Experimenting the role of UX design in the definition of gender-sensitive service design policies

The environmental and social sustainability objectives indicated in the 2030 agenda require the development of services for urban contexts capable of responding to the diversified primary needs of different segments of the population. International studies on gender issues show that understanding the specific needs of women, their behaviors and their expectations can provide indications for creating more equitable and inclusive services. The article reports the significant results obtained using UX Design techniques and tools for gender-oriented service design. University researchers, students and women's associations partnered in order to carry out this activity and gather indications on the specific female points of view capable of guiding the development of better services and inspiring decision makers and service providers. The research also demonstrates the potential of applying the UX Design approach in the investigation of the gender perspective and in dialogue with nonprofit associations interested in social innovation

Stringent bounds on HWWHWW and HZZHZZ anomalous couplings with quantum tomography at the LHC

Quantum tomography provides the full reconstruction of the density matrix of a state. We use it to study the Higgs boson decay into weak gauge bosons. Anomalous couplings beyond the Standard Model can be constrained by means of observables easily defined in terms of entries of the polarization density matrix. We describe a strategy based on two observables that together provide the most stringent limits. One of these observables is linked to entanglement between the polarizations of the two gauge bosons, the other to CP-odd combinations of one momentum and two polarizations. We find that this strategy could offer, already with the available LHC data, an improvement by a factor of 5 to one order of magnitude with respect to the best current bounds.Comment: 13 pages, 3 figures. arXiv admin note: text overlap with arXiv:2302.0068

Exploring the Impact of Learning Paradigms on Network Generalization: A Multi-Center IMT Study

The intima-media thickness (IMT) is an important parameter for evaluating cardiovascular disease risk and progression and can be extracted from B-mode longitudinal ultrasound images of the carotid artery. Despite its clinical significance, inter- and intra-operator variability in IMT measurement is a challenge due to subjective factors. Therefore, automatic and semi-automatic approaches based on heuristic methods and deep neural networks have been proposed to reduce the variability in IMT measurement. However, the inter- and intra- operator variability still remains an issue as it affects the quality and diversity of ground truth (GT) data used for training deep learning models. In this study, the authors evaluate the performance of different learning paradigms using different GTs on a multi-center IMT dataset. A recent segmentation network, ConvNeXt, is trained on a dataset of 2576 B-mode longitudinal ultrasound images of the carotid artery, using different GT annotations and learning paradigms. The method is then tested on an external dataset of 448 images from four different centers for which three manual segmentations were available. The results show how the use of different GT annotations and learning paradigms can enhance the generalization ability of deep learning models, demonstrating the importance of selecting appropriate GT data and learning strategies in achieving robust and reliable solutions. The study highlights the significance of incorporating heuristic methods in the training process of deep learning models to enhance the accuracy and consistency of IMT measurement, thus enabling more precise cardiovascular disease risk assessment

Automatic segmentation of the optic nerve in transorbital ultrasound images using a deep learning approach

Transorbital sonography is able to provide reliable information about (a) intra-cranial pressure estimation through the optic nerve sheath diameter (ONSD) measurement, and (b) optic nerve atrophy in patients with multiple sclerosis through the optic nerve diameter (OND). In this study, we present the first method for the automatic measurement of the OND and ONSD using a deep learning technique (UNet with ResNet50 encoder) for the optic nerve segmentation. The dataset included 201 images from 50 patients. The automated measurements were compared with manual ones obtained by one operator. The mean error was equal to 0.07 ± 0.34 mm and -0.07 ± 0.67 mm, for the OND and ONSD, respectively. The developed system should aid in standardizing OND and ONSD measurements and reduce manual evaluation variability

Chemical shift imaging at 4.7 tesla of brown adipose tissue.

