Mask-R 2 CNN: a distance-field regression version of Mask-RCNN for fetal-head delineation in ultrasound images

Background and objectives: Fetal head-circumference (HC) measurement from ultrasound (US) images provides useful hints for assessing fetal growth. Such measurement is performed manually during the actual clinical practice, posing issues relevant to intra- and inter-clinician variability. This work presents a fully automatic, deep-learning-based approach to HC delineation, which we named Mask-R2CNN. It advances our previous work in the field and performs HC distance-field regression in an end-to-end fashion, without requiring a priori HC localization nor any postprocessing for outlier removal. Methods: Mask-R2CNN follows the Mask-RCNN architecture, with a backbone inspired by feature-pyramid networks, a region-proposal network and the ROI align. The Mask-RCNN segmentation head is here modified to regress the HC distance field. Results: Mask-R2CNN was tested on the HC18 Challenge dataset, which consists of 999 training and 335 testing images. With a comprehensive ablation study, we showed that Mask-R2CNN achieved a mean absolute difference of 1.95 mm (standard deviation = ± 1.92 mm), outperforming other approaches in the literature. Conclusions: With this work, we proposed an end-to-end model for HC distance-field regression. With our experimental results, we showed that Mask-R2CNN may be an effective support for clinicians for assessing fetal growth

Imaging outcome measures for progressive multiple sclerosis trials

Imaging markers that are reliable, reproducible and sensitive to neurodegenerative changes in progressive multiple sclerosis (MS) can enhance the development of new medications with a neuroprotective mode-of-action. Accordingly, in recent years, a considerable number of imaging biomarkers have been included in phase 2 and 3 clinical trials in primary and secondary progressive MS. Brain lesion count and volume are markers of inflammation and demyelination and are important outcomes even in progressive MS trials. Brain and, more recently, spinal cord atrophy are gaining relevance, considering their strong association with disability accrual; ongoing improvements in analysis methods will enhance their applicability in clinical trials, especially for cord atrophy. Advanced magnetic resonance imaging (MRI) techniques (e.g. magnetization transfer ratio (MTR), diffusion tensor imaging (DTI), spectroscopy) have been included in few trials so far and hold promise for the future, as they can reflect specific pathological changes targeted by neuroprotective treatments. Position emission tomography (PET) and optical coherence tomography have yet to be included. Applications, limitations and future perspectives of these techniques in clinical trials in progressive MS are discussed, with emphasis on measurement sensitivity, reliability and sample size calculation

Data Fusion Techniques for Processing Aerospace Remote Sensing Electro-Optical Data

This paper deals with data fusion between different resolution multispectral (MS) and panchromatic (Pan) images in order to obtain high spatial resolution MS images. A survey is provided about the state-of-the-art data fusion techniques and synthesized product's quality assessment criteria. Several fusion algorithms and quality indexes were implemented in a Toolbox with a graphical user interface developed in MATLAB environment, namely Fusion Tool Box (FTB), developed to obtain experimental results. The analysis performed through FTB on two different data sets was oriented to validate the theoretical analysis and to perform a quantitative comparison among fusion algorithms for several applications. Results allow a first level evaluation of advantages and drawbacks of the various techniques for specific applications

Disability through COVID-19 pandemic: neurorehabilitation cannot wait.

Coronavirus disease 2019 (CoViD-19) pandemic is strongly impacting all domains of our healthcare systems, including rehabilitation. In Italy, the first hit European country, medical activities were postponed to allow shifting of staff and facilities to intensive care, with neurorehabilitation limited to time-dependent diseases, <sup>1</sup> including CoViD-19 complications. <sup>2,3</sup> Hospital access to people with chronic neurodegenerative conditions such as multiple sclerosis, movement disorders or dementia, more at risks of serious consequences from the infection, <sup>4</sup> has been postponed. Patients also seek less for hospital care, with over 50% reduced stroke admissions as from an Italian survey, <sup>5</sup> possibly in fear of being infected or denied to see their families after being hospitalized. This situation can be bearable only for a short time, as any initial freezing reaction to a danger

