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

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)

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

Learned and handcrafted features for early-stage laryngeal SCC diagnosis

Squamous cell carcinoma (SCC) is the most common and malignant laryngeal cancer. An early-stage diagnosis is of crucial importance to lower patient mortality and preserve both the laryngeal anatomy and vocal-fold function. However, this may be challenging as the initial larynx modifications, mainly concerning the mucosa vascular tree and the epithelium texture and color, are small and can pass unnoticed to the human eye. The primary goal of this paper was to investigate a learning-based approach to early-stage SCC diagnosis, and compare the use of (i) texture-based global descriptors, such as local binary patterns, and (ii) deep-learning-based descriptors. These features, extracted from endoscopic narrow-band images of the larynx, were classified with support vector machines as to discriminate healthy, precancerous, and early-stage SCC tissues. When tested on a benchmark dataset, a median classification recall of 98% was obtained with the best feature combination, outperforming the state of the art (recall = 95%). Despite further investigation is needed (e.g., testing on a larger dataset), the achieved results support the use of the developed methodology in the actual clinical practice to provide accurate early-stage SCC diagnosis. [Figure not available: see fulltext.]

MyDi application: Towards automatic activity annotation of young patients with Type 1 diabetes

Type I diabetes mellitus (T1DM) is a widespread metabolic disorder characterized by pancreatic insufficiency. People with T1DM require: a lifelong insulin injection, to constantly monitor glycemia and to take note of their activities. This continuous follow-up, especially at a very young age, may be challenging. Adolescents with T1DM may develop anxiety symptoms and depression which can lead to the loss of glycemic control. An assistive technology that automatizes the activity monitoring process could support these young patient in managing T1DM. The aim of this work is to present the MyDi framework which integrates a smart glycemic diary (for Android users), to automatically record and store patient's activity via pictures and a deep-learning (DL)-based technology able to classify the activity performed by the patients (i.e., meal and sport) via picture analysis. The proposed approach was tested on two different datasets, the Insta-Dataset with 3498 pictures (also used for training and validating the DL model) and the MyDi-Dataset with 126 pictures, achieving very encouraging results in both cases (Preci= 1.0, Reci= 1.0, f1i= 1.0 with i E C:[meal, sport]) prompting the possibility of translating this application in the T1DM monitoring process

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

Polyphenols as potential agents in the management of temporomandibular disorders

Temporomandibular disorders (TMD) consist of multifactorial musculoskeletal disorders associated with the muscles of mastication, temporomandibular joint (TMJ), and annexed structures. This clinical condition is characterized by temporomandibular pain, restricted mandibular movement, and TMJ synovial inflammation, resulting in reduced quality of life of affected people. Commonly, TMD management aims to reduce pain and inflammation by using pharmacologic therapies that show efficacy in pain relief but their long-term use is frequently associated with adverse effects. For this reason, the use of natural compounds as an effective alternative to conventional drugs appears extremely interesting. Indeed, polyphenols could represent a potential therapeutic strategy, related to their ability to modulate the inflammatory responses involved in TMD. The present work reviews the mechanisms underlying inflammation-related TMD, highlighting the potential role of polyphenols as a promising approach to develop innovative management of temporomandibular diseases