In vivo distinction between small deposits of brown adipose tissue (BAT) and surrounding tissues may be difficult. In this article, we propose an experiment paradigm, based on techniques of chemical shift magnetic resonance imaging (CSI), which can improve the methods presently available for the study of BAT. Male rats were examined in an imager-spectrometer equipped with a 4.7 T magnet. Proton spectra of isolated BAT deposits showed that both fat and water protons contributed significantly to the genesis of the magnetic resonance signal. An equivocal definition of BAT deposits was obtained by three (respectively, spin-echo, water-selective, and fat-selective) images. The spin-echo (SE), T1-weighted image provided the best anatomical description of the structures. The images selective for fat-protons displayed the degree of lipid accumulation in each area. The images selective for water-protons provided an internal control of adipose tissue localization. The proposed paradigm allows an unequivocal definition of BAT deposits and appears particularly useful in studies where experimental manipulation (i.e., cold acclimation or drug treatment) produces changes in this issue

Automatic segmentation of ultrasound images of gastrocnemius medialis with different echogenicity levels using convolutional neural networks

The purpose of this study was to develop an automatic method for the segmentation of muscle cross-sectional area on transverse B-mode ultrasound images of gastrocnemius medialis using a convolutional neural network(CNN). In the provided dataset images with both normal and increased echogenicity are present. The manually annotated dataset consisted of 591 images, from 200 subjects, 400 relative to subjects with normal echogenicity and 191 to subjects with augmented echogenicity. From the DICOM files, the image has been extracted and processed using the CNN, then the output has been post-processed to obtain a finer segmentation. Final results have been compared to the manual segmentations. Precision and Recall scores as mean ± standard deviation for training, validation, and test sets are 0.96 ± 0.05, 0.90 ± 0.18, 0.89 ± 0.15 and 0.97 ±0.03, 0.89± 0.17, 0.90 ± 0.14 respectively. The CNN approach has also been compared to another automatic algorithm, showing better performances. The proposed automatic method provides an accurate estimation of muscle cross-sectional area in muscles with different echogenicity levels

Comparison of Histogram-based Textural Features between Cancerous and Normal Prostatic Tissue in Multiparametric Magnetic Resonance Images

In the last decade, multiparametric magnetic resonance imaging (mpMRI) has been expanding its role in prostate cancer detection and characterization. In this work, 19 patients with clinically significant peripheral zone (PZ) tumours were studied. Tumour masks annotated on the whole-mount histology sections were mapped on T2-weighted (T2w) and diffusion-weighted (DW) sequences. Gray-level histograms of tumoral and normal tissue were compared using six first-order texture features. Multivariate analysis of variance (MANOVA) was used to compare group means. Mean intensity signal of ADC showed the highest showed the highest area under the receiver operator characteristics curve (AUC) equal to 0.85. MANOVA analysis revealed that ADC features allows a better separation between normal and cancerous tissue with respect to T2w features (ADC: P = 0.0003, AUC = 0.86; T2w: P = 0.03, AUC = 0.74). MANOVA proved that the combination of T2-weighted and apparent diffusion coefficient (ADC) map features increased the AUC to 0.88. Histogram-based features extracted from invivo mpMRI can help discriminating significant PZ PCa

Innovative temporal loss function for segmentation of fine structures in ultrasound images

Over the past few years, there have been significant advancements in deep learning architectures for semantic segmentation. However, the performance of these models heavily relies on the loss function (LF) used during network training. The LF is a crucial component that enables the network to weigh the errors differently based on the segmentation task to be performed. Despite the progress made in designing increasingly complex and deep architectures for semantic segmentation, the LFs used in these models have remained almost unchanged. Accurately segmenting small and fine objects, such as vessel walls (e.g., intima-media complex, IMC) or nerves (e.g., optic nerve), in ultrasound (US) images is still a challenging task. One of the main difficulties is pixel imbalance between the object and the background, which can result in inaccurate segmentation. Additionally, precise and accurate segmentation along the object's edge is crucial for medical diagnosis and treatment. To address these challenges, this paper proposes a new, temporal loss function for semantic segmentation in US images. The idea behind a temporal loss is to enable the network to learn from multiple sources of information simultaneously and to give more emphasis to losses that are more informative at different stages of the training process. The proposed LF considers pixel imbalance between the object and background and enables precise and accurate segmentation along the object's edge. The study aims to demonstrate the effectiveness of the proposed LF by evaluating its performance in segmenting vessel walls in US images