Automatic method based on deep learning to identify and account Rhipicephalus microplus larval hatching.

Reports of Rhipicephalus microplus resistant populations worldwide have increased extensively, making it difficult to control this ectoparasite. The adult immersion test, commonly used to screen for acaricide resistance, produces the results only after 40 days of the tick collection because it needs the eggs to be laid and larvae to hatch. The present study aims to develop an automatic method, based on deep learning, to predict the hatching of R.

Computational method for aid in the diagnosis of sixth optic nerve palsy through digital videos.

The sixth cranial nerve, also known as the abducens nerve, is responsible for controlling the movements of the lateral rectus muscle. Palsies on the sixth nerve prevent some muscles that control eye movements from proper functioning, causing headaches, migraines, blurred vision, vertigo, and double vision. Hence, such palsy should be diagnosed in the early stages to treat it without leaving any sequela.

Development and validation of software that quantifies the larval mortality of Rhipicephalus (Boophilus) microplus cattle tick.

Rhipicephalus (Boophilus) microplus is controlled almost exclusively using synthetic acaricides, and reports of resistant populations have been described worldwide. Several time-consuming and laborious toxicological in vitro tests have been used to diagnose acaricidal resistance, especially those that require differential counting of live and dead larvae. Larval mortality is currently done manually and subjectively, which can limit the performance of a large number of tests and comparing results between different laboratories.

Liver segmentation from computed tomography images using cascade deep learning.

Background: Liver segmentation is a fundamental step in the treatment planning and diagnosis of liver cancer. However, manual segmentation of liver is time-consuming because of the large slice quantity and subjectiveness associated with the specialist's experience, which can lead to segmentation errors. Thus, the segmentation process can be automated using computational methods for better time efficiency and accuracy.

Bayesian convolutional neural network estimation for pediatric pneumonia detection and diagnosis.

Background And Objectives: Pneumonia is a disease that affects the lungs, making breathing difficult. Nowadays, pneumonia is the disease that kills the most children under the age of five in the world, and if no action is taken, pneumonia is estimated to kill 11 million children by the year 2030. Knowing that rapid and accurate diagnosis of pneumonia is a significant factor in reducing mortality, acceleration, or automation of the diagnostic process is highly desirable.

Segmentation and quantification of COVID-19 infections in CT using pulmonary vessels extraction and deep learning.

At the end of 2019, the World Health Organization (WHO) reported pneumonia that started in Wuhan, China, as a global emergency problem. Researchers quickly advanced in research to try to understand this COVID-19 and sough solutions for the front-line professionals fighting this fatal disease. One of the tools to aid in the detection, diagnosis, treatment, and prevention of this disease is computed tomography (CT).

Automatic method for classifying COVID-19 patients based on chest X-ray images, using deep features and PSO-optimized XGBoost.

The COVID-19 pandemic, which originated in December 2019 in the city of Wuhan, China, continues to have a devastating effect on the health and well-being of the global population. Currently, approximately 8.8 million people have already been infected and more than 465,740 people have died worldwide.

Surgical planning of horizontal strabismus using multiple output regression tree.

Strabismus is an eye disease that affects about 0.12%-9.86% of the population, which can cause irreversible sensory damage to vision and psychological problems.

Tear Film Classification in Interferometry Eye Images Using Phylogenetic Diversity Indexes and Ripley's K Function.

Dry eye syndrome is one of the most frequently reported eye diseases in ophthalmological practice. The diagnosis of this disease is a challenging task due to its multifactorial etiology. One of the most applied tests is the manual classification of tear film images captured with the Doane interferometer.

Esophagus segmentation from planning CT images using an atlas-based deep learning approach.

Background And Objective: One of the main steps in the planning of radiotherapy (RT) is the segmentation of organs at risk (OARs) in Computed Tomography (CT). The esophagus is one of the most difficult OARs to segment. The boundaries between the esophagus and other surrounding tissues are not well-defined, and it is presented in several slices of the CT.

Kidney segmentation from computed tomography images using deep neural network.

Background: The precise segmentation of kidneys and kidney tumors can help medical specialists to diagnose diseases and improve treatment planning, which is highly required in clinical practice. Manual segmentation of the kidneys is extremely time-consuming and prone to variability between different specialists due to their heterogeneity. Because of this hard work, computational techniques, such as deep convolutional neural networks, have become popular in kidney segmentation tasks to assist in the early diagnosis of kidney tumors.

