A 3D Radiomics-Based Artificial Neural Network Model for Benign Versus Malignant Vertebral Compression Fracture Classification in MRI.

To train an artificial neural network model using 3D radiomic features to differentiate benign from malignant vertebral compression fractures (VCFs) on MRI. This retrospective study analyzed sagittal T1-weighted lumbar spine MRIs from 91 patients (average age of 64.24 ± 11.75 years) diagnosed with benign or malignant VCFs from 2010 to 2019, of them 47 (51.6%) had benign VCFs and 44 (48.4%) had malignant VCFs. The lumbar fractures were three-dimensionally segmented and had their radiomic features extracted and selected with the wrapper method. The training set consisted of 100 fractured vertebral bodies from 61 patients (average age of 63.2 ± 12.5 years), and the test set was comprised of 30 fractured vertebral bodies from 30 patients (average age of 66.4 ± 9.9 years). Classification was performed with the multilayer perceptron neural network with a back-propagation algorithm. To validate the model, the tenfold cross-validation technique and an independent test set (holdout) were used. The performance of the model was evaluated using the average with a 95% confidence interval for the ROC AUC, accuracy, sensitivity, and specificity (considering the threshold = 0.5). In the internal validation test, the best model reached a ROC AUC of 0.98, an accuracy of 95% (95/100), a sensitivity of 93.5% (43/46), and specificity of 96.3% (52/54). In the validation with independent test set, the model achieved a ROC AUC of 0.97, an accuracy of 93.3% (28/30), a sensitivity of 93.3% (14/15), and a specificity of 93.3% (14/15). The model proposed in this study using radiomic features could differentiate benign from malignant vertebral compression fractures with excellent performance and is promising as an aid to radiologists in the characterization of VCFs.

Enhancing vessel visibility in fundus images to aid the diagnosis of retinopathy of prematurity.

Retinopathy of prematurity is a disease that can affect premature or in similar conditions babies. For diagnosing of retinopathy of prematurity, the infant is examined as soon as possible. Due to the nature of the examination, the images obtained are poor in quality. This article presents an automated method for processing fundus images to improve the visibility of the vascular network. The method includes several processing tasks whose parameters are predicted using an artificial neural network. A set of 88 clinical images were used in this work. The performance of our proposal is efficient, and the average processing time was 42 ms. The method was assessed using both the contrast improvement index and expert opinions. The contrast improvement index average was 2; this means the processed image successfully improved its contrast. Three pediatric ophthalmologists validated the proposed method and agreed that the visual enhancement can help observe clearly the retinal vessels.

Algoritmo para la identificación de nódulos pulmonares solitarios en imágenes de tomografía de tórax / Algorithm for identification of solitary pulmonary nodules in chest tomography scan images

La identificación del cáncer de pulmón en fases iniciales ha sido en los últimos años una tarea priorizada de la comunidad científica. Esta enfermedad representa la primera causa de muerte en el varón y la tercera después del cáncer de colon y mama en la mujer. La realización de estudios imagenológicos contribuye a la detección temprana de esta enfermedad. El elevado volumen de imágenes generado por los equipos médicos provoca la revisión de mucha información para emitir un diagnóstico médico. Con frecuencia se requiere la valoración de varios especialistas para llegar a un diagnóstico acertado, retardando el proceso de atención al paciente. En la presente investigación se exponen los resultados obtenidos al desarrollar un algoritmo utilizando métodos de procesamiento de imágenes, para la identificación de nódulos pulmonares solitarios. La utilización de sistemas que dirigen la atención de los especialistas a regiones candidatas en la imagen, proporcionando una segunda opinión en la interpretación de los resultados, pudiera mejorar la consistencia y agilizar el proceso de diagnóstico. Los resultados arrojados por el algoritmo desarrollado fueron contrastados con las anotaciones realizadas en imágenes publicadas en The Lung Image Database Consortium Image Collection (LIDC-IDRI) y se obtuvo un 77.78 % de acierto en la detección de nódulos pulmonares solitarios(AU)

The identification of lung cancer at early stages has been in recent years a prioritized task for the scientific community. This disease is the leading cause of death in men and the third after the colon and breast cancer in women. Performing imaging studies contributes to the early detection of this disease. The high volume of images generated by medical equipment leads to reviewing much information to issue a medical diagnosis. Often are required the assessment of several specialists to reach an accurate diagnosis, slowing the process of patient care. In the present investigation are exposed the results obtained to develop an algorithm using image processing methods for the identification of solitary pulmonary nodules. The use of systems that direct the attention of specialists to candidate regions in the image, providing a second opinion in the interpretation of results could improve consistency and agility in the diagnostic process. The results obtained by the developed algorithm were compared with annotations in images published in The Lung Image Database Consortium Image Collection (LIDC-IDRI) and was obtained 77.78 % accuracy in the detection of solitary pulmonary nodules(AU)

Algoritmo para la identificación de nódulos pulmonares solitarios en imágenes de tomografía de tórax / Algorithm for identification of solitary pulmonary nodules in chest tomography scan images

