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A few promising solutions for thermal imaging Unexploded Ordnance (UXO) detection were proposed after the start of the military conflict in Ukraine in 2014. At the same time, most of the landmine clearance protocols and practices are based on old, 20th-century technologies. More than 60 countries worldwide are still affected by explosive remnants of war, and new areas are contaminated almost every day. To date, no automated solutions exist for surface UXO detection by using thermal imaging. One of the reasons is also that there are no publicly available data. This research bridges both gaps by introducing an automated UXO detection method, and by publishing thermal imaging data. During a project in Bosnia and Herzegovina in 2019, an organisation, Norwegian People's Aid, collected data about unexploded ordnances and made them available for this research. Thermal images with a size of 720 × 480 pixels were collected by using an Unmanned Aerial Vehicle at a height of 3 m, thus achieving a very small Ground Sampling Distance (GSD). One of the goals of our research was also to verify if the explosive war remnants' detection accuracy could be improved further by using Convolutional Neural Networks (CNN). We have experimented with various existing modern CNN architectures for object identification, whereat the YOLOv5 model was selected as the most promising for retraining. An eleven-class object detection problem was solved primarily in this study. Our data were annotated semi-manually. Five versions of the YOLOv5 model, fine-tuned with a grid-search, were trained end-to-end on randomly selected 640 training and 80 validation images from our dataset. The trained models were verified on the remaining 88 images from our dataset. Objects from each of the eleven classes were identified with more than 90% probability, whereat the Mean Average Precision (mAP) at a 0.5 threshold was 99.5%, and the mAP at thresholds from 0.5 to 0.95 was 87.0% up to 90.5%, depending on the model's complexity. Our results are comparable to the state-of-the-art, whereat these object detection methods have been tested on other similar small datasets with thermal images. Our study is one of the few in the field of Automated UXO detection by using thermal images, and the first that solves the problem of identifying more than one class of objects. On the other hand, publicly available thermal images with a relatively small GSD will enable and stimulate the development of new detection algorithms, where our method and results can serve as a baseline. Only really accurate automatic UXO detection solutions will help to solve one of the least explored worldwide life-threatening problems. [ABSTRACT FROM AUTHOR]

Simple Summary: Tannins have traditionally been avoided in animal nutrition due to their anti-nutritive effects. However, recent studies reported hydrolysable tannins as beneficial additives that have antimutagenic, anticarcinogenic, antidiarrheal, and antiulcerogenic effects on animals. In a study testing the inclusion of hydrolysable tannins as a potential nutritive factor to reduce boar taint in entire males, significant enlargement of the parotid glands (parotidomegaly) was observed. In this study, we aimed to determine the morphological and immunohistochemical basis for the observed parotidomegaly. We discovered that enlargement of glandular lobules and acinar area, an increased ratio between the nucleus and cytoplasm of serous cells, and increased excretion of proline-rich proteins (PRPs) were characteristic of the experimental group that received the highest dietary tannin intake. The mandibular salivary gland, on the other hand, did not show significant morphological changes among the experimental groups. This suggests increased functional activity of the parotid salivary glands as the first and most important line of defense against high dietary tannin and its potential negative effects. The ingestion of hydrolysable tannins as a potential nutrient to reduce boar odor in entire males results in the significant enlargement of parotid glands (parotidomegaly). The objective of this study was to characterize the effects of different levels of hydrolysable tannins in the diet of fattening boars (n = 24) on salivary gland morphology and proline-rich protein (PRP) expression at the histological level. Four treatment groups of pigs (n = 6 per group) were fed either a control (T0) or experimental diet, where the T0 diet was supplemented with 1% (T1), 2% (T2), or 3% (T3) of the hydrolysable tannin-rich extract Farmatan®. After slaughter, the parotid and mandibular glands of the experimental pigs were harvested and dissected for staining using Goldner's Trichrome method, and immunohistochemical studies with antibodies against PRPs. Morphometric analysis was performed on microtome sections of both salivary glands, to measure the acinar area, the lobular area, the area of the secretory ductal cells, and the sizes of glandular cells and their nuclei. Histological assessment revealed that significant parotidomegaly was only present in the T3 group, based on the presence of larger glandular lobules, acinar areas, and their higher nucleus to cytoplasm ratio. The immunohistochemical method, supported by color intensity measurements, indicated significant increases in basic PRPs (PRB2) in the T3 and acidic PRPs (PRH1/2) in the T1 groups. Tannin supplementation did not affect the histo-morphological properties of the mandibular gland. This study confirms that pigs can adapt to a tannin-rich diet by making structural changes in their parotid salivary gland, indicating its higher functional activity. [ABSTRACT FROM AUTHOR]

