BACKGROUND: Biological phenomena usually evolves over time and recent advances in high-throughput microscopy have made possible to collect multiple 3D images over time, generating [Formula: see text] (or 4D) datasets. To extract useful information there is the need to extract spatial and temporal data on the particles that are in the images, but particle tracking and feature extraction need some kind of assistance. RESULTS: This manuscript introduces our new freely downloadable toolbox, the Visual4DTracker. It is a MATLAB package implementing several useful functionalities to navigate, analyse and proof-read the track of each particle detected in any [Formula: see text] stack. Furthermore, it allows users to proof-read and to evaluate the traces with respect to a given gold standard. The Visual4DTracker toolbox permits the users to visualize and save all the generated results through a user-friendly graphical user interface. This tool has been successfully used in three applicative examples. The first processes synthetic data to show all the software functionalities. The second shows how to process a 4D image stack showing the time-lapse growth of Drosophila cells in an embryo. The third example presents the quantitative analysis of insulin granules in living beta-cells, showing that such particles have two main dynamics that coexist inside the cells. CONCLUSIONS: Visual4DTracker is a software package for MATLAB to visualize, handle and manually track [Formula: see text] stacks of microscopy images containing objects such cells, granules, etc.. With its unique set of functions, it remarkably permits the user to analyze and proof-read 4D data in a friendly 3D fashion. The tool is freely available at https://drive.google.com/drive/folders/19AEn0TqP-2B8Z10kOavEAopTUxsKUV73?usp=sharing.

Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether artificial intelligence working with chest X-ray (CXR) scans and clinical data can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. Indeed, further to induce lower radiation dose than computed tomography (CT), CXR is a simpler and faster radiological technique, being also more widespread. In this respect, we present three approaches that use features extracted from CXR images, either handcrafted or automatically learnt by convolutional neuronal networks, which are then integrated with the clinical data. As a further contribution, this work introduces a repository that collects data from 820 patients enrolled in six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, suggesting that clinical data and images have the potential to provide useful information for the management of patients and hospital resources.

The rising capabilities of storing and registering data has increased the number of temporal datasets, boosting the attention on time series classification and forecasting. In case of multivariate time series, symbolic methods that try to predict phenomena transform the data into a more compact format to produce a representation of the time series easy to be handled in a machine learning framework. However, up to now these representations do not grasp information on both inter-attribute variability and temporal variability. In this work we present an approach that, taking into account the relationships between attributes and their periodicity, reduces the multivariate time series to a collection of symbols, whose distribution is represented by histograms. The approach has been successfully tested on a publicly available dataset, the Telecom Italia Big Data Challenge 2014 dataset, reporting also the results attained by other methods available in the literature.

Breast cancer is the most common tumour in women and it is characterized by a huge variety of clinical and histological scenarios and imaging pattern. The axillary lymph node metastases presence or absence is one of the most important prognostic factors affecting the loco-regional recurrence and the overall survival. The lymph node status is usually determined by an histological exam, an invasive procedure that could result in complications. This work aims to provide a safer and non-invasive prognostic approach by introducing a radiomics-based method that predicts axillary lymph node metastasis. It combines primary tumor histological features and patients clinical data with quantitative measures extracted from the MR images. To compute these latter quantities we determine the convex hull of the ROIs and we introduce the Three Orthogonal Planes-Local Binary Pattern (TOP-LBP). On 99 samples the approach achieves a promising AUC equal to 85.6%.

Prostate cancer is the most common form of cancer in Western countries and there is the need to develop clinical decision support systems able to support physicians in the diagnosis of clinical relevant prostate cancer and avoid useless invasive prostate biopsies. In this respect, this paper introduces a radiomic approach that classifies the prostate cancer aggressiveness by combining Three Orthogonal Planes-Local Binary Pattern (TOP - LBP) with other texture measures. Furthermore, to combat the skewed nature of class priors, our proposal employs a data augmentation technique. The results achieved on 99 samples are up-and-coming, they favorably compare against conventional PI-RADS-based approach, and they show also the benefit given by the introduction of TOP-LBP in the radiomic signature.

