Segmentation des structures vasculaires en imagerie TDM par filtrage localement connexe

International audienceLa segmentation vasculaire est souvent une étape essentielle lors de l’analyse d’images médicales, et ce pour de diverses modalités d’imagerie. Malgré la grande richesse de la littérature dans le domaine, les méthodes proposées requièrent pour la plupart une adaptation selon le problème posé et sont, parfois, en deçà de l’exactitude souhaitée en termes de sensibilité et spécificité . Cet article propose une méthode générale de segmentation vasculaire, basée sur du filtrage localement connexe (FLC) multirésolution. L’approche de filtrage opère une détection et une suppression progressive des vaisseaux à partir du relief de l’image et ce à chaque niveau de résolution en combinant des filtres directionnels 2D - 3D. Le résultat de l’approche proposée est démontré sur différentes modalités d’images en imagerie pulmonaire, hépatique et coronair

Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study

The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.Rudyanto, RD.; Kerkstra, S.; Van Rikxoort, EM.; Fetita, C.; Brillet, P.; Lefevre, C.; Xue, W.... (2014). Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study. Medical Image Analysis. 18(7):1217-1232. doi:10.1016/j.media.2014.07.003S1217123218

An automated approach for single-cell tracking in epifluorescence microscopy applied to E. coli growth analysis on microfluidics biochips

International audienceWith the accumulation of knowledge for the intimate molecular mechanisms governing the processes inside the living cells in the later years, the ability to characterize the performance of elementary genetic circuits and parts at the single-cell level is becoming of crucial importance. Biological science is arriving to the point where it can develop hypothesis for the action of each molecule participating in the biochemical reactions and need proper techniques to test those hypothesis. Microfluidics is emerging as the technology that combined with high-magnification microscopy will allow for the long-term single-cell level observation of bacterial physiology. In this study we design, build and characterize the gene dynamics of genetic circuits as one of the basic parts governing programmed cell behavior. We use E. coli as model organism and grow it in microfluidics chips, which we observe with epifluorescence microscopy. One of the most invaluable segments of this technology is the consequent image processing, since it allows for the automated analysis of vast amount of single-cell observation and the fast and easy derivation of conclusions based on that data. Specifically, we are interested in promoter activity as function of time. We expect it to be oscillatory and for that we use GFP (green fluorescent protein) as a reporter in our genetic circuits. In this paper, an automated framework for single-cell tracking in phase-contrast microscopy is developed, combining 2D segmentation of cell time frames and graph-based reconstruction of their spatiotemporal evolution with fast tracking of the associated fluorescence signal. The results obtained on the investigated biological database are presented and discussed

Comparison of CNN architectures and training strategies for quantitative analysis of idiopathic interstitial pneumonia

International audienceFibrosing idiopathic interstitial pneumonia (IIP) is a subclass of interstitial lung diseases manifesting as progressive worsening of lung function. Such degradation is a continuous and irreversible process which requires quantitative follow-up of patients to assess the pathology occurrence and extent in the lung. The development of automated CAD tools for such purpose is oriented today towards machine learning approaches and in particular convolutional neural networks. The difficulty remains in the choice of the network architecture that best fit to the problem, in straight relationship with available databases for training. We follow-up our work on lung texture analysis and investigate different CNN architectures and training strategies in the context of a limited database, with high class imbalance and subjective and partial annotations. We show that increased performances are achieved using an end-to-end architecture versus patch-based, but also that naive implementation in the former case should be avoided. The proposed solution is able to leverage global information in the scan and shows a high improvement in the F1 scores of the predicted classes and visual results of predictions in better accordance with the radiologist expectations

Semi-quantitative assessment of pulmonary perfusion in children using dynamic contrast-enhanced MRI