Superpixel-based deep convolutional neural networks and active contour model for automatic prostate segmentation on 3D MRI scans.

Automatic and reliable prostate segmentation is an essential prerequisite for assisting the diagnosis and treatment, such as guiding biopsy procedure and radiation therapy. Nonetheless, automatic segmentation is challenging due to the lack of clear prostate boundaries owing to the similar appearance of prostate and surrounding tissues and the wide variation in size and shape among different patients ascribed to pathological changes or different resolutions of images. In this regard, the state-of-the-art includes methods based on a probabilistic atlas, active contour models, and deep learning techniques.

Breast cancer diagnosis from histopathological images using textural features and CBIR.

Currently, breast cancer diagnosis is an extensively researched topic. An effective method to diagnose breast cancer is to use histopathological images. However, extracting features from these images is a challenging task.

Interferometer eye image classification for dry eye categorization using phylogenetic diversity indexes for texture analysis.

Background and Objective Dry eye syndrome disease negatively impacts many people in various ways. Several tests are required to diagnose it for evaluating different physiological characteristics. One of the most applied tests for this is the manual classification of tear film images captured with Doane interferometer.

Solid Indeterminate Nodules with a Radiological Stability Suggesting Benignity: A Texture Analysis of Computed Tomography Images Based on the Kurtosis and Skewness of the Nodule Volume Density Histogram.

Background: The number of incidental findings of pulmonary nodules using imaging methods to diagnose other thoracic or extrathoracic conditions has increased, suggesting the need for in-depth radiological image analyses to identify nodule type and avoid unnecessary invasive procedures.

Objectives: The present study evaluated solid indeterminate nodules with a radiological stability suggesting benignity (SINRSBs) through a texture analysis of computed tomography (CT) images.

Methods: A total of 100 chest CT scans were evaluated, including 50 cases of SINRSBs and 50 cases of malignant nodules.

Pathophysiological mapping of tumor habitats in the breast in DCE-MRI using molecular texture descriptor.

Background: We propose a computational methodology capable of detecting and analyzing breast tumor habitats in images acquired by magnetic resonance imaging with dynamic contrast enhancement (DCE-MRI), based on the pathophysiological behavior of the contrast agent (CA).

Methods: The proposed methodology comprises three steps. In summary, the first step is the acquisition of images from the Quantitative Imaging Network Breast.

Correction to: Semivariogram and Semimadogram functions as descriptors for AMD diagnosis on SD-OCT topographic maps using Support Vector Machine.

After publication, it was highlighted that the original publication [1] contained a spelling mistake in the first name of Marcelo Gattas. This was incorrectly captured as Marelo Gattass in the original article which has since been updated.

Modified Quality Threshold Clustering for Temporal Analysis and Classification of Lung Lesions.

Lung cancer is the type of cancer that most often kills after the initial diagnosis. To aid the specialist in its diagnosis, temporal evaluation is a potential tool for analyzing indeterminate lesions, which may be benign or malignant, during treatment. With this goal in mind, a methodology is herein proposed for the analysis, quantification, and visualization of changes in lung lesions.

Semivariogram and Semimadogram functions as descriptors for AMD diagnosis on SD-OCT topographic maps using Support Vector Machine.

Background: Age-related macular degeneration (AMD) is a degenerative ocular disease that develops by the formation of drusen in the macula region leading to blindness. This condition can be detected automatically by automated image processing techniques applied in spectral domain optical coherence tomography (SD-OCT) volumes. The most common approach is the individualized analysis of each slice (B-Scan) of the SD-OCT volumes.

Computer-Aided Diagnosis of Lung Nodules in Computed Tomography by Using Phylogenetic Diversity, Genetic Algorithm, and SVM.

Lung cancer is pointed as the major cause of death among patients with cancer throughout the world. This work is intended to develop a methodology for diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques.

Automatic diagnosis of strabismus in digital videos through cover test.

Background And Objective: Medical image processing can contribute to the detection and diagnosis of human body anomalies, and it represents an important tool to assist in minimizing the degree of uncertainty of any diagnosis, while providing specialists with an additional source of diagnostic information. Strabismus is an anomaly that affects approximately 4% of the population. Strabismus modifies vision such that the eyes do not properly align, influencing binocular vision and depth perception.