The possibility of planning a therapy minimizing side effects and optimizing efficacy of cancer treatments is one of the main challenges tackled by precision medicine research in oncology. In this context, radiomics is revealing itself as a promising path for better understanding the correct approach to personalized cures. Its primary aim is to go beyond basic medical images analysis, which only leverages on direct measurements on the tumor mass, i.e. dimension and shape. On the contrary, radiomics approach is oriented to the extraction of heterogeneous and quantitative data from the images to characterize the disease from a wider perspective, in order to provide the physician a valid support for the therapy decision and survival prediction. This manuscript presents an application of radiomics to Non-Small Cell Lung Cancer, dealing with the novel task of predicting the possibility to carry out an adaptive therapy. We achieved promising performance, reporting a radiomics signature for predicting tumor reduction during therapy.

Neuroscience has been interested in cellular neuroanatomy since the time of Ramón y Cajal. Although the manual reconstruction of neuron morphologies is still widely used, the recent availability of large image data asks for automatic or semi-automatic tools. In this paper we present an automatic method to trace neurites in 3D volumes based on two main steps. The first detects neurites connected with a given seed that satisfy a conservative membership rule, the second detects weak neurite chunks allowing a local growth of the arbor on the basis of local intensity features. The local step also accommodates to the nonuniform illumination and to the noise of the sample. We tested our proposal on the Olfactory Projection dataset belonging to the well-known DIADEM challenge, comparing its performance against those achieved by other state-of-the-art methods available within the BigNeuron Project.

Chronic obstructive pulmonary disease (COPD) is a slowly progressive disease characterized by airway obstruction. Patients suffering from COPD could report exacerbations, which are deteriorations in respiratory health that worsen the course of the disease. The prompt recognition of an exacerbation from daily variations and its early treatment reduces the healing time and the risk of hospitalization. Using a pulse oximeter connected to a mobile phone, we remotely collect the values of heart rate and of the oxygen saturation on a cohort of seven elderly patients affected by severe COPD. We analyze if a score given by the weighted composition of these signals permits to detect the worrisome events prefiguring an exacerbation onset. A cross validation-based evaluation allows us to assess how the results generalize to an independent data set. We found that the tested score does not provide satisfactory results in terms of sensitivity and specificity, suggesting that it is not able to disambiguate between exacerbation onset and other COPD related events.

MOTIVATION: Recently, confocal light sheet microscopy has enabled high-throughput acquisition of whole mouse brain 3D images at the micron scale resolution. This poses the unprecedented challenge of creating accurate digital maps of the whole set of cells in a brain. RESULTS: We introduce a fast and scalable algorithm for fully automated cell identification. We obtained the whole digital map of Purkinje cells in mouse cerebellum consisting of a set of 3D cell center coordinates. The method is accurate and we estimated an F1 measure of 0.96 using 56 representative volumes, totaling 1.09 GVoxel and containing 4138 manually annotated soma centers. AVAILABILITY AND IMPLEMENTATION: Source code and its documentation are available at http://bcfind.dinfo.unifi.it/. The whole pipeline of methods is implemented in Python and makes use of Pylearn2 and modified parts of Scikit-learn. Brain images are available on request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Human action recognition is an active research area with applications in several domains such as visual surveillance, video retrieval and human–computer interaction. Current approaches assign action labels to video streams considering the whole video as a single sequence but, in some cases, the large variability between frames may lead to misclassifications. The authors propose a multiple subsequence combination (MSC) method that divides the video into several consecutive subsequences. It applies part‐based and bag of visual words approaches to classify each subsequence. Then, it combines subsequence labels to assign an action label to the video. The proposed approach was tested on the KTH, UCF sports, Youtube and Robo‐Kitchen datasets, which have large differences in terms of video length, object appearance and pose, object scale, viewpoint, background, as well as number, type and complexity of actions performed. Two main results were achieved. First, the MSC approach shows better performances compared to classify the video as a whole, even when few subsequences are used. Second, the approach is robust and stable since, for each dataset, its performances are comparable to the part‐based approach at the state‐of‐the‐art.

