Olivier Commowick

Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge

EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intra patient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This is compounded by the fact that researchers typically test only on their own data, which varies widely. For this reason, reliable assessment and comparison of different registration algorithms has been virtually impossible in the past. In this work we present the results of the launch phase of EMPIRE10, which comprised the comprehensive evaluation and comparison of 20 individual algorithms from leading academic and industrial research groups. All algorithms are applied to the same set of 30 thoracic CT pairs. Algorithm settings and parameters are chosen by researchers expert in the con figuration of their own method and the evaluation is independent, using the same criteria for all participants. All results are published on the EMPIRE10 website (http://empire10.isi.uu.nl). The challenge remains ongoing and open to new participants. Full results from 24 algorithms have been published at the time of writing. This paper details the organization of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms. The gain in knowledge and future work are discussed.

A log-euclidean framework for statistics on diffeomorphisms

In this article, we focus on the computation of statistics of invertible geometrical deformations (i.e., diffeomorphisms), based on the generalization to this type of data of the notion of principal logarithm. Remarkably, this logarithm is a simple 3D vector field, and is well-defined for diffeomorphisms close enough to the identity. This allows to perform vectorial statistics on diffeomorphisms, while preserving the invertibility constraint, contrary to Euclidean statistics on displacement fields. We also present here two efficient algorithms to compute logarithms of diffeomorphisms and exponentials of vector fields, whose accuracy is studied on synthetic data. Finally, we apply these tools to compute the mean of a set of diffeomorphisms, in the context of a registration experiment between an atlas an a database of 9 T1 MR images of the human brain.

A Fast and Log-Euclidean Polyaffine Framework for Locally Linear Registration

In this article, we focus on the parameterization of non-rigid geometrical deformations with a small number of flexible degrees of freedom. In previous work, we proposed a general framework called polyaffine to parameterize deformations with a finite number of rigid or affine components, while guaranteeing the invertibility of global deformations. However, this framework lacks some important properties: the inverse of a polyaffine transformation is not polyaffine in general, and the polyaffine fusion of affine components is not invariant with respect to a change of coordinate system. We present here a novel general framework, called Log-Euclidean polyaffine, which overcomes these defects.We also detail a simple algorithm, the Fast Polyaffine Transform, which allows to compute very efficiently Log-Euclidean polyaffine transformations and their inverses on regular grids. The results presented here on real 3D locally affine registration suggest that our novel framework provides a general and efficient way of fusing local rigid or affine deformations into a global invertible transformation without introducing artifacts, independently of the way local deformations are first estimated.

Atlas-based delineation of lymph node levels in head and neck computed tomography images

PURPOSE Radiotherapy planning requires accurate delineations of the tumor and of the critical structures. Atlas-based segmentation has been shown to be very efficient to automatically delineate brain critical structures. We therefore propose to construct an anatomical atlas of the head and neck region. METHODS AND MATERIALS Due to the high anatomical variability of this region, an atlas built from a single image as for the brain is not adequate. We address this issue by building a symmetric atlas from a database of manually segmented images. First, we develop an atlas construction method and apply it to a database of 45 Computed Tomography (CT) images from patients with node-negative pharyngo-laryngeal squamous cell carcinoma manually delineated for radiotherapy. Then, we qualitatively and quantitatively evaluate the results generated by the built atlas based on Leave-One-Out framework on the database. RESULTS We present qualitative and quantitative results using this atlas construction method. The evaluation was performed on a subset of 12 patients among the original CT database of 45 patients. Qualitative results depict visually well delineated structures. The quantitative results are also good, with an error with respect to the best achievable results ranging from 0.196 to 0.404 with a mean of 0.253. CONCLUSIONS These results show the feasibility of using such an atlas for radiotherapy planning. Many perspectives are raised from this work ranging from extensive validation to the construction of several atlases representing sub-populations, to account for large inter-patient variabilities, and populations with node-positive tumors.

An efficient locally affine framework for the smooth registration of anatomical structures.

Intra-subject and inter-subject nonlinear registration based on dense transformations requires the setting of many parameters, mainly for regularization. This task is a major issue, as the global quality of the registration will depend on it. Setting these parameters is, however, very hard, and they may have to be tuned for each patient when processing data acquired by different centers or using different protocols. Thus, we present in this article a method to introduce more coherence in the registration by using fewer degrees of freedom than with a dense registration. This is done by registering the images only on user-defined areas, using a set of affine transformations, which are optimized together in a very efficient manner. Our framework also ensures a smooth and coherent transformation thanks to a new regularization of the affine components. Finally, we ensure an invertible transformation thanks to the Log-Euclidean polyaffine framework. This allows us to get a more robust and very efficient registration method, while obtaining good results as explained below. We performed a qualitative and quantitative evaluation of the obtained results on two applications: first on atlas-based brain segmentation, comparing our results with a dense registration algorithm. Then the second application for which our framework is particularly well suited concerns bone registration in the lower-abdomen area. We obtain in this case a better positioning of the femoral heads than with a dense registration. For both applications, we show a significant improvement in computation time, which is crucial for clinical applications.

