A prospective study on the usefulness of high-resolution intraoperative infrared thermography in intracranial tumors.

Introduction: Infrared thermography (IT) is a non-invasive real-time imaging technique with potential application in different areas of neurosurgery. Despite technological advances in the field, intraoperative IT (IIT) has been an underestimated tool with scarce reports on its usefulness during intracranial tumor resection. We aimed to evaluate the usefulness of high-resolution IIT with static and dynamic thermographic maps for transdural lesion localization, and diagnosis, to assess the extent of resection, and the occurrence of perioperative acute ischemia. Methods: In a prospective study, 15 patients affected by intracranial tumors (six gliomas, four meningiomas, and five brain metastases) were examined with a high-resolution thermographic camera after craniotomy, after dural opening, and at the end of tumor resection. Results: Tumors were transdurally located with 93.3% sensitivity and 100% specificity (p < 0.00001), as well as cortical arteries and veins. Gliomas were consistently hypothermic, while metastases and meningiomas exhibited highly variable thermographic maps on static (p = 0.055) and dynamic (p = 0.015) imaging. Residual tumors revealed non-specific static but characteristic dynamic thermographic maps. Ischemic injuries were significantly hypothermic (p < 0.001). Conclusions: High-resolution IIT is a non-invasive alternative intraoperative imaging method for lesion localization, diagnosis, assessing the extent of tumor resection, and identifying acute ischemia changes with static and dynamic thermographic maps.

A computing platform to analyze breast abnormalities using infrared images.

The present work shows a computational tool developed in the MATLAB platform. Its main functionality is to evaluate a thermal model of the breast. This computational infrastructure consists of modules in which manipulate the infrared images and calculate breast temperature profiles. It also allows the analysis of breast nodules. The different modules of the framework are interconnected through an interface which the major purpose is to automatize the whole process of the infrared image analysis, in a quick and organized way. The tool is initially supplied with a three-dimensional mesh that represents the substitute geometry of the patient's breast together with her infrared images which are transformed into temperature matrices. Through these matrices, the frontal and lateral mappings are performed by specified modules. This process generates an image and a text file with all the temperatures associated to the nodes of the surface mesh. The developed tool is also able to manage the use of a commercial mesh generation program and a computational fluid dynamics code, the FLUENT, in order to validate the technique by the use of a parametric analysis. In these analyses, the tumor may have several geometric shapes and different locations within the breast.

Dental arch definition in computed tomographs using two semi-automatic methods.

Computed tomography is a widely used image examination in dental imaging that provides an accurate location of oral structures and features, including the dental arch, which is an important anatomical feature. This study proposes two new semi-automatic methods for arch definition in CTs, with minimal user effort. This study includes 25 CT examinations. The first method is based on the teeth pulps, and the second one is based on the whole mandible. The methods use thresholding and morphological operations to obtain the arches. The evaluation process includes two different metrics DTW and IoU. For both metrics, the initial results of M1 were very low, but the average performance of M2 can be considered high. The analysis showed that changing the input improves the M1 results substantially. The promising results presented here suggest that these methods can be used as auxiliary tools for the proposed task.

Multivariate data driven prediction of COVID-19 dynamics: Towards new results with temperature, humidity and air quality data.

Since the start of the COVID-19 pandemic many studies investigated the correlation between climate variables such as air quality, humidity and temperature and the lethality of COVID-19 around the world. In this work we investigate the use of climate variables, as additional features to train a data-driven multivariate forecast model to predict the short-term expected number of COVID-19 deaths in Brazilian states and major cities. The main idea is that by adding these climate features as inputs to the training of data-driven models, the predictive performance improves when compared to equivalent single input models. We use a Stacked LSTM as the network architecture for both the multivariate and univariate model. We compare both approaches by training forecast models for the COVID-19 deaths time series of the city of São Paulo. In addition, we present a previous analysis based on grouping K-means on AQI curves. The results produced will allow achieving the application of transfer learning, once a locality is eventually added to the task, regressing out using a model based on the cluster of similarities in the AQI curve. The experiments show that the best multivariate model is more skilled than the best standard data-driven univariate model that we could find, using as evaluation metrics the average fitting error, average forecast error, and the profile of the accumulated deaths for the forecast. These results show that by adding more useful features as input to a multivariate approach could further improve the quality of the prediction models.

Classification of Approximal Caries in Bitewing Radiographs Using Convolutional Neural Networks.

Dental caries is an extremely common problem in dentistry that affects a significant part of the population. Approximal caries are especially difficult to identify because their position makes clinical analysis difficult. Radiographic evaluation-more specifically, bitewing images-are mostly used in such cases. However, incorrect interpretations may interfere with the diagnostic process. To aid dentists in caries evaluation, computational methods and tools can be used. In this work, we propose a new method that combines image processing techniques and convolutional neural networks to identify approximal dental caries in bitewing radiographic images and classify them according to lesion severity. For this study, we acquired 112 bitewing radiographs. From these exams, we extracted individual tooth images from each exam, applied a data augmentation process, and used the resulting images to train CNN classification models. The tooth images were previously labeled by experts to denote the defined classes. We evaluated classification models based on the Inception and ResNet architectures using three different learning rates: 0.1, 0.01, and 0.001. The training process included 2000 iterations, and the best results were achieved by the Inception model with a 0.001 learning rate, whose accuracy on the test set was 73.3%. The results can be considered promising and suggest that the proposed method could be used to assist dentists in the evaluation of bitewing images, and the definition of lesion severity and appropriate treatments.

Pulmonary COVID-19: Learning Spatiotemporal Features Combining CNN and LSTM Networks for Lung Ultrasound Video Classification.

Deep Learning is a very active and important area for building Computer-Aided Diagnosis (CAD) applications. This work aims to present a hybrid model to classify lung ultrasound (LUS) videos captured by convex transducers to diagnose COVID-19. A Convolutional Neural Network (CNN) performed the extraction of spatial features, and the temporal dependence was learned using a Long Short-Term Memory (LSTM). Different types of convolutional architectures were used for feature extraction. The hybrid model (CNN-LSTM) hyperparameters were optimized using the Optuna framework. The best hybrid model was composed of an Xception pre-trained on ImageNet and an LSTM containing 512 units, configured with a dropout rate of 0.4, two fully connected layers containing 1024 neurons each, and a sequence of 20 frames in the input layer (20×2018). The model presented an average accuracy of 93% and sensitivity of 97% for COVID-19, outperforming models based purely on spatial approaches. Furthermore, feature extraction using transfer learning with models pre-trained on ImageNet provided comparable results to models pre-trained on LUS images. The results corroborate with other studies showing that this model for LUS classification can be an important tool in the fight against COVID-19 and other lung diseases.

A Computational Study on the Role of Parameters for Identification of Thyroid Nodules by Infrared Images (and Comparison with Real Data).

According to experts and medical literature, healthy thyroids and thyroids containing benign nodules tend to be less inflamed and less active than those with malignant nodules. It seems to be a consensus that malignant nodules have more blood veins and more blood circulation. This may be related to the maintenance of the nodule's heat at a higher level compared with neighboring tissues. If the internal heat modifies the skin radiation, then it could be detected by infrared sensors. The goal of this work is the investigation of the factors that allow this detection, and the possible relation with any pattern referent to nodule malignancy. We aim to consider a wide range of factors, so a great number of numerical simulations of the heat transfer in the region under analysis, based on the Finite Element method, are performed to study the influence of each nodule and patient characteristics on the infrared sensor acquisition. To do so, the protocol for infrared thyroid examination used in our university's hospital is simulated in the numerical study. This protocol presents two phases. In the first one, the body under observation is in steady state. In the second one, it is submitted to thermal stress (transient state). Both are simulated in order to verify if it is possible (by infrared sensors) to identify different behavior referent to malignant nodules. Moreover, when the simulation indicates possible important aspects, patients with and without similar characteristics are examined to confirm such influences. The results show that the tissues between skin and thyroid, as well as the nodule size, have an influence on superficial temperatures. Other thermal parameters of thyroid nodules show little influence on surface infrared emissions, for instance, those related to the vascularization of the nodule. All details of the physical parameters used in the simulations, characteristics of the real nodules and thermal examinations are publicly available, allowing these simulations to be compared with other types of heat transfer solutions and infrared examination protocols. Among the main contributions of this work, we highlight the simulation of the possible range of parameters, and definition of the simulation approach for mapping the used infrared protocol, promoting the investigation of a possible relation between the heat transfer process and the data obtained by infrared acquisitions.