Small-scale distribution of metazoan meiofauna and sedimentary organic matter in subtidal sandy sediments (Mediterranean Sea)

While variations in sedimentary organic matter (OM) quantity, biochemical composition and nutritional quality as well as in meiofaunal abundance and assemblage composition at the macro- and mesoscale are relatively well known, information about variations at the microscale is much scarcer. To shed some light on this issue, we tested the null hypothesis by which abundance and composition of the meiofaunal assemblages, and the quantity, biochemical composition and nutritional quality of sedimentary organic matter in coastal shallow environments do not vary within a frame of 1 m2. No significant variation within the frame emerged for OM quantity, nutritional quality, biochemical composition and the abundance of meiofaunal assemblages. On the other hand, the composition of meiofaunal assemblages varied significantly within the frame and exhibited a clear segregation of assemblages farther to the shore, as a likely result of local micro-hydrodynamic conditions. Spatial autocorrelation analysis revealed that lipid and protein sedimentary contents had a random distribution, whereas carbohydrate and biopolymeric C contents and meiofaunal total abundance were characterized by a patchy distribution, with discrete peaks within the sub-frame squares (ca. 0.1 m2). Phytopigments showed a spatial positive autocorrelation distribution, following the micro-hydrodynamic pattern, with patches larger than the sub-frame square, but smaller than the entire one (1 m2). Overall, our results suggest that, within 1 m2 of subtidal sandy sediments, three replicates could be sufficient to assess correctly OM attributes and the abundance of meiofauna, but could be possibly inadequate for assessing meiofaunal assemblages’ composition at a finer scale (<1 m2)

A regression framework to head-circumference delineation from US fetal images

Background and Objectives: Measuring head-circumference (HC) length from ultrasound (US) images is a crucial clinical task to assess fetus growth. To lower intra- and inter-operator variability in HC length measuring, several computer-assisted solutions have been proposed in the years. Recently, a large number of deep-learning approaches is addressing the problem of HC delineation through the segmentation of the whole fetal head via convolutional neural networks (CNNs). Since the task is a edge-delineation problem, we propose a different strategy based on regression CNNs. Methods: The proposed framework consists of a region-proposal CNN for head localization and centering, and a regression CNN for accurately delineate the HC. The first CNN is trained exploiting transfer learning, while we propose a training strategy for the regression CNN based on distance fields. Results: The framework was tested on the HC18 Challenge dataset, which consists of 999 training and 335 testing images. A mean absolute difference of 1.90 ( ± 1.76) mm and a Dice similarity coefficient of 97.75 ( ± 1.32) % were achieved, overcoming approaches in the literature. Conclusions: The experimental results showed the effectiveness of the proposed framework, proving its potential in supporting clinicians during the clinical practice

Learning-based screening of endothelial dysfunction from photoplethysmographic signals

Endothelial-Dysfunction (ED) screening is of primary importance to early diagnosis cardiovascular diseases. Recently, approaches to ED screening are focusing more and more on photoplethysmography (PPG)-signal analysis, which is performed in a threshold-sensitive way and may not be suitable for tackling the high variability of PPG signals. The goal of this work was to present an innovative machine-learning (ML) approach to ED screening that could tackle such variability. Two research hypotheses guided this work: (H1) ML can support ED screening by classifying PPG features; and (H2) classification performance can be improved when including also anthropometric features. To investigate H1 and H2, a new dataset was built from 59 subject. The dataset is balanced in terms of subjects with and without ED. Support vector machine (SVM), random forest (RF) and k-nearest neighbors (KNN) classifiers were investigated for feature classification. With the leave-one-out evaluation protocol, the best classification results for H1 were obtained with SVM (accuracy = 71%, recall = 59%). When testing H2, the recall was further improved to 67%. Such results are a promising step for developing a novel and intelligent PPG device to assist clinicians in performing large scale and low cost ED screening