Accurate automated localization of cephalometric landmarks in skull X-ray images is the basis for planning orthodontic treatments, predicting skull growth, or diagnosing face discrepancies. Such diagnoses require as many landmarks as possible to be detected on cephalograms. Today's best methods are adapted to detect just 19 landmarks accurately in images varying not too much. This paper describes the development of the SCN-EXT convolutional neural network (CNN), which is designed to localize 72 landmarks in strongly varying images. The proposed method is based on the SpatialConfiguration-Net network, which is upgraded by adding replications of the simpler local appearance and spatial configuration components. The CNN capacity can be increased without increasing the number of free parameters simultaneously by such modification of an architecture. The successfulness of our approach was confirmed experimentally on two datasets. The SCN-EXT method was, with respect to its effectiveness, around 4% behind the state-of-the-art on the small ISBI database with 250 testing images and 19 cephalometric landmarks. On the other hand, our method surpassed the state-of-the-art on the demanding AUDAX database with 4695 highly variable testing images and 72 landmarks statistically significantly by around 3%. Increasing the CNN capacity as proposed is especially important for a small learning set and limited computer resources. Our algorithm is already utilized in orthodontic clinical practice. [ABSTRACT FROM AUTHOR]

Automated detection of ovarian follicles in ultrasound images is much appreciated when its effectiveness is comparable with the experts' annotations. Today's best methods estimate follicles notably worse than the experts. This paper describes the development of two-stage deeply-supervised 3D Convolutional Neural Networks (CNN) based on the established U-Net. Either the entire U-Net or specific parts of the U-Net decoder were replicated in order to integrate the prior knowledge into the detection. Methods were trained end-to-end by follicle detection, while transfer learning was employed for ovary detection. The USOVA3D database of annotated ultrasound volumes, with its verification protocol, was used to verify the effectiveness. In follicle detection, the proposed methods estimate follicles up to 2.9% more accurately than the compared methods. With our two-stage CNNs trained by transfer learning, the effectiveness of ovary detection surpasses the up-to-date automated detection methods by about 7.6%. The obtained results demonstrated that our methods estimate follicles only slightly worse than the experts, while the ovaries are detected almost as accurately as by the experts. Statistical analysis of 50 repetitions of CNN model training proved that the training is stable, and that the effectiveness improvements are not only due to random initialisation. Our deeply-supervised 3D CNNs can be adapted easily to other problem domains. [ABSTRACT FROM AUTHOR]

Currently there is no publicly available adequate dataset that could be used for training Generative Adversarial Networks (GANs) on car images. All available car datasets differ in noise, pose, and zoom levels. Thus, the objective of this work was to create an improved car image dataset that would be better suited for GAN training. To improve the performance of the GAN, we coupled the LSUN and Stanford car datasets. A new merged dataset was then pruned in order to adjust zoom levels and reduce the noise of images. This process resulted in fewer images that could be used for training, with increased quality though. This pruned dataset was evaluated by training the StyleGAN with original settings. Pruning the combined LSUN and Stanford datasets resulted in 2,067,710 images of cars with less noise and more adjusted zoom levels. The training of the StyleGAN on the LSUN-Stanford car dataset proved to be superior to the training with just the LSUN dataset by 3.7% using the Fréchet Inception Distance (FID) as a metric. Results pointed out that the proposed LSUN-Stanford car dataset is more consistent and better suited for training GAN neural networks than other currently available large car datasets. [ABSTRACT FROM AUTHOR]

