Comparison of optical sensors discrimination ability using spectral libraries

In remote sensing, the ability to discriminate different land covers or material types is directly linked with the spectral resolution and sampling provided by the optical sensor. Previous studies showed that the spectral resolution is a critical issue, especially in complex environment. In spite of the increasing availability of hyperspectral data, multispectral optical sensors onboard various satellites are acquiring everyday a massive amount of data with a relatively poor spectral resolution (i.e. usually about 4 to 7 spectral bands). These remotely sensed data are intensively used for Earth observation regardless of their limited spectral resolution. In this paper, we studied seven of these optical sensors: Pleiades, QuickBird, SPOT5, Ikonos, Landsat TM, Formosat and Meris. This study focuses on the ability of each sensor to discriminate different materials according to its spectral resolution. We used four different spectral libraries which contains around 2500 spectra of materials and land covers with a fine spectral resolution. These spectra were convolved with the Relative Spectral Responses (RSR) of each sensor to create spectra at the sensors’ resolutions. Then, these reduced spectra were compared using separability indexes (Divergence, Transformed divergence, Bhattacharyya, Jeffreys-Matusita) and machine learning tools. In the experiments, we highlighted that the spectral bands configuration could lead to important differences in classification accuracy according to the context of application (e.g. urban area)

Crowdsourcing of Histological Image Labeling and Object Delineation by Medical Students

Crowdsourcing in pathology has been performed on tasks that are assumed to be manageable by nonexperts. Demand remains high for annotations of more complex elements in digital microscopic images, such as anatomical structures. Therefore, this work investigates conditions to enable crowdsourced annotations of high-level image objects, a complex task considered to require expert knowledge. 76 medical students without specific domain knowledge who voluntarily participated in three experiments solved two relevant annotation tasks on histopathological images: (1) Labeling of images showing tissue regions, and (2) delineation of morphologically defined image objects. We focus on methods to ensure sufficient annotation quality including several tests on the required number of participants and on the correlation of participants' performance between tasks. In a set up simulating annotation of images with limited ground truth, we validated the feasibility of a confidence score using full ground truth. For this, we computed a majority vote using weighting factors based on individual assessment of contributors against scattered gold standard annotated by pathologists. In conclusion, we provide guidance for task design and quality control to enable a crowdsourced approach to obtain accurate annotations required in the era of digital pathology

Learning fuzzy rules to characterize objects of interest from remote sensing images

In this article a new method for learning concepts from examples of objects provided by experts for remote sensing images is presented. The goal of this method is to give the geographer expert a description of complex objects of interest extracted from very high resolution remote sensing images. The description of such objects needs to handle imprecision inherent to segmentation and very high resolution images. The first step of this approach is to classify objects composing all the examples. This classification allows the learning of a rule describing how the examples are composed regarding the segmentation. Finally, this rule is used to extract objects corresponding to the examples. Experiments on a remote sensing image of a urban landscape in Toulouse, France are presented to show the relevance of the method

In-silico insights on the prognostic potential of immune cell infiltration patterns in the breast lobular epithelium

Scattered inflammatory cells are commonly observed in mammary gland tissue, most likely in response to normal cell turnover by proliferation and apoptosis, or as part of immunosurveillance. In contrast, lymphocytic lobulitis (LLO) is a recurrent inflammation pattern, characterized by lymphoid cells infiltrating lobular structures, that has been associated with increased familial breast cancer risk and immune responses to clinically manifest cancer. The mechanisms and pathogenic implications related to the inflammatory microenvironment in breast tissue are still poorly understood. Currently, the definition of inflammation is mainly descriptive, not allowing a clear distinction of LLO from physiological immunological responses and its role in oncogenesis remains unclear. To gain insights into the prognostic potential of inflammation, we developed an agent-based model of immune and epithelial cell interactions in breast lobular epithelium. Physiological parameters were calibrated from breast tissue samples of women who underwent reduction mammoplasty due to orthopedic or cosmetic reasons. The model allowed to investigate the impact of menstrual cycle length and hormone status on inflammatory responses to cell turnover in the breast tissue. Our findings suggested that the immunological context, defined by the immune cell density, functional orientation and spatial distribution, contains prognostic information previously not captured by conventional diagnostic approaches. Several studies provided conclusive evidence that a delicate balance between mammary epithelial cell proliferation and apoptosis regulates homeostasis in the healthy breast tissue 1-7. After menarche, and in the absence of pregnancy, the adult female mammary gland is subjected to cyclic fluctuations depending on hormonal stimulation 1,8. In response to such systemic hormonal changes, the breast epithelium undergoes a tightly regulated sequence of cell proliferation and apoptosis during each ovarian/menstrual cycle 1-3. The peak of epithelial cell proliferation has been reported to occur during the luteal phase, suggesting a synergistic influence of steroid hormones, such as estrogen and progesterone 2-5. In turn, the peak of apoptotic activity would be expected in response to decreasing hormone levels towards the end of the menstrual cycle 2-5. However, recent histologic findings indicate that apoptosis reaches its maximum levels in the middle of the luteal phase, although there is also a peak at about the third day of the menstrual cycle 6,7. Experimental measurements of cell turnover, i.e. programmed cell death and proliferation, demonstrated that an imbalance between the mitotic and apoptotic activity might lead to malignant transformation of epithelial cells and tumorigenic processes 9-11. Indeed, excessive cell proliferation promotes accumulation of DNA damage due to insufficient timely repair and mutations 12,13. There is also recent evidence that hormones suppress effective DNA repair and alter DNA damage response (DDR) 13-15