The analysis of EEG evoked potentials strongly relies on the correct alignment of different segments of the recorded EEG activity. The alignment of segments is needed in order to extract event related waves hidden in the background EEG activity. Commonly, the information on onsets is provided by the acquisition system. However, wrong configuration of the recording system or human errors during the acquisition or storage of data may make this information unavailable. Usually, these errors are discovered during the datasets analysis stage, and this stage can take place even several months after the acquisition of datasets. Unluckily, changes on patients status and the expensiveness of EEG registrations make unfeasible to repeat the acquisitions. In this paper we present and evaluate two mechanisms that we included in an EEGLAB plugin for the automatic reconstruction of onsets in EEG-TMS recordings. The methods of the TMS Triggers Reconstruction Software (TRS-TMS) plugin are discussed and evaluated obtaining guidelines for their correct configuration in the routine usage.

Several medical and biological applications face with multiclass recognition problems. Such polychotomies can be addressed by decomposition techniques, which reduce the polychotomy into a series of dichotomies and then provide the final multiclass label using a reconstruction rule. Within this framework, we present a reconstruction rule based on softmax regression, where the features of the new classification task are the crisp labels and the reliabilities of dichotomizers' classifications. The approach has been tested on six medical and biological datasets, decomposing the polychotomies via the Error-Correcting Output Code. Its performances favorably compare with those provided by other two well-known reconstruction rules both in terms of global accuracy and accuracy per class.

This paper proposes a new method for removing noise and blurring from 3D microscopy images. The main contribution is the definition of a space-variant generating model of a 3-D signal, which is capable to stochastically describe a wide class of 3-D images. Unlike other approaches, the space-variant structure allows the model to consider the information on edge locations, if available. A suitable description of the image acquisition process, including blurring and noise, is then associated to the model. A state-space realization is finally derived, which is amenable to the application of standard Kalman filter as an image restoration algorithm. The so obtained method is able to remove, at each spatial step, both blur and noise, via a linear minimum variance recursive one-shot procedure, which does not require the simultaneous processing of the whole image. Numerical results on synthetic and real microscopy images confirm the merit of the approach.

Antinuclear autoantibodies (ANAs) are important markers to diagnose autoimmune diseases, very serious and also invalidating illnesses. The benchmark procedure for ANAs diagnosis is the indirect immunofluorescence (IIF) assay performed on the HEp-2 substrate. Medical doctors first determine the fluorescence intensity exhibited by HEp-2 cells, and then report the staining pattern for positive wells only. With reference to staining pattern recognition, in the literature we found works recognizing five main patterns characterized by well-defined cell edges. These approaches are based on cell segmentation, a task that should be harder than the classification itself. We present here a method extending the panel of detectable HEp-2 staining patterns, introducing the centromere and cytoplasmic patterns, which do not show well-defined cell edges, and where a segmentation-based classification may fail. We apply a local approach which extracts SIFT descriptors and then classifies an image through the bag of visual words approach. This permits to represent complex image contents without applying the segmentation procedure. We test our approach on a dataset of HEp-2 images with large variability in both fluorescence intensity and staining patterns. Despite the large skew of the a-priori class distribution, our system correctly recognizes the 98.3% of samples, with a F-measure equal to 92.3%, 95.2% and 99.0%, for each class.

