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. However, automatic liver segmentation is a challenging task, as the liver can vary in shape, ill-defined borders, and lesions, which affect its appearance. We aim to propose an automatic method for liver segmentation using computed tomography (CT) images. METHODS: The proposed method, based on deep convolutional neural network models and image processing techniques, comprise of four main steps: (1) image preprocessing, (2) initial segmentation, (3) reconstruction, and (4) final segmentation. RESULTS: We evaluated the proposed method using 131 CT images from the LiTS image base. An average sensitivity of 95.45%, an average specificity of 99.86%, an average Dice coefficient of 95.64%, an average volumetric overlap error (VOE) of 8.28%, an average relative volume difference (RVD) of -0.41%, and an average Hausdorff distance (HD) of 26.60 mm were achieved. CONCLUSIONS: This study demonstrates that liver segmentation, even when lesions are present in CT images, can be efficiently performed using a cascade approach and including a reconstruction step based on deep convolutional neural networks.

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. An important step in combating COVID-19 is the screening of infected patients using chest X-ray (CXR) images. However, this task is extremely time-consuming and prone to variability among specialists owing to its heterogeneity. Therefore, the present study aims to assist specialists in identifying COVID-19 patients from their chest radiographs, using automated computational techniques. The proposed method has four main steps: (1) the acquisition of the dataset, from two public databases; (2) the standardization of images through preprocessing; (3) the extraction of features using a deep features-based approach implemented through the networks VGG19, Inception-v3, and ResNet50; (4) the classifying of images into COVID-19 groups, using eXtreme Gradient Boosting (XGBoost) optimized by particle swarm optimization (PSO). In the best-case scenario, the proposed method achieved an accuracy of 98.71%, a precision of 98.89%, a recall of 99.63%, and an F1-score of 99.25%. In our study, we demonstrated that the problem of classifying CXR images of patients under COVID-19 and non-COVID-19 conditions can be solved efficiently by combining a deep features-based approach with a robust classifier (XGBoost) optimized by an evolutionary algorithm (PSO). The proposed method offers considerable advantages for clinicians seeking to tackle the current COVID-19 pandemic.

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. The interference phenomena in these images can be characterized as texture patterns, which can be automatically classified into one of the following categories: strong fringes, coalescing strong fringes, fine fringes, coalescing fine fringes, and debris. This work presents a method for classifying tear film images based on texture analysis using phylogenetic diversity indexes and Ripley's K function. The proposed method consists of six main steps: acquisition of the image dataset; segmentation of the region of interest; feature extraction using phylogenetic diversity indexes and Ripley's K function; feature selection using Greedy Stepwise; classification using the algorithms Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), Multilayer Perceptron (MLP), Random Tree (RT) and Radial Basis Function Network (RBFNet); and (6) validation of results. The best result, using the RF classifier, we obtained classification rates higher than 99% of accuracy with 0.843% of standard deviation, 0.999 of the area under the Receiver Operating Characteristics (ROC) curve, 0.995 of Kappa and 0.996 of F-Measure. The experimental results demonstrate that the proposed method is promising and can potentially be used by experts to accurately diagnose dry eye syndrome in tear film images.

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. In this study, we propose an automatic method to delimit the kidneys in computed tomography (CT) images using image processing techniques and deep convolutional neural networks (CNNs) to minimize false positives. METHODS: The proposed method has four main steps: (1) acquisition of the KiTS19 dataset, (2) scope reduction using AlexNet, (3) initial segmentation using U-Net 2D, and (4) false positive reduction using image processing to maintain the largest elements (kidneys). RESULTS: The proposed method was evaluated in 210 CTs from the KiTS19 database and obtained the best result with an average Dice coefficient of 96.33%, an average Jaccard index of 93.02%, an average sensitivity of 97.42%, an average specificity of 99.94% and an average accuracy of 99.92%. In the KiTS19 challenge, it presented an average Dice coefficient of 93.03%. CONCLUSION: In our method, we demonstrated that the kidney segmentation problem in CT can be solved efficiently using deep neural networks to define the scope of the problem and segment the kidneys with high precision and with the use of image processing techniques to reduce false positives.

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. Interferometry images can be categorized into five groups: debris, fine fringes, coalescing fine fringes, strong fringes, and coalescing strong fringes. Instability in the tear film creates the need for an automatic system to provide experts with diagnostic support. Therefore, the purpose of this study was to propose a method for automatic classification of the tear film lipid layer using phylogenetic diversity indexes for feature extraction and several classifiers. Methods The proposed method consisted of five main steps: (1) acquisition of VOPTICAL_GCU image dataset, (2) segmentation of the region of interest, (3) feature extraction using phylogenetic diversity indexes, (4) classification using the algorithms Support Vector Machines, Random Forest, Naive Bayes, Multilayer Perceptron, Random Tree, and RBFNetwork, and, (5) validation of results. Results The best result was obtained using Random Forest classifier, reaching an accuracy of over 97%, standard deviation of 0.51%, an area under the receiver operating characteristic curve of 0.99, a Kappa index of 0.96, and an F-Measure of 0.97. Conclusions The proposed method demonstrated that the tear film lipid layer classification problem can be resolved efficiently by using phylogenetic diversity indexes.