Explore the vast range of titles available.

Objective: Destroying liver tumors is a challenge for contemporary interventional radiology. The aim of this work is to compare different techniques used for the measurement of respiratory motion, as this is the main hurdle to the effective implementation of this therapy. Methods: Laparoscopic stereoscopic reconstruction of point displacements on the surface of the liver, observation of breathing using external markers placed on the surface of the abdominal cavity, and methods for registration of the surface of the abdominal cavity during breathing were implemented and evaluated. Results: The following accuracies were obtained: above 4 mm and 0.5 mm, and below 8 mm for laparoscopic, skin markers, and skin surface registration methods, respectively. Conclusions: The clinical techniques and accompanying imaging modalities employed to destroy liver tumors, as well as the advantages and limitations of the proposed methods, are presented. Further directions for their development are also indicated.

Objective: Respiratory movements are a significant factor that may hinder the use of image navigation systems during minimally invasive procedures used to destroy focal lesions in the liver. This article aims to present a method of estimating the displacement of the target point due to respiratory movements during the procedure, working in real time. Method: The real-time method using skin markers and non-rigid registration algorithms has been implemented and tested for various classes of transformation. The method was validated using clinical data from 21 patients diagnosed with liver tumors. For each patient, each marker was treated as a target and the remaining markers as target position predictors, resulting in 162 configurations and 1095 respiratory cycles analyzed. In addition, the possibility of estimating the respiratory phase signal directly from intraoperative US images and the possibility of synchronization with the 4D CT respiratory sequence are also presented, based on ten patients. Results: The median value of the target registration error (TRE) was 3.47 for the non-rigid registration method using the combination of rigid transformation and elastic body spline curves, and an adaptation of the assessing quality using image registration circuits (AQUIRC) method. The average maximum distance was 3.4 (minimum: 1.6, maximum 6.8) mm. Conclusions: The proposed method obtained promising real-time TRE values. It also allowed for the estimation of the TRE at a given geometric margin level to determine the estimated target position. Directions for further quantitative research and the practical possibility of combining both methods are also presented.

Despite advances in assistive technologies, existing tools for teaching mathematics to students with low vision often fail to effectively convey structural information in graphs and function plots. Current methods, such as screen readers or magnifiers, are frequently limited in their ability to present complex visual data, leading to increased cognitive load and reduced learning efficiency. This article introduces the audio-graphic method designed to address these limitations by integrating audio feedback with graphical content. The method was implemented in an educational platform and evaluated with a group of visually impaired participants divided into three subgroups based on the WHO classification of visual impairment: mild, moderate, and severe. The findings indicate that the proposed method achieves comparable or improved learning outcomes with reduced effort and frustration, particularly for students with mild and severe low vision. Statistically significant reductions in task completion times were observed, especially for complex exercises involving power and trigonometric functions. Usability assessments further confirm the platform’s accessibility and ease of use. These results suggest promising directions for developing multimodal educational tools that better support STEM learning for individuals with low vision.

Surface registration is a one of the crucial and actual problems of computer aided surgery. This paper presents the modification of the non-rigid Iterative Closest Point Algorithm which takes into account an anisotropic noise model and landmarks as guided correspondence at the transformation step in every iteration. The presented approach was validated on human abdominal briefing surface data from a time-of-flight camera. We took the median of the resulting measures and the outcome is presented: the median of means of surfaces distance was at the same level for both variants of the ICP algorithm and is comparable with the isotropic variant, the median of mean landmark position errors decreased by 0.93 units (over 20% improvement) and the median of percentage of single correspondences in target point cloud increased by 11.96%. The results showed that the introduction of the anisotropic model of noise for the ToF camera allows for the improvement the percentage of target cloud points which had only one correspondent over 10% impartment and additional weighting of markers also improves the measure of the quality of finding real correspondents over 20% improvement. In the examined dataset, where the average initial distance between the clouds of points in the inspiratory and expiration is equal to approx. 7.5 mm, a more than 10% improvement in the quality of the correspondence improves the accuracy of matching the surface within 1 mm which is a significant value in application of minimally invasive image guided interventions.

Teaching mathematics to blind people is a challenge of modern educational methods. This article presents a method of preparing the adapted material and its usage in the learning process of mathematics by blind people, as well as the results of evaluating the proposed approach. The presented results were obtained based on a mathematical analysis course conducted in two classes—with and without using the developed method. The developed method uses the conceptualization of knowledge as a graph. The learning process is supported by feedback processes that consider the mechanisms of knowledge and error vectors, on which a personalized adaptation of the learning path is made for each particular student. The evaluation process has shown a statistically significant improvement in learning results achieved by blind students. The average final test score in the group working with the platform during learning increased by 14%. In addition, there was an increase in cooperation between blind students who had the opportunity to take on the role of a teacher, which was observed in 27% of the participants. Our results indicate the effectiveness of the developed approach and motivate us to evaluate the method in a broader group of students. The engagement of students indirectly indicates overcoming the barriers known from the state of the art: uncertainty, poor motivation, and difficulties in consolidating the acquired skills.