BACKGROUND: 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. RESULTS: We propose a free and fully automated 3D Stitching tool designed to match the special requirements coming out of teravoxel-sized tiled microscopy images that is able to stitch them in a reasonable time even on workstations with limited resources. The tool was tested on teravoxel-sized whole mouse brain images with micrometer resolution and it was also compared with the state-of-the-art stitching tools on megavoxel-sized publicy available datasets. This comparison confirmed that the solutions we adopted are suited for stitching very large images and also perform well on datasets with different characteristics. Indeed, some of the algorithms embedded in other stitching tools could be easily integrated in our framework if they turned out to be more effective on other classes of images. To this purpose, we designed a software architecture which separates the strategies that use efficiently memory resources from the algorithms which may depend on the characteristics of the acquired images. CONCLUSIONS: TeraStitcher is a free tool that enables the stitching of Teravoxel-sized tiled microscopy images even on workstations with relatively limited resources of memory (<8 GB) and processing power. It exploits the knowledge of approximate tile positions and uses ad-hoc strategies and algorithms designed for such very large datasets. The produced images can be saved into a multiresolution representation to be efficiently retrieved and processed. We provide TeraStitcher both as standalone application and as plugin of the free software Vaa3D.

Photobleaching effect is a major issue in developing autofocus algorithms for indirect immunofluorescence (IIF) assay since it quickly flattens the fluorescence intensity of the samples. To overcome this limitation we propose an autofocus algorithm, suited for IIF, aiming at minimizing the number of images required to focus the sample. We test our algorithm with an heterogeneous set of IIF images containing the substrates most widely used in clinical practice, comparing popular focus functions in order to find out the most appropriate for our procedure. Experimental results prove that our algorithm focus the sample using less images than other popular algorithms proposed in the literature.

Decomposition methods are multiclass classification schemes where the polychotomy is reduced into several dichotomies. Each dichotomy is addressed by a classifier trained on a training set derived from the original one on the basis of the decomposition rule adopted. These new training sets may present a disproportion between the classes, harming the global recognition accuracy. Indeed, traditional learning algorithms are biased towards the majority class, resulting in poor predictive accuracy over the minority one. This paper investigates if the application of learning methods specifically tailored for imbalanced training set introduces any performance improvement when used by dichotomizers of decomposition methods. The results on five public datasets show that the application of these learning methods improves the global performance of decomposition schemes.

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.

The performance of a classification system is sometimes unsatisfactory for the needs of real applications. In these cases, the measure of classification reliability should be useful since it takes into account the many issues that influence the achievement of satisfactory results. The most common choice for confidence evaluation consists in using the confusion matrix estimated during the learning phase. As a consequence, the same reliability value is associated with every decision attributing a sample to the same class. In this respect, this paper proposes and compares three different reliability estimators of each classification act of classification systems that belong to the one-per-class framework. They are based on the reliabilities provided by each dichotomizer and are independent of the binary module design. Their performance have been assessed and ranked on private and public medical datasets, showing that one of the estimators outperforms the others.

Applications of robotics and mechatronics to neurorehabilitation are getting more and more consensus in the clinical community thanks to early encouraging results. They enable an objective assessment of patient's motor recovery and the administration of rehabilitation treatments specific for each patient. In particular, isometric force/torque measurements in post-stroke patients were recently used in clinical trials for the functional assessment, with encouraging results. A challenging issue in the processing of such measurements is to detect the initiation of the voluntary contraction of the patient (i.e., onset time). The onset detection is crucial to obtain clinically relevant data. In previous works, different deterministic methods for onset detection were presented. Each of those methods is signal-structure dependant, causing drop of performance when applied to different kind of signals. In this paper, we introduce an innovative technique for the automatic selection of the best onset detection method. To this aim, we adopt a supervised pattern recognition approach that dynamically selects, from a pool of deterministic methods, the one that is best suited for each signal according to the signal structure. The method has been tested on annotated force and torque datasets, showing that such a method improves not only the performance achieved by the single deterministic techniques, but also those attained by a group of clinical experts.

