Abstract
Encouraging pre-university students to pursue engineering courses at the university level is essential to meet the industry’s escalating demand for engineers. Each year, universities host hundreds of secondary students who tour their facilities to get a feel for the academic environment. This paper discusses an educational experiment designed as part of a semester-long undergraduate project in Informatics Engineering. The project involves tailoring a Dobot Magician robot, equipped with a standard webcam, to engage in a game of tic-tac-toe against a human user. The camera stream is continuously processed by a computer vision algorithm to detect the pieces placement in the game board. The paper outlines the project development stages, the elements involved, and presents preliminary test results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
Canine hip dysplasia (CHD) screening relies on accurate positioning in the ventrodorsal hip extended (VDHE) view, as even mild pelvic rotation can affect CHD scoring and impact breeding decisions. This study aimed to assess the association between pelvic rotation and asymmetry in obturator foramina areas (AOFAs) and to develop a computer vision model for automated AOFA measurement. In the first part, 203 radiographs were analyzed to examine the relationship between pelvic rotation, assessed through asymmetry in iliac wing and obturator foramina widths (AOFWs), and AOFAs. A significant association was found between pelvic rotation and AOFA, with AOFW showing a stronger correlation (R-2 = 0.92, p < 0.01). AOFW rotation values were categorized into minimal (n = 71), moderate (n = 41), marked (n = 37), and extreme (n = 54) groups, corresponding to mean AOFA +/- standard deviation values of 33.28 +/- 27.25, 54.73 +/- 27.98, 85.85 +/- 41.31, and 160.68 +/- 64.20 mm(2), respectively. ANOVA and post hoc testing confirmed significant differences in AOFA across these groups (p < 0.01). In part two, the dataset was expanded to 312 images to develop the automated AOFA model, with 80% allocated for training, 10% for validation, and 10% for testing. On the 32 test images, the model achieved high segmentation accuracy (Dice score = 0.96; Intersection over Union = 0.93), closely aligning with examiner measurements. Paired t-tests indicated no significant differences between the examiner and model's outputs (p > 0.05), though the Bland-Altman analysis identified occasional discrepancies. The model demonstrated excellent reliability (ICC = 0.99) with a standard error of 17.18 mm(2). A threshold of 50.46 mm(2) enabled effective differentiation between acceptable and excessive pelvic rotation. With additional training data, further improvements in precision are expected, enhancing the model's clinical utility.

Abstract
Radiographic canine hip dysplasia (CHD) diagnosis is crucial for breeding selection and disease management, delaying progression and alleviating the associated pain. Radiography is the primary imaging modality for CHD diagnosis, and visual assessment of radiographic features is sometimes used for accurate diagnosis. Specifically, alterations in femoral neck shape are crucial radiographic signs, with existing literature suggesting that dysplastic hips have a greater femoral neck thickness (FNT). In this study we aimed to develop a three-stage deep learning-based system that can automatically identify and quantify a femoral neck thickness index (FNTi) as a key metric to improve CHD diagnosis. Our system trained a keypoint detection model and a segmentation model to determine landmark and boundary coordinates of the femur and acetabulum, respectively. We then executed a series of mathematical operations to calculate the FNTi. The keypoint detection model achieved a mean absolute error (MAE) of 0.013 during training, while the femur segmentation results achieved a dice score (DS) of 0.978. Our three-stage deep learning-based system achieved an intraclass correlation coefficient of 0.86 (95% confidence interval) and showed no significant differences in paired t-test compared to a specialist (p > 0.05). As far as we know, this is the initial study to thoroughly measure FNTi by applying computer vision and deep learning-based approaches, which can provide reliable support in CHD diagnosis. © The Author(s) 2024.

Abstract
This article conducts a comprehensive review of the existing literature on data augmentation and data generation techniques within the context of medical image processing. Addressing the challenges associated with building sizable medical image datasets, including the rarity of certain medical conditions, patient privacy concerns, the need for expert labeling, and the associated expenses, this review focuses on methodologies aimed at enhancing the volume and diversity of available data. Special emphasis is placed on techniques such as data augmentation and data generation, with a particular interest in their application to medical image datasets. The objective is to provide a synthesis of current research, methodologies, and advancements in this domain, offering insights into the state-of-the-art practices and identifying potential avenues for future developments in medical image data augmentation.

Abstract
Growing obesity has been a worldwide issue for several years. This is the outcome of common nutritional disorders which results in obese individuals who are prone to many diseases. Managing diet while simultaneously dealing with the obligations of a working adult can be difficult. Today, people have a very fast-paced life and sometimes neglect food choices. In order to simplify the interpretation of the Nutri-score labeling this paper proposes a method capable of automatically reading food labels with this format. This method is intended to support users when choosing the products to buy based on the letter identification of the label. For this purpose, a dataset was created, and a prototype mobile application was developed using a deep learning network to recognize the Nutri-score information. Although the final solution is still in progress, the reading module, which includes the proposed method, achieved an encouraging and promising accuracy (above 90%). The upcoming developments of the model include information to the user about the nutritional value of the analyzed product combining it's Nutri-score label and composition.

Abstract
This paper presents a study on the automated detection of landmarks in medical x-ray images using deep learning techniques. In this work we developed two neural networks based on semantic segmentation to automatically detect landmarks in x-ray images, using a dataset of 200 encephalogram images: the UNet architecture and the FPN architecture. The UNet and FPN architectures are compared and it can be concluded that the FPN model, with IoU=0.91, is more robust and accurate in predicting landmarks. The study also had the goal of direct application in a medical context of diagnosing the models and their predictions. Our research team also developed a metric analysis, based on the encephalograms in the dataset, on the type of Mandibular Occlusion of the patients, thus allowing a fast and accurate response in the identification and classification of a diagnosis. The paper highlights the potential of deep learning for automating the detection of anatomical landmarks in medical imaging, which can save time, improve diagnostic accuracy, and facilitate treatment planning. We hope to develop a universal model in the future, capable of evaluating any type of metric using image segmentation.