Automated Segmentation of Cells with IHC Membrane Staining

This study presents a fully automated membrane segmentation technique for immunohistochemical tissue images with membrane staining, which is a critical task in computerized immunohistochemistry (IHC). Membrane segmentation is particularly tricky in immunohistochemical tissue images because the cellular membranes are visible only in the stained tracts of the cell, while the unstained tracts are not visible. Our automated method provides accurate segmentation of the cellular membranes in the stained tracts and reconstructs the approximate location of the unstained tracts using nuclear membranes as a spatial reference. Accurate cell-by-cell membrane segmentation allows per cell morphological analysis and quantification of the target membrane proteins that is fundamental in several medical applications such as cancer characterization and classification, personalized therapy design, and for any other applications requiring cell morphology characterization. Experimental results on real datasets from different anatomical locations demonstrate the wide applicability and high accuracy of our approach in the context of IHC analysi

Realistic Multi-Scale Modelling of Household Electricity Behaviours

To improve the management and reliability of power distribution networks, there is a strong demand for models simulating energy loads in a realistic way. In this paper, we present a novel multi-scale model to generate realistic residential load profiles at different spatial-temporal resolutions. By taking advantage of information from Census and national surveys, we generate statistically consistent populations of heterogeneous families with their respective appliances. Exploiting a Bottom-up approach based on Monte Carlo Non Homogeneous Semi-Markov, we provide household end-user behaviours and realistic households load profiles on a daily as well as on a weekly basis, for either weekdays and weekends. The proposed approach overcomes limitations of state-of-art solutions that do not consider neither the time-dependency of the probability of performing specific activities in a house, nor their duration, or are limited in the type of probability distributions they can model. On top of that, it provides outcomes that are not limited on a per-day basis. The range of available space and time resolutions span from single household to district and from second to year, respectively, featuring multi-level aggregation of the simulation outcomes. To demonstrate the accuracy of our model, we present experimental results obtained simulating realistic populations in a period covering a whole calendar year and analyse our model’s outcome at different scales. Then, we compare such results with three different data-sets that provide real load consumption at household, national and European levels, respectively

Applying Textural Features to the Classification of HEp-2 Cell Patterns in IIF images

The analysis of anti-nuclear antibodies in HEp-2 cells by indirect immunofluorescence (IIF) is fundamental for the diagnosis of important immune pathologies; in particular, classifying the staining pattern of the cell is critical for the differential diagnosis of several types of diseases. Current tests based on human evaluation are time-consuming and suffer from very high variability, which impacts on the reliability of the results. As a solution to this problem, in this work we propose a technique that performs automated classification of the staining pattern. Our method combines textural feature extraction and a two-step feature selection scheme to select a limited number of image attributes that are best suited to the classification purpose and then recognizes the staining pattern by means of a Support Vector Machine module. Experiments on IIF images showed that our method is able to identify staining patterns with average accuracy of about 87%

Classification of HEp-2 staining patterns in ImmunoFluorescence images. Comparison of Support Vector Machines and Subclass Discriminant Analysis strategies

nti-nuclear antibodies test is based on the visual evaluation of the intensity and staining pattern in HEp-2 cell slides by means of indirect immunofluorescence (IIF) imaging, revealing the presence of autoantibodies responsible for important immune pathologies. In particular, the categorization of the staining pattern is crucial for differential diagnosis, because it provides information about autoantibodies type. Their manual classification is very time-consuming and not very reliable, since it depends on the subjectivity and on the experience of the specialist. This motivates the growing demand for computer-aided solutions able to perform staining pattern classification in a fully automated way. In this work we compare two classification techniques, based respectively on Support Vector Machines and Subclass Discriminant Analysis. A set of textural features characterizing the available samples are first extracted. Then, a feature selection scheme is applied in order to produce different datasets, containing a limited number of image attributes that are best suited to the classification purpose. Experiments on IIF images showed that our computer-aided method is able to identify staining patterns with an average accuracy of about 91% and demonstrate, in this specific problem, a better performance of Subclass Discriminant Analysis with respect to Support Vector Machine