Accurate illuminant estimation from digital image data is a fundamental step of practically every image colour correction. Combinational illuminant estimation schemes have been shown to improve estimation accuracy significantly compared to other colour constancy algorithms. These schemes combine individual estimates of simpler colour constancy algorithms in some 'intelligent' manner into a joint and, usually, more efficient illuminant estimation. Among them, a combinational method based on Support Vector Regression (SVR) was proposed recently, demonstrating the more accurate illuminant estimation (Li et al. IEEE Trans. Image Process. 23(3), 1194-1209, 2014). We extended this method by our previously introduced convolutional framework, in which the illuminant was estimated by a set of image-specific filters generated using a linear analysis. In this work, the convolutional framework was reformulated, so that each image-specific filter obtained by principal component analysis (PCA) produced one illuminant estimate. All these individual estimates were then combined into a joint illuminant estimation by using SVR. Each illuminant estimation by using a single image-specific PCA filter within the convolutional framework actually represented one base algorithm for the combinational method based on SVR. The proposed method was validated on the well-known Gehler image dataset, reprocessed and prepared by author Shi, and, as well, on the NUS multi-camera dataset. It was shown that the median and trimean angular errors were (non-significantly) lower for our proposed method compared to the original combinational method based on SVR for which our method utilized just 6 image-specific PCA filters, while the original combinational method required 12 base algorithms for similar results. Nevertheless, a proposed method unified grey edge framework, PCA analysis, linear filtering theory, and SVR regression formally for the combinational illuminant estimation. [ABSTRACT FROM AUTHOR]

This article presents a reformulation of the Grey Edge framework for colour constancy. Colour constancy is the ability of a visual system to perceive objects' colours independently of their scenes' illuminants. Colour constancy algorithms try to estimate the colour of an illuminant from image values. This estimation can later be used to correct the image as though it were taken under a white illuminant. The modification presented allows the framework to incorporate image-specific filters instead of the commonly used edge detectors. A colour constancy algorithm is proposed using PCA and FastICA linear component analyses methods for the construction of such filters. The results show that the proposed method improves the accuracies of the Grey Edge framework algorithms whilst on the other hand, achieving comparable accuracies with the state-of-the-art methods, but improving their time efficiencies. [ABSTRACT FROM AUTHOR]

Modelling of patient knee joint from the MRI data and simulating its kinematics is presented. A flexion of the femur with respect to the tibia from 0° to around 40° is simulated. The finite element knee model is driven by compressive load measured 'in vivo' during MRI process by using specially developed optical force measurement system. Predicted kinematics is evaluated against the high-quality model obtained by registration from experimentally gathered low-quality MRI at fixed flexions. Validation pointed out that the mean square error (MSE) for the Euler rotation angles are bellow 1.73°, while the MSE for Euler translation is smaller than 5.93 mm. [ABSTRACT FROM AUTHOR]

A framework for construction of virtual models of the medical phenomena is proposed. Major construction steps are discussed in detail. The construction of virtual medical model is guided from acquisition of patient imaging material, to 3D reconstruction based on the image processing, to the basic modelling and simulation approaches. This framework is demonstrated on human knee joint virtual model construction. Statistical assessment of the built knee joint model points out sufficient quality and accuracy. Model assessment from clinical point of view confirmed this evaluation, and, simultaneously, verified the proposed construction chain as very prospective. [ABSTRACT FROM AUTHOR]

The ingestion of hydrolysable tannins as a potential nutrient to reduce boar odor in entire males results in the significant enlargement of parotid glands (parotidomegaly). The objective of this study was to characterize the effects of different levels of hydrolysable tannins in the diet of fattening boars (n = 24) on salivary gland morphology and proline-rich protein (PRP) expression at the histological level. Four treatment groups of pigs (n = 6 per group) were fed either a control (T0) or experimental diet, where the T0 diet was supplemented with 1% (T1), 2% (T2), or 3% (T3) of the hydrolysable tannin-rich extract Farmatan ® . After slaughter, the parotid and mandibular glands of the experimental pigs were harvested and dissected for staining using Goldner's Trichrome method, and immunohistochemical studies with antibodies against PRPs. Morphometric analysis was performed on microtome sections of both salivary glands, to measure the acinar area, the lobular area, the area of the secretory ductal cells, and the sizes of glandular cells and their nuclei. Histological assessment revealed that significant parotidomegaly was only present in the T3 group, based on the presence of larger glandular lobules, acinar areas, and their higher nucleus to cytoplasm ratio. The immunohistochemical method, supported by color intensity measurements, indicated significant increases in basic PRPs (PRB2) in the T3 and acidic PRPs (PRH1/2) in the T1 groups. Tannin supplementation did not affect the histo-morphological properties of the mandibular gland. This study confirms that pigs can adapt to a tannin-rich diet by making structural changes in their parotid salivary gland, indicating its higher functional activity.