Comprehensive optical and data management infrastructure for high-throughput light-sheet microscopy of whole mouse brains.

Comprehensive mapping and quantification of neuronal projections in the central nervous system requires high-throughput imaging of large volumes with microscopic resolution. To this end, we have developed a confocal light-sheet microscope that has been optimized for three-dimensional (3-D) imaging of structurally intact clarified whole-mount mouse brains. We describe the optical and electromechanical arrangement of the microscope and give details on the organization of the microscope management software. The software orchestrates all components of the microscope, coordinates critical timing and synchronization, and has been written in a versatile and modular structure using the LabVIEW language. It can easily be adapted and integrated to other microscope systems and has been made freely available to the light-sheet community. The tremendous amount of data routinely generated by light-sheet microscopy further requires novel strategies for data handling and storage. To complete the full imaging pipeline of our high-throughput microscope, we further elaborate on big data management from streaming of raw images up to stitching of 3-D datasets. The mesoscale neuroanatomy imaged at micron-scale resolution in those datasets allows characterization and quantification of neuronal projections in unsectioned mouse brains.

A versatile clearing agent for multi-modal brain imaging.

Extensive mapping of neuronal connections in the central nervous system requires high-throughput µm-scale imaging of large volumes. In recent years, different approaches have been developed to overcome the limitations due to tissue light scattering. These methods are generally developed to improve the performance of a specific imaging modality, thus limiting comprehensive neuroanatomical exploration by multi-modal optical techniques. Here, we introduce a versatile brain clearing agent (2,2'-thiodiethanol; TDE) suitable for various applications and imaging techniques. TDE is cost-efficient, water-soluble and low-viscous and, more importantly, it preserves fluorescence, is compatible with immunostaining and does not cause deformations at sub-cellular level. We demonstrate the effectiveness of this method in different applications: in fixed samples by imaging a whole mouse hippocampus with serial two-photon tomography; in combination with CLARITY by reconstructing an entire mouse brain with light sheet microscopy and in translational research by imaging immunostained human dysplastic brain tissue.

A multi-environment dataset for activity of daily living recognition in video streams.

Public datasets played a key role in the increasing level of interest that vision-based human action recognition has attracted in last years. While the production of such datasets has been influenced by the variability introduced by various actors performing the actions, the different modalities of interactions with the environment introduced by the variation of the scenes around the actors has been scarcely took into account. As a consequence, public datasets do not provide a proper test-bed for recognition algorithms that aim at achieving high accuracy, irrespective of the environment where actions are performed. This is all the more so, when systems are designed to recognize activities of daily living (ADL), which are characterized by a high level of human-environment interaction. For that reason, we present in this manuscript the MEA dataset, a new multi-environment ADL dataset, which permitted us to show how the change of scenario can affect the performances of state-of-the-art approaches for action recognition.

The classification of Crithidia luciliae immunofluorescence test (CLIFT) using a novel automated system.

INTRODUCTION: In recent years, there has been an increased demand for computer-aided diagnosis (CAD) tools to support clinicians in the field of indirect immunofluorescence. To this aim, academic and industrial research is focusing on detecting antinuclear, anti-neutrophil, and anti-double-stranded (anti-dsDNA) antibodies. Within this framework, we present a CAD system for automatic analysis of dsDNA antibody images using a multi-step classification approach. The final classification of a well is based on the classification of all its images, and each image is classified on the basis of the labeling of its cells. METHODS: We populated a database of 342 images--74 positive (21.6%) and 268 negative (78.4%)-- belonging to 63 consecutive sera: 15 positive (23.8%) and 48 negative (76.2%). We assessed system performance by using k-fold cross-validation. Furthermore, we successfully validated the recognition system on 83 consecutive sera, collected by using different equipment in a referral center, counting 279 images: 92 positive (33.0%) and 187 negative (67.0%). RESULTS: With respect to well classification, the system correctly classified 98.4% of wells (62 out of 63). Integrating information from multiple images of the same wells recovers the possible misclassifications that occurred at the previous steps (cell and image classification). This system, validated in a clinical routine fashion, provides recognition accuracy equal to 100%. CONCLUSION: The data obtained show that automation is a viable alternative for Crithidia luciliae immunofluorescence test analysis.

Centromere and cytoplasmic staining pattern recognition: a local approach.

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 97.12 % 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 97.22 %.

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

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.

A decision support system for Crithidia luciliae image classification.

OBJECTIVE: 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. METHODS: In this paper we propose a system that classifies CL wells integrating information extracted from different images. It is based on three main decision phases. Two steps, named as threshold-based classification and single cells recognition, are applied for image classification. They minimise false negative and false positive classifications, respectively. Feature extraction and selection have been carried out to determine a compact set of descriptors to distinguish between positive and negative cells. The third step applies majority voting rule at well recognition level, enabling us to recover possible errors provided by previous phases. RESULTS: The system performance have been evaluated on an annotated database of IIF CL wells, composed of 63 wells for a total of 342 images and 1487 cells. Accuracy, sensitivity and specificity of image recognition step are 99.4%, 98.6% and 99.6%, respectively. At level of well recognition, accuracy, sensitivity and specificity are 98.4%, 93.3% and 100.0%, respectively. The system has been also validated in a daily routine fashion on 48 consecutive analyses of hospital outpatients and inpatients. The results show very good performance for well recognition (100% of accuracy, sensitivity and specificity), due to the integration of cells and images information. CONCLUSIONS: The described recognition system can be applied in daily routine in order to improve the reliability, standardisation and reproducibility of CL readings in IIF.