Estimating A Reference Standard Segmentation With Spatially Varying Performance Parameters: Local MAP STAPLE

We present a new algorithm, called local MAP STAPLE, to estimate from a set of multi-label segmentations both a reference standard segmentation and spatially varying performance parameters. It is based on a sliding window technique to estimate the segmentation and the segmentation performance parameters for each input segmentation. In order to allow for optimal fusion from the small amount of data in each local region, and to account for the possibility of labels not being observed in a local region of some (or all) input segmentations, we introduce prior probabilities for the local performance parameters through a new maximum a posteriori formulation of STAPLE. Further, we propose an expression to compute confidence intervals in the estimated local performance parameters. We carried out several experiments with local MAP STAPLE to characterize its performance and value for local segmentation evaluation. First, with simulated segmentations with known reference standard segmentation and spatially varying performance, we show that local MAP STAPLE performs better than both STAPLE and majority voting. Then we present evaluations with data sets from clinical applications. These experiments demonstrate that spatial adaptivity in segmentation performance is an important property to capture. We compared the local MAP STAPLE segmentations to STAPLE, and to previously published fusion techniques and demonstrate the superiority of local MAP STAPLE over other state-of-the-art algorithms.

The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery

Diffusion tensor imaging (DTI) tractography reconstruction of white matter pathways can help guide brain tumor resection. However, DTI tracts are complex mathematical objects and the validity of tractography‐derived information in clinical settings has yet to be fully established. To address this issue, we initiated the DTI Challenge, an international working group of clinicians and scientists whose goal was to provide standardized evaluation of tractography methods for neurosurgery. The purpose of this empirical study was to evaluate different tractography techniques in the first DTI Challenge workshop.

A pre-clinical assessment of an atlas-based automatic segmentation tool for the head and neck

BACKGROUND AND PURPOSE Accurate conformal radiotherapy treatment requires manual delineation of target volumes and organs at risk (OAR) that is both time-consuming and subject to large inter-user variability. One solution is atlas-based automatic segmentation (ABAS) where a priori information is used to delineate various organs of interest. The aim of the present study is to establish the accuracy of one such tool for the head and neck (H&N) using two different evaluation methods. MATERIALS AND METHODS Two radiotherapy centres were provided with an ABAS tool that was used to outline the brainstem, parotids and mandible on several patients. The results were compared to manual delineations for the first centre (EM1) and reviewed/edited for the second centre (EM2), both of which were deemed as equally valid gold standards. The contours were compared in terms of their volume, sensitivity and specificity with the results being interpreted using the Dice similarity coefficient and a receiver operator characteristic (ROC) curve. RESULTS Automatic segmentation took typically approximately 7min for each patient on a standard PC. The results indicated that the atlas contour volume was generally within +/-1SD of each gold standard apart from the parotids for EM1 and brainstem for EM2 that were over- and under-estimated, respectively (within +/-2SD). The similarity of the atlas contours with their respective gold standard was satisfactory with an average Dice coefficient for all OAR of 0.68+/-0.25 for EM1 and 0.82+/-0.13 for EM2. All data had satisfactory sensitivity and specificity resulting in a favourable position in ROC space. CONCLUSIONS These tests have shown that the ABAS tool exhibits satisfactory sensitivity and specificity for the OAR investigated. There is, however, a systematic over-segmentation of the parotids (EM1) and under-segmentation of the brainstem (EM2) that require careful review and editing in the majority of cases. Such issues have been discussed with the software manufacturer and a revised version is due for release.

Non-rigid Atlas to Subject Registration with Pathologies for Conformal Brain Radiotherapy

Warping a digital atlas toward a patient image allows the simultaneous segmentation of several structures. This may be of great interest for cerebral images, since the brain contains a large number of small but important structures (optical nerves, grey nuclei, etc.). One important application is the conformal radiotherapy of cerebral tumor, where a precise delineation of all these structures is required. However, in this case, the variability induced by the tumor or a surgical resection, that are not present in the digital atlas, prevents an accurate registration between the atlas and the patient images. Since our registration method allows to locally control the amount of regularization, we are able to explicitly introduce those areas in the warping process. For computational efficiency, we have created a parallel implementation that can be used from the clinical environment through a grid interface.

Diffusion Features of White Matter in Tuberous Sclerosis With Tractography

Normal-appearing white matter has been shown via diffusion tensor imaging to be affected in tuberous sclerosis complex. Under the hypothesis that some systems might be differentially affected, including the visual pathways and systems of social cognition, diffusion properties of various regions of white matter were compared. For 10 patients and 6 age-matched control subjects, 3 T magnetic resonance imaging was assessed using diffusion tensor imaging obtained in 35 directions. Three-dimensional volumes corresponding to the geniculocalcarine tracts were extracted via tractography, and two-dimensional regions of interest were used to sample other regions. Regression analysis indicated lower fractional anisotropy in the splenium of corpus callosum and geniculocalcarine tracts in tuberous sclerosis complex group, as well as lower axial diffusivity in the internal capsule, superior temporal gyrus, and geniculocalcarine tracts. Mean and radial diffusivity of the splenium of corpus callosum were higher in the tuberous sclerosis complex group. The differences in diffusion properties of white matter between tuberous sclerosis complex patients and control subjects suggest disorganized and structurally compromised axons with poor myelination. The visual and social cognition systems appear to be differentially involved, which might in part explain the behavioral and cognitive characteristics of the tuberous sclerosis complex population.