Certifying cost annotations in compilers

We discuss the problem of building a compiler which can lift in a provably correct way pieces of information on the execution cost of the object code to cost annotations on the source code. To this end, we need a clear and flexible picture of: (i) the meaning of cost annotations, (ii) the method to prove them sound and precise, and (iii) the way such proofs can be composed. We propose a so-called labelling approach to these three questions. As a first step, we examine its application to a toy compiler. This formal study suggests that the labelling approach has good compositionality and scalability properties. In order to provide further evidence for this claim, we report our successful experience in implementing and testing the labelling approach on top of a prototype compiler written in OCAML for (a large fragment of) the C language

Efficient depth estimation using trinocular stereo

Journal ArticleWe present recent advancements in our passive trinocular stereo system. These include a technique for calibrating and rectifying in a very efficient and simple manner the triplets of images taken for trinocular stereovision systems. After the rectification of images, epipolar lines are parallel to the axes of the image coordinate frames. Therefore, potential matches between the three images satisfy simpler relations, allowing for a less complicated and more efficient matching algorithm. We also describe a more robust and general control strategy now employed in our trinocular stereo system. We have also developed an innovative method for the reconstruction of 3-D segments which provides better results and a new validation technique based on the observation that neighbors in the image should be neighbors in space. Experiments are presented demonstrating these advancements

3D Consistent Biventricular Myocardial Segmentation Using Deep Learning for Mesh Generation

We present a novel automated method to segment the myocardium of both left and right ventricles in MRI volumes. The segmentation is consistent in 3D across the slices such that it can be directly used for mesh generation. Two specific neural networks with multi-scale coarse-to-fine prediction structure are proposed to cope with the small training dataset and trained using an original loss function. The former segments a slice in the middle of the volume. Then the latter iteratively propagates the slice segmentations towards the base and the apex, in a spatially consistent way. We perform 5-fold cross-validation on the 15 cases from STACOM to validate the method. For training, we use real cases and their synthetic variants generated by combining motion simulation and image synthesis. Accurate and consistent testing results are obtained

A patch-based approach for the segmentation of pathologies: Application to glioma labelling

International audienceIn this paper, we describe a novel and generic approach to address fully-automatic segmentation of brain tumors by using multi-atlas patch-based voting techniques. In addition to avoiding the local search window assumption, the conventional patch-based framework is enhanced through several simple procedures: an improvement of the training dataset in terms of both label purity and intensity statistics, augmented features to implicitly guide the nearest-neighbor-search, multi-scale patches, invariance to cube isometries, stratification of the votes with respect to cases and labels. A probabilistic model automatically delineates regions of interest enclosing high-probability tumor volumes, which allows the algorithm to achieve highly competitive running time despite minimal processing power and resources. This method was evaluated on Multimodal Brain Tumor Image Segmentation challenge datasets. State-of-the-art results are achieved, with a limited learning stage thus restricting the risk of overfit. Moreover, segmentation smoothness does not involve any post-processing

Certifying and reasoning on cost annotations in C programs

International audienceWe present a so-called labelling method to enrich a compiler in order to turn it into a ''cost annotating compiler'', that is, a compiler which can {\em lift} pieces of information on the execution cost of the object code as cost annotations on the source code. These cost annotations characterize the execution costs of code fragments of constant complexity. The first contribution of this paper is a proof methodology that extends standard simulation proofs of compiler correctness to ensure that the cost annotations on the source code are sound and precise with respect to an execution cost model of the object code. As a second contribution, we demonstrate that our label-based instrumentation is scalable because it consists in a modular extension of the compilation chain. To that end, we report our successful experience in implementing and testing the labelling approach on top of a prototype compiler written in \ocaml for (a large fragment of) the {\sc C} language. As a third and last contribution, we provide evidence for the usability of the generated cost annotations as a mean to reason on the concrete complexity of programs written in {\sc C}. For this purpose, we present a {\sc Frama-C} plugin that uses our cost annotating compiler to automatically infer trustworthy logic assertions about the concrete worst case execution cost of programs written in a fragment of the {\sc C} language. These logic assertions are synthetic in the sense that they characterize the cost of executing the entire program, not only constant-time fragments. (These bounds may depend on the size of the input data.) We report our experimentations on some {\sc C} programs, especially programs generated by a compiler for the synchronous programming language {\sc Lustre} used in critical embedded software

