Achieving the Way for Automated Segmentation of Nuclei in Cancer Tissue Images through Morphology-Based Approach: a Quantitative Evaluation

In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification and for cancer diagnosis and therapy. We present a fully automated morphology-based technique able to perform accurate nuclear segmentations in images with heterogeneous staining and multiple tissue layers and we compare it with an alternate semi-automated method based on a well established segmentation approach, namely active contours. We discuss active contours’ limitations in the segmentation of immunohistochemical images and we demonstrate and motivate through extensive experiments the better accuracy of our fully automated approach compared to various active contours implementations

Light-sheet microscopy: a tutorial

This paper is intended to give a comprehensive review of light-sheet (LS) microscopy from an optics perspective. As such, emphasis is placed on the advantages that LS microscope configurations present, given the degree of freedom gained by uncoupling the excitation and detection arms. The new imaging properties are first highlighted in terms of optical parameters and how these have enabled several biomedical applications. Then, the basics are presented for understanding how a LS microscope works. This is followed by a presentation of a tutorial for LS microscope designs, each working at different resolutions and for different applications. Then, based on a numerical Fourier analysis and given the multiple possibilities for generating the LS in the microscope (using Gaussian, Bessel, and Airy beams in the linear and nonlinear regimes), a systematic comparison of their optical performance is presented. Finally, based on advances in optics and photonics, the novel optical implementations possible in a LS microscope are highlighted.Peer ReviewedPostprint (published version

Confidence bands for inverse regression models with application to gel electrophoresis

We construct uniform confidence bands for the regression function in inverse, homoscedastic regression models with convolution-type operators. Here, the convolution is between two non-periodic functions on the whole real line rather than between two period functions on a compact interval, since the former situation arguably arises more often in applications. First, following Bickel and Rosenblatt [Ann. Statist. 1, 10711095] we construct asymptotic confidence bands which are based on strong approximations and on a limit theorem for the supremum of a stationary Gaussian process. Further, we propose bootstrap confidence bands based on the residual bootstrap. A simulation study shows that the bootstrap confidence bands perform reasonably well for moderate sample sizes. Finally, we apply our method to data from a gel electrophoresis experiment with genetically engineered neuronal receptor subunits incubated with rat brain extract. --Confidencebands,Inverseproblems,Deconvolution,Rates of convergences,Nonparametric Regression

High-resolution in-depth imaging of optically cleared thick samples using an adaptive SPIM

Today, Light Sheet Fluorescence Microscopy (LSFM) makes it possible to image fluorescent samples through depths of several hundreds of microns. However, LSFM also suffers from scattering, absorption and optical aberrations. Spatial variations in the refractive index inside the samples cause major changes to the light path resulting in loss of signal and contrast in the deepest regions, thus impairing in-depth imaging capability. These effects are particularly marked when inhomogeneous, complex biological samples are under study. Recently, chemical treatments have been developed to render a sample transparent by homogenizing its refractive index (RI), consequently enabling a reduction of scattering phenomena and a simplification of optical aberration patterns. One drawback of these methods is that the resulting RI of cleared samples does not match the working RI medium generally used for LSFM lenses. This RI mismatch leads to the presence of low-order aberrations and therefore to a significant degradation of image quality. In this paper, we introduce an original optical-chemical combined method based on an adaptive SPIM and a water-based clearing protocol enabling compensation for aberrations arising from RI mismatches induced by optical clearing methods and acquisition of high-resolution in-depth images of optically cleared complex thick samples such as Multi-Cellular Tumour Spheroids

Scattering Correction Methods of Infrared Spectra Using Graphics Processing Units

Fourier transform infrared (FTIR) microspectroscopy has been used for many years as a technique that provides distinctive structure-specific infrared spectra for a wide range of materials (e.g., biological (tissues, cells, bacteria, viruses), polymers, energy related, composites, minerals). The mid-infrared radiation can strongly scatter from distinct particles, with diameters ranging between 2-20 micrometer. Transmission measurements of samples (approximately 100 micrometers x 100 micrometers x 10 micrometers) with distinct particles. will be dominated by this scattering (Mie scattering). The scattering distorts the measured spectra, and the absorption spectra appear different from pure absorbance spectra. This thesis presents development and implementation of two algorithms for processing of FTIR spectra and evaluating the resulting mid-FTIR images. The first procedure removes Mie scattering spectral features, and shows resulting spectra and images to confirm that scattering intensity has been minimized, and the second procedure is a spatial deconvolution algorithm which is used to improve the contrast and fidelity of the imaging data. Both the algorithms discussed in this thesis were implemented using Graphics Processing Units (GPUs) for fast hyperspectral processing by exploiting the parallelism in distributed computational environment. 30x speedup was achieved in spatial deconvolution algorithm implementation as compared with MATLAB implementation of the same problem specifications. Scattering correction implementation on GPU achieved 10x speedup for single iteration as compared with previous MATLAB implementation. Next, some tests were run on real datasets and its\u27 GPU implementation time is compared with previous implementation on CPUs. In the end some future directions and prospects are mentioned

Automated segmentation of tissue images for computerized IHC analysis

This paper presents two automated methods for the segmentation ofimmunohistochemical tissue images that overcome the limitations of themanual approach aswell as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologie