Despite several automated strategies for identification/segmentation of Multiple Sclerosis (MS) lesions in Magnetic Resonance Imaging (MRI) being developed, they consistently fall short when compared to the performance of human experts. This emphasizes the unique skills and expertise of human professionals in dealing with the uncertainty resulting from the vagueness and variability of MS, the lack of specificity of MRI concerning MS, and the inherent instabilities of MRI. Physicians manage this uncertainty in part by relying on their radiological, clinical, and anatomical experience. We have developed an automated framework for identifying and segmenting MS lesions in MRI scans by introducing a novel approach to replicating human diagnosis, a significant advancement in the field. This framework has the potential to revolutionize the way MS lesions are identified and segmented, being based on three main concepts: (1) Modeling the uncertainty; (2) Use of separately trained Convolutional Neural Networks (CNNs) optimized for detecting lesions, also considering their context in the brain, and to ensure spatial continuity; (3) Implementing an ensemble classifier to combine information from these CNNs. The proposed framework has been trained, validated, and tested on a single MRI modality, the FLuid-Attenuated Inversion Recovery (FLAIR) of the MSSEG benchmark public data set containing annotated data from seven expert radiologists and one ground truth. The comparison with the ground truth and each of the seven human raters demonstrates that it operates similarly to human raters. At the same time, the proposed model demonstrates more stability, effectiveness and robustness to biases than any other state-of-the-art model though using just the FLAIR modality.

One of the studies of the active brain-computer interface (BCI) focuses on identifying movements from human neurophysiological signals to control external devices such as robotic arms. In the literature, EEG-based BCI is utilized to decode user’s information to perform action or fill the gap from the brain to the arms in the case of illness. The purpose of this work is to understand, among the scientific literature, what the best Deep Learning (DL) architecture is for motor execution (ME) classification. Data from 105 people from the Physionet dataset and 15 subjects from the Upper Limb dataset were used. EEGnetv4, Deep4Net, and EEGITnet were used to classify EEG signals under ME for real-time BCI. The best results were achieved from the EEGNET trained without Common Spatial Pattern transformation, for both datasets.

Digital Twin technology in healthcare offers personalized care through advanced analytics, real-time data, and virtual models. In this paper we propose the adoption of the Digital Twin approach as a means for modeling hands, both injured and healthy, in a Double Digital Twin (DDT) using the Virtual Glove. The VG acts as a supportive rehabilitation device, capable of collecting data and reconstructing models for both the impaired and healthy hands. This study emphasizes the calibration process, which adjusts the model of the healthy hand to match the injured hand. Additionally, the transformed healthy hand model is used to guide the task with the injured system, ensuring synchronization and repeatability of exercises. Furthermore, the framework associated with DDT facilitates the analysis and quantification of the mobility of the impaired hand in comparison to the healthy one. Finally, a preliminary experiment is presented. Index Terms—Virtual Glove, Tele-Medicine, Double Digital Twin, Hand Rehabiliation, Virtual Reality

Magnetic Resonance Imaging (MRI) stands as a noninvasive tool for diagnosing and monitoring various diseases. The flexibility of MRI configuration parameters allows for adaptable imaging sequences, and at the same time poses challenges in terms of reproducibility, as variability in imaging sequences leads to significant differences in image contrast. This is one of the major causes that compromise the reliability of deep learning methods. Since the majority of the literature is focused on documenting the effects of this issue rather than delving into its underlying causes, this work follows a different approach. A Siamese Neural Network (SNN) has been trained to identify the scanner that acquired the input image. Experimental results include the use of Euclidean Distance (ED) and machine learning algorithms trained and tested using the feature vectors generated with the SNN. The results have shown that the proposed method is capable of distinguishing the scanner used for the acquisition with high accuracy. For a comprehensive interpretation of the results, the feature vectors have been dimensionality reduced and visualized with a 3D plot. Finally, the proposed method is sensitive to MR image contrast variability and could be used to detect data-related inconsistencies and provide a mechanism to make users aware of potential issues.

Emotion recognition is useful in several fields, starting from medical diagnosis to driving a Brain-Computer Interface (BCI) or helping people with disabilities. During the last decades, many researchers applied automatic strategies to identify emotional states based on data acquired by electroen-cephalography (EEG). However, the task is very hard and results have been often ambiguous. This work aims to perform brain connectivity studies of EEG data of four self-stimulated emotional classes (“relax”, “anger”, “happiness”, “sadness”) using a graph model of the Phase Lag Index (PLI), being PLI a measurement of connection insensitive to volume conduction effect. Qualitative results show that, for the analyzed emotions, connectivity analysis indicates some relevant differences both in the active brain regions and in the bandwidths involved in the activation. This method for connectome generation and analysis shows that useful information can be derived and used for contributing to disambiguating the problem of automatic emotion recognition.

Magnetic resonance imaging (MRI) is an effective imaging tool that, due to its non-invasiveness and multiple-parameter nature, is frequently used in medicine. In particular, the MRI's inherent flexibility deriving from the usage of multiple parameters allows to obtain images of variable contrast and quality. However, intrinsic MRI contrast variability often comes with drawbacks in terms of differences in different scanners, thus resulting in the impossibility of standardizing the image contrast. In particular, this variability could negatively affect the automatic analysis of Deep Learning (DL) methods, both in the training phase and in the test phase. In this work, we present several results on how images collected from different MRI scanners are handled by DL methods. To this end, we trained a Siamese network (SNN), based on the EfficientNet-B0 Convolutional Neural Network (EN-CNN), to learn how to recognize the scanner that has generated a given image. The output encoding features of the SNN have been projected into a 2D space with Uniform Manifold Approximation and Projection (UMAP) and have been discussed. Regarding the training phase, the UMAP projects show that the network is capable of separating MR images encoded features from different MRI scanners. Moreover, even if the MR images of different subjects are acquired with the same scanner, the results suggest that there are considerable differences in how the SNN encoded those features. The test phase confirmed that the SNN architecture is capable of recognizing images from different MRI scanners.

In this paper we analyze different Mixed Integer Linear Programming (MILP) models in order to produce 1D and 3D Spread-Out Bragg peaks (SOBP) for protons in water. Our techniques do not use much computational resources; in particular, all our experiments have been performed by a standard personal computer. As main result we give the proof of concept that the techniques that we use to create parameterized uniform SOBP can be fruitfully used in Treatment Planning Systems (TPS) for Intensity Modulated Proton Therapy (IMPT). As technical result we show, for the first time to our best knowledge, that there is a trade-off between the minimum number of energies (or layers) to be used to have a SOBP peak within a uniformity tolerance parameter Dtand the same parameter Dt. Minimizing the number of energies also has the advantage of reducing the delivery time using the facilities in operation nowadays.

Hand tracking and gesture recognition are fundamental in a multitude of applications. Various sensors have been used for this purpose, however, all monocular vision systems face limitations caused by occlusions. Wearable equipment overcome said limitations, although deemed impractical in some cases. Using more than one sensor provides a way to overcome this problem, but necessitates more complicated designs. In this work, we aim to highlight contemporary methods used for hand tracking and gesture recognition by collecting publications of systems developed in the last decade, that employ contactless devices as RGB cameras, IR, and depth sensors, along with some preceding pillar works. Additionally, we briefly present common steps, techniques, and basic algorithms used during the process of developing modern hand tracking and gesture recognition systems and, finally, we derive the trend for the next future.

In medical imaging, images represent the quantification of the interaction between electromagnetic waves and our body and are represented in grey-scale. In addition, medical imaging often produces multimodal images. However, the analysis and interpretation of these images mostly occur in sequence or, as in the case of automatic tools, they are simply concatenated as independent sources of information. In both cases, color perception and color contrast are not exploited. Color perception and color contrast play a crucial role in human vision to recognize objects effectively and efficiently, and this can in principle extend to automatic systems. In this paper we show how color coding, particularly using color opponent models, can become an effective tool for preliminary color-based segmentation. Tests have been conducted on multimodal Magnetic Resonance Imaging (MRI) of the brain collected in a public database and the results obtained show the importance of color coding in medical imaging analysis.