3D Consistent Biventricular Myocardial Segmentation Using Deep Learning for Mesh Generation

We present a novel automated method to segment the my-ocardium of both left and right ventricles in MRI volumes. The segmen-tation is consistent in 3D across the slices such that it can be directly used for mesh generation. Two specific neural networks with multi-scale coarse-to-fine prediction structure are proposed to cope with the small training dataset and trained using an original loss function. The former segments a slice in the middle of the volume. Then the latter iteratively propagates the slice segmentations towards the base and the apex, in a spatially consistent way. We perform 5-fold cross-validation on the 15 cases from STACOM to validate the method. For training, we use real cases and their synthetic variants generated by combining motion simulation and image synthesis. Accurate and consistent testing results are obtained

Surface Simplex Meshes for 3D Medical Image Segmentation

International audienceMedical image segmentation is often a difficult task due to the low contrast, the low signal/noise ratio and the presence of outliers in images. However, it remains a critical issue for image interpretation, pattern recognition and automatic diagnosis. Deformable models are well-suited for capturing the geometry and the shape variability of anatomical structures from medical images. Indeed, they introduce an a priori knowledge in the segmentation process that increases its robustness to noise and outliers. In this paper, we address many problems related to volumetric medical image segmentation based on deformable models including model initialization, model topology, deformation behavior and image features extraction

Patch-based Segmentation of Brain Tissues

International audienceWe describe our submission to the Brain Tumor Segmentation Challenge (BraTS) at MICCAI 2013. This segmentation approach is based on similarities between multi-channel patches. After patches are extracted from several MR channels for a test case, similar patches are found in training images for which label maps are known. These labels maps are then combined to result in a segmentation map for the test case. The labelling is performed, in a leave-one-out scheme, for each case of a publicly available training set, which consists of 30 real cases (20 high-grade gliomas, 10 low-grade gliomas) and 50 synthetic cases (25 high-grade gliomas, 25 low-grade gliomas). Promising results are shown on the training set, and we believe this algorithm would perform favourably well in comparison to the state of the art on a testing set

Representation, Shape, Topology and Evolution of Deformable Surfaces. Application to 3D Medical Image Segmentation

These last years, deformable models raised much interest and found various applications in the field of computer vision. They provide an extensible framework to reconstruct shapes. Deformable surfaces, in particular, are used to represent 3D objects. They have been used for pattern recognition [47,2], computer animation [118], geometric modelling [40,75], simulation [45], boundaries tracking [14], segmentation [83], etc. In this report we propose a deformable surfaces survey. Many surface representation have been proposed to meet different 3D reconstruction problem requirements. We try to classify the main representations proposed in the literature and we study the effect of the representation on the model evolution behavior, revealing some similarities between different approaches. Whe then focus on a powerful discrete mesh representation, the simplex meshes. We propose different algorithms to control simplex meshes shape and topology. Whe show results on 3D medical images segmentation

Extended Modality Propagation: Image Synthesis of Pathological Cases

International audienceThis paper describes a novel generative model for the synthesis of multi-modal medical images of pathological cases based on a single label map. Our model builds upon i) a generative model commonly used for label fusion and multi-atlas patch-based segmentation of healthy anatomical structures, ii) the Modality Propagation iterative strategy used for a spatially-coherent synthesis of subject-specific scans of desired image modalities. The expression Extended Modality Propagation is coined to refer to the extension of Modality Propagation to the synthesis of images of pathological cases. Moreover, image synthesis uncertainty is estimated. An application to Magnetic Resonance Imaging synthesis of glioma-bearing brains is i) validated on the training dataset of a Multimodal Brain Tumor Image Segmentation challenge, ii) compared to the state-of-the-art in glioma image synthesis, and iii) illustrated using the output of two different tumor growth models. Such a generative model allows the generation of a large dataset of synthetic cases, which could prove useful for the training, validation, or benchmarking of image processing algorithms