Epithelial thymic tumours in paediatric age: a report from the TREP project

<p>Abstract</p> <p>Background</p> <p>Thymic epithelial tumours (thymoma and carcinoma) are exceptionally rare in children. We describe a national multicentre series with a view to illustrating their clinical behaviour and the results of treatment.</p> <p>Methods</p> <p>From January 2000 all patients under 18 years of age diagnosed with "<it>rare paediatric tumours</it>" were centrally registered by the Italian centres participating in the TREP project (<b>T</b>umori <b>R</b>ari in <b>E</b>tà <b>P</b>ediatrica [Rare Tumours in Paediatric Age]). The clinical data of children with a thymic epithelial tumour registered as at December 2009 were analyzed for the purposes of the present study.</p> <p>Results</p> <p>Our series comprised 4 patients with thymoma and 5 with carcinoma (4 males, 5 females; median age 12.4 years). The tumour masses were mainly large, exceeding 5 cm in largest diameter. Based on the Masaoka staging system, 3 patients were stage I, 1 was stage III, 1 was stage IVa and 4 were stage IVb.</p> <p>All 3 patients with stage I thymoma underwent complete tumour resection at diagnosis and were alive 22, 35 and 93 months after surgery. One patient with a thymoma metastasizing to the kidneys died rapidly due to respiratory failure.</p> <p>Thymic carcinomas were much more aggressive, infiltrating nearby organs (in 4 cases) and regional nodes (in 5), and spreading to the bone (in 3) and liver (in 1). All patients received multidrug chemotherapy (platinum derivatives + etoposide or other drugs) with evidence of tumour reduction in 3 cases. Two patients underwent partial tumour resection (after chemo-radiotherapy in one case) and 4 patients were given radiotherapy (45-54 Gy). All patients died of their disease.</p> <p>Conclusions</p> <p>Children with thymomas completely resected at diagnosis have an excellent prognosis while thymic carcinomas behave aggressively and carry a poor prognosis despite multimodal treatment.</p

A Non-Linear Autoregressive Model for Indoor Air-Temperature Predictions in Smart Buildings

In recent years, the contrast against energy waste and pollution has become mandatory and widely endorsed. Among the many actors at stake, the building sector energy management is one of the most critical. Indeed, buildings are responsible for 40% of total energy consumption only in Europe, affecting more than a third of the total pollution produced. Therefore, energy control policies of buildings (for example, forecast-based policies such as Demand Response and Demand Side Management) play a decisive role in reducing energy waste. On these premises, this paper presents an innovative methodology based on Internet-of-Things (IoT) technology for smart building indoor air-temperature forecasting. In detail, our methodology exploits a specialized Non-linear Autoregressive neural network for short- and medium-term predictions, envisioning two different exploitation: (i) on realistic artificial data and (ii) on real data collected by IoT devices deployed in the building. For this purpose, we designed and optimized four neural models, focusing respectively on three characterizing rooms and on the whole building. Experimental results on both a simulated and a real sensors dataset demonstrate the prediction accuracy and robustness of our proposed models

Subclass Discriminant Analysis of Morphological and Textural Features for HEp-2 Staining Pattern Classification

Classifying HEp-2 fluorescence patterns in Indirect Immunofluorescence (IIF) HEp-2 cell imaging is important for the differential diagnosis of autoimmune diseases. The current technique, based on human visual inspection, is time-consuming, subjective and dependent on the operator's experience. Automating this process may be a solution to these limitations, making IIF faster and more reliable. This work proposes a classification approach based on Subclass Discriminant Analysis (SDA), a dimensionality reduction technique that provides an effective representation of the cells in the feature space, suitably coping with the high within-class variance typical of HEp-2 cell patterns. In order to generate an adequate characterization of the fluorescence patterns, we investigate the individual and combined contributions of several image attributes, showing that the integration of morphological, global and local textural features is the most suited for this purpose. The proposed approach provides an accuracy of the staining pattern classification of about 90%

Type III pleuropulmonary blastoma in a 7-month-old female baby with impending respiratory failure: a case report

INTRODUCTION: Pleuropulmonary blastoma is a very rare, aggressive, embryonal pulmonary neoplasm which mostly affects children under the age of 5. According to the histopathological features, three subtypes of pleuropulmonary blastoma have been recognized: type I (purely cystic), type II (grossly visible cystic and solid elements) and type III (purely solid). Characteristics of type I and type II blastoma allow an earlier diagnosis compared with type III. Here we present a case report of an unusual presentation of type III pleuropulmonary blastoma. CASE PRESENTATION: We describe the case of a 7-month-old female baby of Italian mother and Kurdish father who was diagnosed with type III pleuropulmonary blastoma, which entirely occupied her right hemithorax. CONCLUSIONS: The reported case is an unusual presentation because type III pleuropulmonary blastoma typically occurs in older children. The complete re-expansion of her residual, previously totally compressed, right lung observed immediately after the resection of the lesion suggests an atypical rapid growth of this embryonal tumor in the late phase of gestation or after delivery. This case report suggests that, in addition to other childhood tumors, type III pleuropulmonary blastoma should be included in the differential diagnosis of solid nonhomogeneous thoracic large masses, compressing the mediastinal and chest wall structures in infants. This is an original case report of interest for several specialities such us pediatrics, radiology, surgery and oncology