15 pagesInternational audienceThis paper addresses the study of semi-quantitative assessment of pulmonary perfusion acquired from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in a study population mainly composed of children with pulmonary malformations. The automatic analysis approach proposed is based on the indicator-dilution theory introduced in 1954. First, a robust method is developed to segment the pulmonary artery and the lungs from anatomical MRI data, exploiting 2D and 3D mathematical morphology operators. Second, the time-dependent contrast signal of the lung regions is deconvolved by the arterial input function for the assessment of the local hemodynamic system parameters, ie. mean transit time, pulmonary blood volume and pulmonary blood flow. The discrete deconvolution method implements here a truncated singular value decomposition (tSVD) method. Parametric images for the entire lungs are generated as additional elements for diagnosis and quantitative follow-up. The preliminary results attest the feasibility of perfusion quantification in pulmonary DCE-MRI and open an interesting alternative to scintigraphy for this type of evaluation, to be considered at least as a preliminary decision in the diagnostic due to the large availability of the technique and to the non-invasive aspects

Cascade of convolutional neural networks for lung texture classification: overcoming ontological overlapping

International audienceThe infiltrative lung diseases are a class of irreversible, non-neoplastic lung pathologies requiring regular follow-up with CT imaging. Quantifying the evolution of the patient status imposes the development of automated classification tools for lung texture. Traditionally, such classification relies on a two-dimensional analysis of axial CT images. This paper proposes a cascade of the existing CNN based CAD system, specifically tuned-up. The advantage of using a deep learning approach is a better regularization of the classification output. In a preliminary evaluation, the combined approach was tested on a 13 patient database of various lung pathologies, showing an increase of 10% in True Positive Rate (TPR) with respect to the best suited state of the art CNN for this tas

Diffusion-based interpolation with geometrical constraints applied to investigation of interstitial lung diseases

International audienceImage interpolation is required in several biomedical applications, such as estimating missing information, simulating a continuously evolving process or for comparative analysis in the same referential (longitudinal studies, patient follow-up, analysis of moving organs). The purpose of this study is to develop a 2D interpolation method that can simultaneously apply as a geometric registration approach of two images. The underlying idea is to estimate the transform between the source and target images based on semantic information available, namely the knowledge of the image regions that have to match during the deformation. Such initial knowledge is available from image segmentation and stored as a Max-Tree atlas. The proposed solution consists of building the deformation field in a higher (3D) dimension space based on the identification of the corresponding points in the source/target atlases. A vector field is initiated at the atlas section by pointing to the corresponding regions in the pair atlas. The diffusion of the initial vector field through the entire 3D space, with constraints associated to each atlas region, allows generating a smooth deformation field consistent with even opposite displacements. The final transformation between the source/target images is defined as the flow computed from the deformation vector field. The proposed approach is demonstrated for two use cases of interstitial lung disease: severity assessment in acute respiratory distress syndrome (ARDS) and investigation of lung compliance in fibrosing idiopathic interstitial pneumonia (fIIP)

A unified approach for high throughput analysis of real-time biomolecular interactions in surface plasmon resonance and fluorescence imaging

International audienceThe analysis of real-time biomolecular interactions (observation is performed as the biological interaction occurs) provides information on the formation of target/probe complexes, particularly on their dynamic behaviours. Namely, it allows the determination of the affinity constant, a static value that characterizes the interaction properties, using two dynamic values, the association and dissociation constants. Such dynamic behaviour can be assessed either with surface plasmon resonance (SPR) or uorescence-based biosensors. The challenging issue is the automatic extraction and analysis of the interaction signal for each spotted probe on the biosensor in a highthroughput framework (hundreds of probes). This paper addresses such issue and develops a uniffied approach for analyzing the image data provided by the above-mentioned technologies. A mathematical modelling of the image data allowed building-up a virtual biosensor able to simulate biologic experiences related to various possible parameters (level of signal and noise, presence of artefacts, surface functionalization, spotting heterogeneity). Based on such simulation, a generic and automated approach combining 3D mathematical morphology and spatio-temporal classiffication is proposed for detecting the interacting probes, segmenting the regions of effective signal, and characterizing the associated affinity constants. The developed method has been assessed both qualitatively and quantitatively on simulated and experimental datasets and showed accurate results (maximum error of 7% for the most difficult cases in terms of noise and surface functionalization)