Proton therapy is a cancer therapy that is more expensive than classical radiotherapy but that is considered the gold standard in several situations. Since there is also a limited amount of delivering facilities for this techniques, it is fundamental to increase the number of treated patients over time. The objective of this work is to offer an insight on the problem of the optimization of the part of the delivery time of a treatment plan that relates to the movements of the system. We denote it as the Nozzle Travel Time Problem (NTTP), in analogy with the Leaf Travel Time Problem (LTTP) in classical radiotherapy. In particular this work: (i) describes a mathematical model for the delivery system and formalize the optimization problem for finding the optimal sequence of movements of the system (nozzle and bed) that satisfies the covering of the prescribed irradiation directions; (ii) provides an optimization pipeline that solves the problem for instances with an amount of irradiation directions much greater than those usually employed in the clinical practice; (iii) reports preliminary results about the effects of employing two different resolution strategies within the aforementioned pipeline, that rely on an exact Traveling Salesman Problem (TSP) solver, Concorde, and an efficient Vehicle Routing Problem (VRP) heuristic, VROOM.

Proton therapy is nowadays a major clinical modality in the fight against cancer due to the advantages offered by its peculiar depth dose profile, that allows to improve its efficacy on tumors while reducing damages to healthy tissues. The number of worldwide facilities and of treated patients is increasing every year. A challenge of proton therapy is that treatment planning systems require accurate dose computation, but the golden standard in accuracy are Monte Carlo algorithms that are slow. For this reason, accurate and faster dose calculation algorithms are needed. In this paper we use Deep Learning to achieve both speed and accuracy in dose calculation for proton therapy. The results positively compare with previous existing literature in the thorax cases, that are usually the most difficult to calculate by fast algorithms.

Given a set $\mathcal{D}$ ofqdocuments, the Longest Common Substring (LCS) problem asks, for any integer 2 ⩽k⩽q, the longest substring that appears inkdocuments. LCS is a well-studied problem having a wide range of applications in Bioinformatics: from microarrays to DNA sequences alignments and analysis. This problem has been solved by Hui (2000International Journal of Computer Science and Engineering1573–76) by using a famous constant-time solution to the Lowest Common Ancestor (LCA) problem in trees coupled with the use of suffix trees. In this article, we present a simple method for solving the LCS problem by using suffix trees (STs) and classical union-find data structures. In turn, we show how this simple algorithm can be adapted in order to work with other space efficient data structures such as the enhanced suffix arrays (ESA) and the compressed suffix tree.

LZ77-based compression schemes compress the input text by replacing factors in the text with an encoded reference to a previous occurrence formed by the couple (length, offset). For a given factor, the smallest is the offset, the smallest is the resulting compression ratio. This is optimally achieved by using the rightmost occurrence of a factor in the previous text. Given a cost function, for instance the minimum number of bits used to represent an integer, we define the Rightmost Equal-Cost Position (REP) problem as the problem of finding one of the occurrences of a factor whose cost is equal to the cost of the rightmost one. We present the Multi-Layer Suffix Tree data structure that, for a text of length n, at any time i, it provides REP(LPF) in constant time, where LPF is the longest previous factor, i.e. the greedy phrase, a reference to the list of REP({set of prefixes of LPF}) in constant time and REP(p) in time O(|p| log log n) for any given pattern p.

Classically, several properties and relations of words, such as “being a power of the same word” can be expressed by using word equations. This paper is devoted to a general study of the expressive power of word equations. As main results we prove theorems which allow us to show that certain properties of words are not expressible as components of solutions of word equations. In particular, “the primitiveness” and “the equal length” are such properties, as well as being “any word over a proper subalphabet”.

We give a new text-compression scheme based on forbidden words ("antidictionary"). We prove that our algorithms attain the entropy for balanced binary sources. They run in linear time. Moreover, one of the main advantages of this approach is that it produces very fast decompressors. A second advantage is a synchronization property that is helpful to search compressed data and allows parallel compression. The techniques used in this paper are from information theory and finite automata.

The tessellation of the plane given by square cells of equal size can be considered the Cayley graph of the free abelian group of rank 2. This group has polynomial growth. The theorems of Moore [Symposium on Applied Mathematics, Vol. XIV, American Mathematical Society, Providence, Rhode Island, 1962, pp. 17–33] and Myhill [Proceedings of the American Mathematical Society, 14 (1963), pp. 685–686] on the existence of Garden of Eden configurations for an automaton defined on such a graph are extended to Cayley graphs of groups whose growth function is not exponential. Examples are given of Cayley graphs of groups of exponential growth for which these theorems do not hold.