Performance of wireless communications systems is degraded by fading, typically combated via spatial diversity by multiple transmit and receiver antennas. Practical limitations may limit their use; thus, cooperative transmissions have been proposed to introduce diversity without the need for multiple antennas. We propose a novel cooperative scheme that performs distributed coding of user data. It needs each user transmit on one single channel and allows large flexibility between rate and diversity. A trade-off is increased complexity as more users are required to be decoded simultaneously by the base station. We present construction of the distributed code for different choices of rates and diversity orders by means of simple linear block codes, resulting in reduced complexity for decoding operations. Achieved diversity order is bounded by the minimum distance of the implemented distributed code. Performance of the scheme is studied via analytical bounds and numerical simulations.

The introduction of the IP multimedia subsystem on 3G cellular networks and the integration with other widely deployed wireless networks based on the IEEE 802.11 protocol family require support for both mobility and quality of service. When mobile systems move across heterogeneous networks, ongoing real-time sessions are affected not only by handoff delay but also by different packet delay and bit rate. In this paper, we propose a cross-layer mechanism that takes into account mobility at different layers of the network stack in order to yield better quality for VoIP, videoconferencing, and other real-time applications. We describe our cross-layer architecture, adaptation techniques, a prototype implementation, and experimental results.

Indirect immunofluorescence is the recommended method for antinuclear autoantibodies (ANA) detection. IIF diagnosis requires estimating fluorescent intensity and pattern description, but resources and adequately trained personnel are not always available for these tasks. In this respect, an evident medical demand is the development of computer aided diagnosis tools that can offer a support to physician decision. In this paper we propose a system to classify the fluorescent intensity: initially we discuss two classifiers based on artificial neural networks that can recognize intrinsically dubious samples and whose error tolerance can be flexibly set according to a given rule. Since such classifiers complement one other, we adopt a multiple expert system that aggregates the two experts. The final decision of the system results from the combination of the outputs of the single experts. Measured performance shows error rates less than 1%, which candidates the method to be used in daily medical practice

In this paper, we report our experience in the development of a system for automatic acquisition of Immuno-Fluorescence Assay (IFA) images. We focus on two basic issues. Firstly, we determine an autofocus function that can deal with photobleaching, a physical phenomenon affecting automatic acquisition of IFA images, and present a set of experiments on real images that confirm its effectiveness. Secondly, we discuss if the physicians may reliably use digital IFA images in place of direct microscope observations to carry out the diagnosis. In this respect, we present the results of a preliminary experiment where physicians perform the diagnosis on a set of images both by looking directly to them at the fluorescence microscope and by looking at digital images on the screen of a workstation.

Traffic modeling is a fertile research area. This paper proposes a packet-level traffic model of traffic sources based on hidden Markov model. It has been developed by using real network traffic and estimating in a combined fashion packet size and inter packet time. The effectiveness of the proposed model is evaluated by studying several traffic types with strong differences in terms of both applications/users and protocol behavior. Indeed, we applied our model to real traffic traces of Age of Mythology (a Multi Player Network Game), SMTP, and HTTP. An analytical basis and the mathematical details regarding the model are given. Results show how the proposed model captures first-order statistics, as well as temporal dynamics via auto- and cross-correlation. Also, the capability to accurately replicate the considered traffic sources is shown. Finally, preliminary results for model-based traffic prediction reveal encouraging.

This paper proposes a source-traffic based model to estimate jointly packet losses and delays statistical behavior of a network path. The approach relies on a hidden Markov model built on real-traffic information. The effectiveness of the model is evaluated over different real heterogeneous network scenarios. Our experimental results show that the model captures average (long-term) and conditional (short-term) statistics that in most cases are typical of the single scenario. Preliminary results about prediction on a sample path as well as investigation on the use of the same model across different scenarios are given.