Fast segmentation for texture-based cartography of whole slide images

In recent years, new optical microscopes have been developed, providing very high spatial resolution images called Whole Slide Images (WSI). The fast and accurate display of such images for visual analysis by pathologists and the conventional automated analysis remain challenging, mainly due to the image size (sometimes billions of pixels) and the need to analyze certain image features at high resolution. To propose a decision support tool to help the pathologist interpret the information contained by the WSI, we present a new approach to establish an automatic cartography of WSI in reasonable time. The method is based on an original segmentation algorithm and on a supervised multiclass classification using a textural characterization of the regions computed by the segmentation. Application to breast cancer WSI shows promising results in terms of speed and quality

Synthesizing Whole Slide Images

The increasing availability of digital whole slide images opens new perspectives for computer-assisted image analysis complementing modern histopathology, assuming we can implement reliable and efficient image analysis algorithms to extract the biologically relevant information. Both validation and supervised learning techniques typically rely on ground truths manually made by human experts. However, this task is difficult, subjective and usually not exhaustive. This is a well-known issue in the field of biomedical imaging, and a common solution is the use of artificial “phantoms”. Following this trend, we study the feasibility of synthesizing artificial histological images to create perfect ground truths. In this paper, we show that it is possible to generate a synthetic whole slide image with reasonable computing resources, and we propose a way to evaluate its quality

Stain unmixing in brightfield multiplexed immunohistochemistry

Automated image analysis of multiplexed brightfield immunohistochemistry assays is a challenging objective. One central task of the analysis is the robust identification of the different stains in the image, called stain unmixing. Stain unmixing strongly depends on the method of image acquisition. Currently available multispectral cameras enable color unmixing of single fields of view (FoV), selected by matter experts (e.g. pathologists). Beyond the individual FoV approach, there is an increasing need to process larger regions or whole histopathological sections (whole slide imaging; WSI). Rapid color deconvolution in WSI is a challenge that is only partially solved. We propose a method based on a multilayer perceptron to compute dye-specific stain layers for chromogenic red and brown labeling in WSI

Duplication of 7q34 is specific to juvenile pilocytic astrocytomas and a hallmark of cerebellar and optic pathway tumours

BACKGROUND: Juvenile pilocytic astrocytomas (JPA), a subgroup of low-grade astrocytomas (LGA), are common, heterogeneous and poorly understood subset of brain tumours in children. Chromosomal 7q34 duplication leading to fusion genes formed between KIAA1549 and BRAF and subsequent constitutive activation of BRAF was recently identified in a proportion of LGA, and may be involved in their pathogenesis. Our aim was to investigate additional chromosomal unbalances in LGA and whether incidence of 7q34 duplication is associated with tumour type or location. METHODS AND RESULTS: Using Illumina-Human-Hap300-Duo and 610-Quad high-resolution-SNP-based arrays and quantitative PCR on genes of interest, we investigated 84 paediatric LGA. We demonstrate that 7q34 duplication is specific to sporadic JPA (35 of 53-66%) and does not occur in other LGA subtypes (0 of 27) or NFI-associated-JPA (0 of 4). We also establish that it is site specific as it occurs in the majority of cerebellar JPA (24 of 30-80%) followed by brainstem, hypothalamic/optic pathway JPA (10 of 16-62.5%) and is rare in hemispheric JPA (1 of 7-14%). The MAP-kinase pathway, assessed through ERK phosphorylation, was active in all tumours regardless of 7q34 duplication. Gain of function studies performed on hTERT-immortalised astrocytes show that overexpression of wild-type BRAF does not increase cell proliferation or baseline MAPK signalling even if it sensitises cells to EGFR stimulation. CONCLUSIONS AND INTERPRETATION: Our results suggest that variants of JPA might arise from a unique site-restricted progenitor cell where 7q34 duplication, a hallmark of this tumour-type in association to MAPK-kinase pathway activation, potentially plays a site-specific role in their pathogenesis. Importantly, gain of function abnormalities in components of MAP-Kinase signalling are potentially present in all JPA making this tumour amenable to therapeutic targeting of this pathway. British Journal of Cancer (2009) 101, 722-733. doi: 10.1038/sj.bjc.6605179 www.bjcancer.com Published online 14 July 2009 (C) 2009 Cancer Research U