of 1295 structures and 1760 landmarks. Ten participants contributed with automatic algorithms for the organ segmentation task, and three for the landmark localization task. Different algorithms obtained the best scores in the four available imaging modalities and for subsets of anatomical structures. The annotation framework, resulting data set, evaluation setup, results and performance analysis from the three VISCERAL Anatomy benchmarks are presented in this article. Both the VISCERAL data set and Silver Corpus generated with the fusion of the participant algorithms on a larger set of non-manually-annotated medical images are available to the research community.

Shortest-Path Constraints for 3D Multiobject Semiautomatic Segmentation Via Clustering and Graph Cut

We derive shortest-path constraints from graph models of structure adjacency relations and introduce them in a joint centroidal Voronoi image clustering and Graph Cut multiobject semiautomatic segmentation framework. The vicinity prior model thus defined is a piecewise-constant model incurring multiple levels of penalization capturing the spatial configuration of structures in multiobject segmentation. Qualitative and quantitative analyses and comparison with a Potts prior-based approach and our previous contribution on synthetic, simulated, and real medical images show that the vicinity prior allows for the correct segmentation of distinct structures having identical intensity profiles and improves the precision of segmentation boundary placement while being fairly robust to clustering resolution. The clustering approach we take to simplify images prior to segmentation strikes a good balance between boundary adaptivity and cluster compactness criteria furthermore allowing to control the trade-off. Compared with a direct application of segmentation on voxels, the clustering step improves the overall runtime and memory footprint of the segmentation process up to an order of magnitude without compromising the quality of the result.

Automatic 3D Multiorgan Segmentation via Clustering and Graph Cut Using Spatial Relations and Hierarchically-Registered Atlases

We propose a generic method for automatic multiple-organ segmentation based on a multilabel Graph Cut optimization approach which uses location likelihood of organs and prior information of spatial relationships between them. The latter is derived from shortest-path constraints defined on the adjacency graph of structures and the former is defined by probabilistic atlases learned from a training dataset. Organ atlases are mapped to the image by a fast (2+1)D hierarchical registration method based on SURF keypoints. Registered atlases are furthermore used to derive organ intensity likelihoods. Prior and likelihood models are then introduced in a joint centroidal Voronoi image clustering and Graph Cut multiobject segmentation framework. Qualitative and quantitative evaluation has been performed on contrast-enhanced CT images from the Visceral Benchmark dataset.

Efficient multi-object segmentation of 3D medical images using clustering and graph cuts

We propose an application of multi-label “Graph Cut” optimization algorithms to the simultaneous segmentation of multiple anatomical structures, initialized via an over-segmentation of the image computed by a fast centroidal Voronoi diagram (CVD) clustering algorithm. With respect to comparable segmentations computed directly on the voxels of image volumes, we demonstrate performance improvements on both execution speed and memory footprint by, at least, an order of magnitude, making it possible to process large volumes on commodity hardware which could not be processed pixel-wise.

Image denoising using contextual modeling of curvelet coefficients

We propose an image denoising method which takes curvelet domain inter-scale, inter-location and inter-orientation dependencies into account in a maximum a posteriori labeling of the curvelet coefficients of a noisy image. The rationale is that generalized neighborhoods of curvelet coefficients contain more reliable information on the true image than individual coefficients. Based on the labeling of coefficients and their magnitudes, a smooth thresholding functional produces denoised coefficients from which the denoised image is reconstructed. We also outline a faster approach to labeling and thresholding, relying on contextual comparisons of coefficients. Quantitative and qualitative evaluations on natural and X-ray images show that our method outperforms related multiscale approaches and compares favorably to the state-of-art BM3D method on X-ray data while executing faster.

Positioning of anatomical landmarks in orthopedics by MESH registration

We propose a system for automatic positioning of identified landmarks on anatomical structures generated from medical images. We obtain a rigid registration of 3-D shapes using the Iterative Closest Point (ICP) algorithm where a curvature constraint is added as a supplementary dimension in order to improve robustness. A dichotomic scale search and a local rigid registration allow fast local adjustment. We show the effectiveness of the proposed approach by comparing the automatic positioning of anatomical landmarks against the manual positioning by a trained operator.

New data model for graph-cut segmentation: Application to automatic melanoma delineation

We propose a new data model for graph-cut image segmentation, defined according to probabilities learned by a classification process. Unlike traditional graph-cut methods, the data model takes into account not only color but also texture and shape information. For melanoma images, we also introduce skin chromophore features and automatically derive “seed” pixels used to train the classifier from a coarse initial segmentation. On natural images, our method successfully segments objects having similar color but different texture. Its application to melanoma delineation compares favorably to manual delineation and related graph-cut segmentation methods.

Automatic Multiorgan Segmentation Using Hierarchically Registered Probabilistic Atlases

We propose a generic method for the automatic multiple-organ segmentation of 3D images based on a multilabel graph cut optimization approach which uses location likelihood of organs and prior information of spatial relationships between them. The latter is derived from shortest-path constraints defined on the adjacency graph of structures and the former is defined by probabilistic atlases learned from a training dataset. Organ atlases are mapped to the image by a fast (2+1)D hierarchical registration method based on SURF keypoints. Registered atlases are also used to derive organ intensity likelihoods. Prior and likelihood models are then introduced in a joint centroidal Voronoi image clustering and graph cut multiobject segmentation framework. Qualitative and quantitative evaluation has been performed on contrast-enhanced CT and MR images from the VISCERAL dataset.