Combining Genetic Algorithms and SVM for Breast Cancer Diagnosis Using Infrared Thermography.

Breast cancer is one of the leading causes of mortality globally, but early diagnosis and treatment can increase the cancer survival rate. In this context, thermography is a suitable approach to help early diagnosis due to the temperature difference between cancerous tissues and healthy neighboring tissues. This work proposes an ensemble method for selecting models and features by combining a Genetic Algorithm (GA) and the Support Vector Machine (SVM) classifier to diagnose breast cancer. Our evaluation demonstrates that the approach presents a significant contribution to the early diagnosis of breast cancer, presenting results with 94.79% Area Under the Receiver Operating Characteristic Curve and 97.18% of Accuracy.

Hyperparameter Optimization for COVID-19 Pneumonia Diagnosis Based on Chest CT.

Convolutional Neural Networks (CNNs) have been successfully applied in the medical diagnosis of different types of diseases. However, selecting the architecture and the best set of hyperparameters among the possible combinations can be a significant challenge. The purpose of this work is to investigate the use of the Hyperband optimization algorithm in the process of optimizing a CNN applied to the diagnosis of SARS-Cov2 disease (COVID-19). The test was performed with the Optuna framework, and the optimization process aimed to optimize four hyperparameters: (1) backbone architecture, (2) the number of inception modules, (3) the number of neurons in the fully connected layers, and (4) the learning rate. CNNs were trained on 2175 computed tomography (CT) images. The CNN that was proposed by the optimization process was a VGG16 with five inception modules, 128 neurons in the two fully connected layers, and a learning rate of 0.0027. The proposed method achieved a sensitivity, precision, and accuracy of 97%, 82%, and 88%, outperforming the sensitivity of the Real-Time Polymerase Chain Reaction (RT-PCR) tests (53-88%) and the accuracy of the diagnosis performed by human experts (72%).

Do Radiographic Assessments of Periodontal Bone Loss Improve with Deep Learning Methods for Enhanced Image Resolution?

Resolution plays an essential role in oral imaging for periodontal disease assessment. Nevertheless, due to limitations in acquisition tools, a considerable number of oral examinations have low resolution, making the evaluation of this kind of lesion difficult. Recently, the use of deep-learning methods for image resolution improvement has seen an increase in the literature. In this work, we performed two studies to evaluate the effects of using different resolution improvement methods (nearest, bilinear, bicubic, Lanczos, SRCNN, and SRGAN). In the first one, specialized dentists visually analyzed the quality of images treated with these techniques. In the second study, we used those methods as different pre-processing steps for inputs of convolutional neural network (CNN) classifiers (Inception and ResNet) and evaluated whether this process leads to better results. The deep-learning methods lead to a substantial improvement in the visual quality of images but do not necessarily promote better classifier performance.

Application of thermography in the diagnostic investigation of thyroid nodules.

Thyroid nodules (TN) are common in the general population, and the clinical importance of diagnosing thyroid nodules is based on excluding the possibility of thyroid cancer, which occurs in 7-15% of cases. The thyroid gland, owing to its superficial location, is easily accessible via thermography, a noninvasive method of recording body temperature that measures infrared radiation emitted by the body surface. Therefore, this study aimed to evaluate the temperature differences between benign and malignant TN by using thermography. We conducted a cross-sectional study where 147 TN were divided into two groups: the first group included 120 benign nodules and the other included 27 malignant nodules. All the nodules were subjected to ultrasound, fine needle aspiration biopsy, and thermography. On analyzing the thermography results, the benign nodules had a higher temperature at the beginning of the thermography evaluation, and the malignant nodules showed a higher temperature in the middle and at the end (Ft). Using the relationships, it was observed that the temperature delta (ΔT), ΔT nodule/ΔT healthy, ΔT nodule minus ΔT healthy, and nodule Ft minus Ft of the healthy region were higher in malignant nodules. The ROC curve analysis of ΔT demonstrated a cutoff point of 2.38°C, with a sensitivity of 0.963 and specificity of 0.992. Malignant nodules have higher temperatures than benign nodules on thermographic evaluation. This finding suggests that thermography can be a useful tool in the diagnosis of thyroid nodules.

A Computational Method to Assist the Diagnosis of Breast Disease Using Dynamic Thermography.

Breast cancer has been the second leading cause of cancer death among women. New techniques to enhance early diagnosis are very important to improve cure rates. This paper proposes and evaluates an image analysis method to automatically detect patients with breast benign and malignant changes (tumors). Such method explores the difference of Dynamic Infrared Thermography (DIT) patterns observed in patients' skin. After obtaining the sequential DIT images of each patient, their temperature arrays are computed and new images in gray scale are generated. Then the regions of interest (ROIs) of those images are segmented and, from them, arrays of the ROI temperature are computed. Features are extracted from the arrays, such as the ones based on statistical, clustering, histogram comparison, fractal geometry, diversity indices and spatial statistics. Time series that are broken down into subsets of different cardinalities are generated from such features. Automatic feature selection methods are applied and used in the Support Vector Machine (SVM) classifier. In our tests, using a dataset of 68 images, 100% accuracy was achieved.

An Infrared Thermal Images Database and a New Technique for Thyroid Nodules Analysis.

Thyroid nodules diseases are a common health problem and thyroidal cancer is becoming increasingly prevalent. They appear in the neck and bottom neck region, superficially over the trachea. Cancer tissues are characterized by higher temperatures than surrounding tissues. Thermography is a diagnostic tool increasingly used to detect cancer and abnormalities. Artificial intelligence is an approach which can be applied to thyroid nodules classification, but is necessary to have a proper number of cases with proven diagnosis. In this paper, a new database that contain infrared thermal images, clinical and physiological data is presented. The description of each nodule per patient, and the acquisition protocol (based on Dynamic Infrared Thermography approach) is considered as well. A semi-automatic method for image registration was implemented to pre-process the thermograms and a new method for the Region of Interest (ROI) extraction is proposed. Moreover, the obtained ROI results are confirmed by medical specialists and turned available for future comparison with other works.

On the Automated Segmentation of Epicardial and Mediastinal Cardiac Adipose Tissues Using Classification Algorithms.

The quantification of fat depots on the surroundings of the heart is an accurate procedure for evaluating health risk factors correlated with several diseases. However, this type of evaluation is not widely employed in clinical practice due to the required human workload. This work proposes a novel technique for the automatic segmentation of cardiac fat pads. The technique is based on applying classification algorithms to the segmentation of cardiac CT images. Furthermore, we extensively evaluate the performance of several algorithms on this task and discuss which provided better predictive models. Experimental results have shown that the mean accuracy for the classification of epicardial and mediastinal fats has been 98.4% with a mean true positive rate of 96.2%. On average, the Dice similarity index, regarding the segmented patients and the ground truth, was equal to 96.8%. Therfore, our technique has achieved the most accurate results for the automatic segmentation of cardiac fats, to date.

Thermal Signal Analysis for Breast Cancer Risk Verification.

