Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation

In this paper, we present an automated approach for segmenting multiple sclerosis (MS) lesions from multi-modal brain magnetic resonance images. Our method is based on a deep end-to-end 2D convolutional neural network (CNN) for slice-based segmentation of 3D volumetric data. The proposed CNN includes a multi-branch downsampling path, which enables the network to encode information from multiple modalities separately. Multi-scale feature fusion blocks are proposed to combine feature maps from different modalities at different stages of the network. Then, multi-scale feature upsampling blocks are introduced to upsize combined feature maps to leverage information from lesion shape and location. We trained and tested the proposed model using orthogonal plane orientations of each 3D modality to exploit the contextual information in all directions. The proposed pipeline is evaluated on two different datasets: a private dataset including 37 MS patients and a publicly available dataset known as the ISBI 2015 longitudinal MS lesion segmentation challenge dataset, consisting of 14 MS patients. Considering the ISBI challenge, at the time of submission, our method was amongst the top performing solutions. On the private dataset, using the same array of performance metrics as in the ISBI challenge, the proposed approach shows high improvements in MS lesion segmentation compared with other publicly available tools.Comment: This paper has been accepted for publication in NeuroImag

Aspectos sonoros e acessibilidade: debate sobre inclusão

as pesquisadoras Rela, Addimando e Rocca apresentam entrevista realizada na modalidade grupo focal com os pesquisadores Marina Santi, Michele Mainardi e Heidrun Demo na qual discutem sobre a possibilidade de todos os sujeitos observarem as paisagens sonoras como um novo modo para experenciar a escuta, de sair de uma condição normal   para experenciar uma excepcionalidade. A interação entre os participantes foi enriquecida pelas opiniões formuladas, à medida que cada participante, levando em consideração as falas dos demais, colocou novos andaimes ao tema em questão.

Best Practices in Knowledge Transfer: Insights from Top Universities

The impact of knowledge transfer induced by universities on economy, society, and culture is widely acknowledged; nevertheless, this aspect is often neglected by university rankings. Here, we considered three of the most popular global university rankings and specific knowledge transfer indicators by U-multirank, a European ranking system launched by the European Commission, in order to answer to the following research question: how do the world top universities, evaluated according to global university rankings, perform from a knowledge transfer point of view? To this aim, the top universities have been compared with the others through the calculation of a Global Performance Indicator in Knowledge Transfer (GPI KT), a hierarchical clustering, and an outlier analysis. The results show that the universities best rated by global rankings do not always perform as well from knowledge transfer point of view. By combining the obtained results, it is possible to state that only 5 universities (Berkeley, Stanford, MIT, Harvard, CALTEC), among the top in the world, exhibit a high-level performance in knowledge transfer activities. For a better understanding of the success factors and best practices in knowledge transfer, a brief description of the 5 cited universities, in terms of organization of technology transfer service, relationship with business, entrepreneurship programs, and, more generally, third mission activities, is provided. A joint reading of the results suggests that the most popular global university rankings probably fail to effectively photograph third mission activities because they can manifest in a variety of forms, due to the intrinsic and intangible nature of third mission variables, which are difficult to quantify with simple and few indicators

In vitro evaluation of structural factors favouring bacterial adhesion on orthodontic adhesive resins

Bacterial adhesion to the surface of orthodontic materials is an important step in the formation and proliferation of plaque bacteria, which is responsible for enamel demineralization and periodontium pathologies. With the intent of investigating if adhesive resins used for bracket bonding are prone to bacteria colonization, the surface roughness of these materials has been analyzed, combining information with a novel methodology to observe the internal structures of orthodontic composites. Scanning electron microscopy, combined with focus ion bean micromachining and stylus profilometry analyses, were performed to evaluate the compositional factors that can influence specific pivotal properties facilitating the adhesion of bacteria to the surface, such as surface roughness and robustness of three orthodontic adhesive composite resins. To confirm these findings, contact angle measurements and bacteria incubation on resin slide have been performed, evaluating similarities and differences in the final achievement. In particular, the morphological features that determine an increase in the resins surface wettability and influence the bacterial adhesion are the subject of speculation. Finally, the focused ion beam technique has been proposed as a valuable tool to combine information coming from surface roughness with specific the internal structures of the polymers

Predicting brain age with complex networks: From adolescence to adulthood.

In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brain scans (in the age range 7-64years). We introduce a structural connectivity model of the human brain: MRI scans are divided in rectangular boxes and Pearson's correlation is measured among them in order to obtain a complex network model. Brain connectivity is then characterized through few and easy-to-interpret centrality measures; finally, brain age is predicted by feeding a compact deep neural network. The proposed approach is accurate, robust and computationally efficient, despite the large and heterogeneous dataset used. Age prediction accuracy, in terms of correlation between predicted and actual age r=0.89and Mean Absolute Error MAE =2.19years, compares favorably with results from state-of-the-art approaches. On an independent test set including 262 subjects, whose scans were acquired with different scanners and protocols we found MAE =2.52. The only imaging analysis steps required in the proposed framework are brain extraction and linear registration, hence robust results are obtained with a low computational cost. In addition, the network model provides a novel insight on aging patterns within the brain and specific information about anatomical districts displaying relevant changes with aging