AN AUTOMATIC SYSTEM FOR THE ANALYSIS OF INTERCELLULAR COMMUNICATION AND EARLY CARCINOGENESIS

International audienceSome recent works on intercellular communication pointed out an impaired trafficking of Cx43 proteins in early carcinogenesis. In collaboration with biologists, we propose an automatic system for the analysis of spatial protein configurations within cells at early tumor stages. This system is an essential step towards the future development of a computer-aided diagnosis tool and the statistical validation of biological hypotheses about Cx43 expressions and configurations during tumorogenesis. The proposed system contains two dependent part: a segmentation part in which the cell structures of interest are automatically located on images and a characterization part in which some spatial features are computed for the classification of cells. Using immunofluorescent images of cells, the nucleus, cytoplasm and proteins structures within the cell are extracted. Then, some spatial features are computed to characterize spatial configurations of the proteins with regard to the nucleus and cytoplasm areas in the image. Last, the 3D cell images are classified into pathogenic or viable classes. The system has been quantitatively evaluated over 60 cell images acquired by a deconvolution high-resolution microscope and whose ground truth has been manually given by a biologist expert. As a perspective, a 3D spatial reasoning and visualization module is currently under development

LOW RESOLUTION CONVOLUTIONAL NEURAL NETWORK FOR AUTOMATIC TARGET RECOGNITION

International audienceIn this work, we present an extended study of image representations for automatic target recognition (ATR). More specifically , we tackle the issue of the image resolution influence on the classification performances, an understudied yet major parameter in image classification. Besides, we propose a parallel between low-resolution image recognition and image classification in a fine-grained context. Indeed, in these two particular cases, the main difficulty is to discriminate small details on very similar objects. In this paper, we evaluate Fisher Vectors and deep representations on two significant publicly available fine-grained oriented datasets with respect to the input image resolution. We also introduce LR-CNN, a deep structure designed for classification of low-resolution images with strong semantic content. This net provides rich compact features and outperforms both pre-trained deep features and Fisher Vectors. We also present visual results of our LR-CNN on Thales near-infrared images

Automated Nuclear Analysis of Leishmania major Telomeric Clusters Reveals Changes in Their Organization during the Parasite's Life Cycle

Parasite virulence genes are usually associated with telomeres. The clustering of the telomeres, together with their particular spatial distribution in the nucleus of human parasites such as Plasmodium falciparum and Trypanosoma brucei, has been suggested to play a role in facilitating ectopic recombination and in the emergence of new antigenic variants. Leishmania parasites, as well as other trypanosomes, have unusual gene expression characteristics, such as polycistronic and constitutive transcription of protein-coding genes. Leishmania subtelomeric regions are even more unique because unlike these regions in other trypanosomes they are devoid of virulence genes. Given these peculiarities of Leishmania, we sought to investigate how telomeres are organized in the nucleus of Leishmania major parasites at both the human and insect stages of their life cycle. We developed a new automated and precise method for identifying telomere position in the three-dimensional space of the nucleus, and we found that the telomeres are organized in clusters present in similar numbers in both the human and insect stages. While the number of clusters remained the same, their distribution differed between the two stages. The telomeric clusters were found more concentrated near the center of the nucleus in the human stage than in the insect stage suggesting reorganization during the parasite's differentiation process between the two hosts. These data provide the first 3D analysis of Leishmania telomere organization. The possible biological implications of these findings are discussed

Image-Derived Input Function for Human Brain Using High Resolution PET Imaging with [11C](R)-rolipram and [11C]PBR28

The aim of this study was to test seven previously published image-input methods in state-of-the-art high resolution PET brain images. Images were obtained with a High Resolution Research Tomograph plus a resolution-recovery reconstruction algorithm using two different radioligands with different radiometabolite fractions. Three of the methods required arterial blood samples to scale the image-input, and four were blood-free methods. values was quantified using a scoring system. Using the image input methods that gave the most accurate results with Logan analysis, we also performed kinetic modelling with a two-tissue compartment model.)-rolipram, which has a lower metabolite fraction. Compartment modeling gave less reliable results, especially for the estimation of individual rate constants.C]PBR28), the more difficult it is to obtain a reliable image-derived input function; and 4) in association with image inputs, graphical analyses should be preferred over compartmental modelling

Transaxial slices from a [¹¹C](<i>R</i>)-rolipram brain scan of a healthy volunteer and from a simulated study using a digital phantom.

<p>Upper row: [¹¹C](<i>R</i>)-rolipram images across the thalamus summed over the whole duration of the scan from a phantom (<b>A</b>) and a healthy volunteer (<b>B</b>). The phantom images are realistic and quite similar to those from the real subjects. The external rim of activity surrounding the brain, in both the subject and the phantom, is scalp activity. Middle row: images summed over the first two minutes at the carotid level. The carotids are well visible near the temporal lobes for both the phantom (<b>C</b>) and the healthy volunteer (<b>D</b>). The regions of high activity visible in the lower part of the cerebellum of the subject (<b>D</b>) are the cerebellar venous sinuses (not simulated in the phantom studies). Bottom row: late images (three summed frames taken at about 1 hour after injection) from a phantom (<b>E</b>) and a subject (<b>F</b>). At late times the carotids are not well visible anymore and the spill-over effect from surrounding tissues becomes more important.</p

Image/blood <i>V</i>_T ratios (mean ± SD) and scores calculated for each method.

<p>The scores are calculated by giving 2 points each time the image/arterial <i>V</i>_T ratio comprised between ±5%, 1 point if comprised between ±5–10%, and 0 points if higher than ±10%. The most accurate results for both tracers were obtained using two blood-based methods (Chen and Mourik) and the Logan plot. When <i>V</i>_T ratios were calculated using these two blood-based methods and an unconstrained two-tissue compartment model (2TCM), the overall results were less accurate. Please note that even if the Chen-[¹¹C]PBR28 score is comparable between the two modeling approaches (11/38 vs. 12/38), the 2TCM yields a greater bias and a greater standard deviation of the mean <i>V</i>_T ratio (1.10±0.17 vs. 1.15±0.20).</p

AUC ratio (mean ± SD) calculated for each method and for both whole-blood and plasma curves for each tracer.

<p>The AUC ratio is on average more accurate for blood-based methods than for blood-free methods. For [¹¹C]PBR28, but not for [¹¹C](<i>R</i>)-rolipram, the parent AUC ratios of the blood-based methods are less accurate than the whole-blood AUC ratios.</p

Image/blood ratio of the individual rate constants obtained with an unconstrained two-tissue compartment model.

<p>Image/blood ratio of the individual rate constants obtained with an unconstrained two-tissue compartment model.</p