Performance of real-time applications on end-to-end packet channels are strongly related to losses and temporal delays. Several studies showed that these network features may be correlated and present a certain degree of memory such as bursty losses and delays. The memory and the statistical dependence between losses and temporal delays suggest that the channel may be well modeled by a hidden Markov model with appropriate hidden variables that capture the current state of the network. In this paper we propose an input/output hidden Markov model that, trained with a modified version of the expectation-maximization algorithm, shows excellent performance in modeling typical channel behaviors in a set of real packet links. The work extends to case of variable inter-departure time the previous proposed hidden Markov model that well characterizes losses and delays of packets from a periodic source.

In this paper, we report on the experiences in designing a portable parallel file system for clusters. The file system offers to the applications an interface compliant with MPI-IO, the I/O interface of the MPI-2 standard. The file system implementation relies upon MPI for internal coordination and communication. This guarantees high performance and portability over a wide range of hardware and software cluster platforms. The internal architecture of the file system has been designed to allow rapid prototyping and experimentation of novel strategies for managing parallel I/O in a cluster environment. The discussion of the file system design and early implementation is completed with basic performance measures confirming the potential of the approach.

Single System Image is a desirable property for all services provided by a cluster environment. Coherently with this principle, cluster file systems should make data distribution transparent to the application, giving rise to a heavy communication workload induced by I/O activity. Hence, I/O performance greatly depends on communication performance and on how the storage and network subsystems interact at all levels. The authors present a detailed performance analysis of the storage and network subsystems of a properly configured cluster node. In particular, we consider the case of an Ultra2 SCSI controller with multiple attached disks sharing the I/O bus with a Gigabit LAN adapter. The analysis helps to understand how the several hardware and software components interact, which are the potential bottlenecks, and how these bottlenecks affect the overall performance.

Collective communication operations (CCOs) are one of the most powerful tools for parallel processing on distributed memory architectures. From the theoretical viewpoint there has been a major effort in the design of optimal algorithms for these operations, especially for massive parallel processors (MPPs). However, in spite of the increasing availability of MPPs, there are just a few limited experimental checks of the different theories, so the assessment of their real value is not easy. The aim of the present paper is to address such issues for the most common CCOs, considering practical algorithms that can be included in a generic communication library. The main result is a new algorithm for building a quasi-optimal broadcast tree that is much simpler than, and as efficient as, previously available algorithms. To investigate the advantages and drawbacks of the proposed algorithms, a large set of experimental data has been collected on an IBM SP2 parallel system. The data demonstrate the efficiency of our approach in a number of interesting cases. Finally, all the experimental results have been related to the model used in designing the algorithms. © 1998 John Wiley & Sons, Ltd.

We consider the problem of efficiently performing a reduce-scatter operation in a message passing system. Reduce-scatter is the composition of an element-wise reduction on vectors of n elements initially held by n processors, with a scatter of the resulting vector among the processors. In this paper, we present two algorithms for the reduce-scatter operation, designed in LogGP. The first algorithm assumes an associative and commutative reduction operator and it is optimal in LogGP within a small constant factor. The second algorithm allows the reduction operator to be noncommutative, and it is asymptotically optimal when values to be combined are large arrays. To achieve these results, we developed a complete analysis of both algorithms in LogGP, including the derivation of lower bounds for the reduce-scatter operation, and the study of the m-item version of the problem, i.e., the case when the initial elements are vectors themselves. Reduce-scatter has been included as a collective operation in the MPI standard message passing library, and can be used, for instance, in parallel matrix-vector multiply when the matrix is decomposed by columns. To model a message passing system, we adopted the LogGP model, an extension of LogP that allows the modeling of messages of different length. While this choice makes the analysis somewhat more complex, it leads to more realistic results in the case of gather/scatter algorithms.

Abstract A programming discipline to write abstract data types, iterators and generic modules using the C language is proposed. Program examples are presented making use of features of the ANSI standard for C. The discipline supports object‐oriented development by allowing the programmer to implement abstractions through modules with well‐defined interfaces. To use the abstractions, the programmer is not required to be aware of how they are implemented.