Breast cancer is the second most common cancer in the world. Currently, there are no effective methods to prevent this disease. However, early diagnosis increases chances of remission. Breast thermography is an option to be considered in screening strategies. This paper proposes a new dynamic breast thermography analysis technique in order to identify patients at risk for breast cancer. Thermal signals from patients of the Antonio Pedro University Hospital (HUAP), available at the Mastology Database for Research with Infrared Image - DMR-IR were used to validate the study. First, each patient's images are registered. Then, the breast region is divided into subregions of 3x3 pixels and the average temperature from each of these regions is observed in all images of the same patient. Features of the thermal signals of such subregions are calculated. Then, the k-means algorithm is applied over feature vectors building two clusters. Silhouette index, Davies-Bouldin index and Calinski-Harabasz index are applied to evaluate the clustering. The test results showed that the methodology presented in this paper is able to identify patients with breast cancer. Classification techniques have been applied on the index values and 90.90% hit rate has been achieved.

Uso de ontologias para a manipulação de imagens relacionadas ao câncer de mama / Using Ontologies for Handling Images Related to Breast Cancer

Em um cenário no qual é crescente a quantidade de fontes de dados disponíveis, a Web Semântica vem desempenhando um papel fundamental para o compartilhamento, recuperação, seleção e combinação de dados armazenados nos mais variados formatos. O armazenamento e recuperação de imagens médicas também se beneficia da aplicação destas tecnologias. Neste trabalho apresentamos um estudo sistemático de trabalhos que utilizam ontologias como ferramenta para a manipulação em imagens médicas relacionadas ao câncer de mama, descrevendo as principais características de sistemas que as utilizam

In a scenario in which there is a growing amount of available data sources, the Semantic Web has played a key role in the sharing, retrieval, selection and combination of data stored in various formats. The storage and retrieval of medical images also benefits from the application of these technologies. In this work we present a systematic study of works that use ontologies as a tool for manipulating medical images related to breast cancer, describing the main characteristics of systems that use such tool

Quantitative ventricular neuroendoscopy performed on the third ventriculostomy: anatomic study.

BACKGROUND: One of the key elements for a successful endoscopic intervention in the ventricular system is the ability to recognize the anatomic structures and use them as a reference. OBJECTIVE: To measure the choroid plexus with endoscopy in the interventricular foramen, together with the structures on the third ventricle floor, and to compare these variables. METHODS: An observational prospective study was carried out on 37 brains of cadavers for which the cause of death was assessed at the Death Check Unit of the University of São Paulo in April 2008. This study was done on adults of both sexes with a rigid neuroendoscope. Endoscopic images were recorded, submitted for correction of distortion, and then measured. RESULTS: The measurements of the choroid plexus in the interventricular foramen, laterolateral distance of mammillary bodies, distance from the infundibular recess to the mammillary bodies, and area of the triangle in the tuber cinereum were 1.71 ± 0.77 mm, 2.23 ± 0.74 mm, 3.22 ± 0.82 mm, and 3.69 ± 2.09 mm, respectively. The ventricle floor was opaque in 84% of cases. The internal distance of mammillary bodies was absent in 89%. Associations between the translucent floor of the third ventricle and laterolateral distance of mammillary bodies, internal distance of mammillary bodies, and age were identified. CONCLUSION: Before this research, there was no record of the measurements of the choroid plexus in the interventricular foramen. The remaining variables of the present study show a greater number in normal brains compared with others.

Manipulaçäo e modelagem númerica do envelhecimento frontal da face / Numerical manipulation and modeling of face frontal aging

Neste trabalho apresenta-se uma metodologia numérica para obtençäo de parâmetros de envelhecimento/rejuvenecimento. Estes parâmetros säo manipulados em imagens fotográficas, através de Computaçäo Gráfica usando uma t:cnica de warping

Abstract - ln this work we present a numerical methodology to obtain ageing parameters. Those parameters are manipulated in photographic images. using a Computer Graphic technique of warping