Background Diagnosing and treating anorexia nervosa is an important challenge for modern psychiatry. Taking into account a connection between the mental state of a person and the characteristics of their language, this paper presents developed and tested method for analyzing the written statements of patients with anorexia nervosa and healthy individuals, including the identification of keywords. Methods Due to the short nature of the texts, which is related to the difficulty of expressing oneself about one’s body when suffering from anorexia, the bag of words approach was used for documents’ information representation. The document is represented as a vector, where its various elements indicate the number of individual words. Then, a rule-based model was created, where as a collection of rules, dictionary files were used corresponding to three groups of positive, negative and neutral sounds for each subcategory. Next in the analyzed texts were searched and counted keywords. Based on the keywords found, each of the documents was categorized into one of the groups in every subcategory. Results It is possible to indicate a set of characteristics sentiment for every person. Additionally, the results of specific patient could be analyzed in six specific subcategories: self-esteem, acceptance of the assessment of the environment, emotions, autoimmune, functioning of the body and body image. Conclusions The described analysis indicates the existence of a relationship between the mental state of the author’s textual health and the vocabulary he or she uses. It is possible to indicate a set of characteristic sentiment terms specific to a given group of people. Their presence is related to the author’s mental state and their body image. It could help focus on specific topics during therapy.

This paper demonstrates how natural language processing methods can support the computer-aided rapid assessment of young adults suffering from anorexia nervosa. We applied natural language processing and machine learning techniques to develop methods that classified body image notes into four categories (sick/healthy, past tense, irony, and sentiment) and analyzed personal vocabulary. The datasets consisted of notes from 115 anorexic patients, 85 healthy participants, and 50 participants with head and neck cancer. To evaluate the usefulness of the proposed approach, we interviewed ten professional psychologists who were experts in eating disorders, eight direct (first contact) staff, and fourteen school counselors and school psychologists. The developed tools correctly differentiated the individuals suffering from anorexia nervosa, which was reflected in the linguistic profile and the results of the machine learning classification of the body image notes. The developed tool also received a positive evaluation from the psychologists specializing in treating eating disorders, school psychologists, and nurses. The obtained results indicate the potential of using natural language processing techniques for the computer-aided rapid assessment of a person’s condition in terms of anorexia nervosa. This method could be applied as both a screening tool and for the regular monitoring of people at risk of eating disorders.

Background Liver segmentation in computed tomography is required in many clinical applications. The segmentation methods used can be classified according to a number of criteria. One important criterion for method selection is the shape representation of the segmented organ. The aim of the work is automatic liver segmentation using general purpose shape modeling methods. Methods As part of the research, methods based on shape information at various levels of advancement were used. The single atlas based segmentation method was used as the simplest shape-based method. This method is derived from a single atlas using the deformable free-form deformation of the control point curves. Subsequently, the classic and modified Active Shape Model (ASM) was used, using medium body shape models. As the most advanced and main method generalized statistical shape models, Gaussian Process Morphable Models was used, which are based on multi-dimensional Gaussian distributions of the shape deformation field. Results Mutual information and sum os square distance were used as similarity measures. The poorest results were obtained for the single atlas method. For the ASM method in 10 analyzed cases for seven test images, the Dice coefficient was above 55 % , of which for three of them the coefficient was over 70 % , which placed the method in second place. The best results were obtained for the method of generalized statistical distribution of the deformation field. The DICE coefficient for this method was 88.5 % Conclusions This value of 88.5 % Dice coefficient can be explained by the use of general-purpose shape modeling methods with a large variance of the shape of the modeled object—the liver and limitations on the size of our training data set, which was limited to 10 cases. The obtained results in presented fully automatic method are comparable with dedicated methods for liver segmentation. In addition, the deforamtion features of the model can be modeled mathematically by using various kernel functions, which allows to segment the liver on a comparable level using a smaller learning set.

Alternative methods of presenting the information contained in mathematical images, which are adapted to the needs of blind people, are significant challenges in modern education. This article presents an alternative multimodal method that substitutes the sense of sight with the sense of touch and hearing to convey graphical information. The developed method was evaluated at a center specializing in the education of the blind in Poland, on a group of 46 students aged 15–19. They solved a set of 60 high school-level problems on geometry, mathematical analysis, and various types of graphs. We assessed the mechanisms introduced for the sense of touch and hearing, as well as the overall impression of the users. The system usability scale and the NASA task load index tests were used in the evaluation. The results obtained indicate an overall increase in user satisfaction and usefulness of the proposed approach and a reduction in the workload during exercise solving. The results also show a significant impact of the proposed navigation modes on the average time to reach objects in the drawing. Therefore, the presented method could significantly contribute to the development of systems supporting multimodal education for people with blindness.

Background The geometry of the vessels is easy to assess in novel 3D studies. It has significant influence on flow patterns and this way the evolution of vascular pathologies such as aneurysms and atherosclerosis. It is essential to develop robust system for vascular anatomy measurement and digital description allowing for assessment of big numbers of vessels. Methods A semiautomatic, robust, integrated method for vascular anatomy measurements and mathematical description are presented. Bezier splines of 6th degree and continuity of C3 was proposed and distribution of control points was dependent on local radius. Due to main interest of our institution, the system was primarily used for the assessment of the geometry of the intracranial arteries, especially the first Medial Cerebral Artery division. Results 1359 synthetic figures were generated: 381 torus and 978 spirals. Experimental verification of the proposed methodology was conducted on 400 Middle Cerebral Artery divisions. Conclusions In difference to other described solution all proposed methodology steps were integrated allows analysis of variability of geometrical parameters among big number of Medial Cerebral Artery bifurcations using single application. This allows for determination of significant trends in the parameters variability with age and in contrary almost no differences between men and women.