Micron-scale Resolution Optical Tomography of Entire Mouse Brains with Confocal Light Sheet Microscopy

Understanding the architecture of mammalian  brain at single-cell resolution is one of the key issues of neuroscience.  However, mapping neuronal soma and ...

University Campus Bio-medico of Rome

The recommended method for antinuclear antibodies (ANA) detection is indirect immunofluorescence (IIF). To pursue a high image quality without artefacts and reduce interobserver variability, this study aims at evaluating the reliability of automatically acquired digital images of IIF slides for diagnostic purposes.

Indirect immunofluorescence in autoimmune diseases: assessment of digital images for diagnostic purpose.

Indirect immunofluorescence is currently the recommended method for the detection of antinuclear autoantibodies (ANA). The diagnosis consists of both estimating the fluorescence intensity and reporting the staining pattern for positive wells only. Since resources and adequately trained personnel are not always available for these tasks, an evident medical demand is the development of computer-aided diagnosis (CAD) tools that can support the physician decisions. In this paper, we present a system that classifies the staining pattern of positive wells on the strength of the recognition of their cells. The core of the CAD is a multiple expert system (MES) based on the one-per-class approach devised to label the pattern of single cells. It employs a hybrid approach since each composing binary module is constituted by an ensemble of classifiers combined by a fusion rule. Each expert uses a set of stable and effective features selected from a wide pool of statistical and spectral measurements. In this framework, we present a novel parameter that measures the reliability of the final classification provided by the MES. This feature is used to introduce a reject option that allows to reduce the error rate in the recognition of the staining pattern of the whole well. The approach has been evaluated on 37 wells, for a total of 573 cells. The measured performance shows a low overall error rate ( 2.7%-5.8%), which is below the observed intralaboratory variability.

Aggregation of classifiers for staining pattern recognition in antinuclear autoantibodies analysis.

Recent research has successfully introduced the application of robotics and mechatronics to functional assessment and motor therapy. Measurements of movement initiation in isometric conditions are widely used in clinical rehabilitation and their importance in functional assessment has been demonstrated for specific parts of the human body. The determination of the voluntary movement initiation time, also referred to as onset time, represents a challenging issue since the time window characterizing the movement onset is of particular relevance for the understanding of recovery mechanisms after a neurological damage. Establishing it manually as well as a troublesome task may also introduce oversight errors and loss of information.

Human movement onset detection from isometric force and torque measurements: a supervised pattern recognition approach.

Systemic lupus erythematosus is a connective tissue disease affecting multiple organ systems and characterised by a chronic inflammatory process. It is considered a very serious sickness, further to be classified as an invalidating chronic disease. The recommended method for its detection is the indirect immunofluorescence (IIF) based on Crithidia Luciliae (CL) substrate. Hoverer, IIF is affected by several issues limiting tests reliability and reproducibility. Hence, an evident medical demand is the development of computer-aided diagnosis tools that can offer a support to physician decision.

A decision support system for Crithidia luciliae image classification.

The recommended method for antinuclear antibodies (ANA) detection is IIF but it is influenced by many different factors. In order to pursue a high image quality without artefacts and to reduce inter-observer variability, this study aims to evaluate the reliability of using automatically acquired digital images for diagnostic purposes. In this paper we present SLIM-system a comprehensive system that supports the two sides of IIF tests classification. It is based on two systems: the first labels the fluorescence intensity, whereas the second recognizes the staining pattern of positive wells. We populated a dataset of 600 images obtained from sera screened for ANA by IIF on Hep-2 cells. The error rate has been evaluated according to eight-fold cross validation method; the rates reported in the following are the mean of the tests. Performance of the system in positive/negative recognition ranges from 87% up to more than 94%. Staining pattern classification accuracy of main classes ranges from 71% to 74%. The system provides high and reliable identification of negative samples and a flexibility that permits to use this application for different purposes. The analysis of its perspective performance shows the system potential in lowering the method variability, in increasing the level of standardization and in reducing the specialist workload of more than 80%. Our data represent a first step to validate the use of Computer Aided Diagnosis (CAD), thus offering an opportunity for standardizing and automatizing the detection of ANA by IIF.

Novel opportunities in automated classification of antinuclear antibodies on HEp-2 cells.

Further advances in modern microscopy are leading to teravoxel-sized tiled 3D images at high resolution, thus increasing the dimension of the stitching problem of at least two orders of magnitude. The existing software solutions do not seem adequate to address the additional requirements arising from these datasets, such as the minimization of memory usage and the need to process just a small portion of data.

TeraStitcher - a tool for fast automatic 3D-stitching of teravoxel-sized microscopy images.

Autoimmune diseases are very serious and also invalidating illnesses. The benchmark procedure for their diagnosis is the indirect immunofluorescence (IIF) assay performed on the HEp-2 substrate. Medical doctors first determine the fluorescence intensity exhibited by HEp-2 wells and then report the staining pattern. Despite its pivotal role, IIF is affected by inter- and intra-laboratory variabilities demanding for the development of computer-aided-diagnosis tools supporting medical doctor decisions. With reference to staining pattern recognition, state-of-the-art approaches recognize five main patterns characterized by well-defined cell edges. These approaches are based on cell segmentation, a task that recent work suggests to be harder than the classification itself. In this paper, we extend the panel of detectable HEp-2 staining patterns, introducing the recognition of centromere and cytoplasmic patterns, which have a high specific match with certain autoimmune diseases, from other stainings. Since image segmentation algorithms fail on these samples, we developed a classification system integrating local descriptors and the bag of visual word approach, which represents image contents without the burden of segmentation. We tested our approach on a large dataset of HEp-2 images with high variability in both fluorescence intensity and staining patterns correctly recognizing the 97.12Â % of samples. The system has also been validated in a daily routine fashion on 108 consecutive IIF analyses of hospital outpatients and inpatients, achieving an accuracy rate of 97.22Â %.

Giulio Iannello

Integrated Research Centre

University Campus Bio-medico of Rome

Micron-scale Resolution Optical Tomography of Entire Mouse Brains with Confocal Light Sheet Microscopy

Giulio Iannello

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Integrated Research Centre

University Campus Bio-medico of Rome

Micron-scale Resolution Optical Tomography of Entire Mouse Brains with Confocal Light Sheet Microscopy

Integrated Research Centre