La identificación del cáncer de pulmón en fases iniciales ha sido en los últimos años una tarea priorizada de la comunidad científica. Esta enfermedad representa la primera causa de muerte en el varón y la tercera después del cáncer de colon y mama en la mujer. La realización de estudios imagenológicos contribuye a la detección temprana de esta enfermedad. El elevado volumen de imágenes generado por los equipos médicos provoca la revisión de mucha información para emitir un diagnóstico médico. Con frecuencia se requiere la valoración de varios especialistas para llegar a un diagnóstico acertado, retardando el proceso de atención al paciente. En la presente investigación se exponen los resultados obtenidos al desarrollar un algoritmo utilizando métodos de procesamiento de imágenes, para la identificación de nódulos pulmonares solitarios. La utilización de sistemas que dirigen la atención de los especialistas a regiones candidatas en la imagen, proporcionando una segunda opinión en la interpretación de los resultados, pudiera mejorar la consistencia y agilizar el proceso de diagnóstico. Los resultados arrojados por el algoritmo desarrollado fueron contrastados con las anotaciones realizadas en imágenes publicadas en The Lung Image Database Consortium Image Collection (LIDC-IDRI) y se obtuvo un 77.78 por ciento de acierto en la detección de nódulos pulmonares solitarios(AU)

The identification of lung cancer at early stages has been in recent years a prioritized task for the scientific community. This disease is the leading cause of death in men and the third after the colon and breast cancer in women. Performing imaging studies contributes to the early detection of this disease. The high volume of images generated by medical equipment leads to reviewing much information to issue a medical diagnosis. Often are required the assessment of several specialists to reach an accurate diagnosis, slowing the process of patient care. In the present investigation are exposed the results obtained to develop an algorithm using image processing methods for the identification of solitary pulmonary nodules. The use of systems that direct the attention of specialists to candidate regions in the image, providing a second opinion in the interpretation of results could improve consistency and agility in the diagnostic process. The results obtained by the developed algorithm were compared with annotations in images published in The Lung Image Database Consortium Image Collection (LIDC-IDRI) and was obtained 77.78 percent accuracy in the detection of solitary pulmonary nodules(AU)

Processamento de estruturas tridimensionais de Medicina Nuclear na modalidade PET / Processing of three-dimensional structures of Nuclear Medicine in PET modality

A Medicina Nuclear, como especialidade de obtenção de imagens médicas é um dos principais procedimentos utilizados hoje nos centros de saúde, tendo como grande vantagem a capacidade de analisar o comportamento metabólico do paciente. Este projeto está baseado em imagens médicas obtidas através da modalidade PET (Positron Emission Tomography). Para isso, foi desenvolvida uma estrutura de processamento de imagens tridimensionais PET, constituída por etapas sucessivas que se iniciam com a obtenção das imagens padrões (gold standard), sendo utilizados para este fim volumes simulados do Ventrículo Esquerdo do Coração criadas como parte do projeto, assim como phantoms gerados com o software NCAT-4D. A seguir, nos volumes simulados é introduzido ruído Poisson que é o ruído característico das imagens PET. Na sequência é executada uma etapa de pré-processamento, utilizando alguns filtros 3D tais como o filtro da mediana, o filtro da Gaussiana ponderada e o filtro Anscombe/Wiener. Posteriormente é aplicada a etapa de segmentação, processo baseado na teoria de Conectividade Fuzzy sendo implementadas quatro diferentes abordagens 3D: Algoritmo Genérico, LIFO, kTetaFOEMS e Pesos Dinâmicos. Finalmente, um procedimento de avaliação conformado por três parâmetros (Verdadeiro Positivo, Falso Positivo e Máxima Distância) foi utilizado para mensurar o nível de eficiência e precisão do processo. Constatou-se que o par Filtro - Segmentador constituído pelo filtro Anscombe/Wiener junto com o segmentador Fuzzy baseado em Pesos Dinâmicos proporcionou os melhores resultados, com taxas de VP e FP na ordem de 98,49 ± 0,27% e 2,19 ± 0,19%, respectivamente, para o caso do volume do Ventrículo Esquerdo simulado. Com o conjunto de escolhas feitas ao longo da estrutura de processamento, encerrou-se o projeto analisando um número reduzido de volumes pertencentes a um exame PET real, obtendo-se a quantificação dos volumes.

The Nuclear medicine, as a specialty to obtain medical images is very important, and it has became one of the main procedures utilized in Health Care Centers to analyze the metabolic behavior of the patient. This project was based on medical images obtained by the PET modality (Positron Emission Tomography). Thus, we developed a framework for processing Nuclear Medicine three-dimensional images of the PET modality, which is composed of consecutive steps that start with the generation of standard images (gold standard) by using simulated images of the Left Ventricular Heart, such as phantoms obtained from the NCAT-4D software. Then, Poisson quantum noise was introduced into the whole volume to simulate the characteristic noises in PET images. Subsequently, the pre-processing step was executed by using specific 3D filters, such as the median filter, the weighted Gaussian filter, and the Anscombe/Wiener filter. Then the segmentation process, which is based on the Fuzzy Connectedness theory, was implemented. For that purpose four different 3D approaches were implemented: Generic, LIFO, kTetaFOEMS, and Dynamic Weight algorithm. Finally, an assessment procedure was used as a measurement tool to quantify three parameters (True Positive, False Positive and Maximum Distance) that determined the level of efficiency and precision of our process. It was found that the pair filter - segmenter formed by the Anscombe/Wiener filter together with the Fuzzy segmenter based on Dynamic Weights provided the best results, with VP and FP rates of 98.49 ± 0.27% and 2.19 ± 0.19%, respectively, for the simulation of the Left Ventricular volume. Along with the set of choices made during the processing structure, the project was finished with the analysis of a small number of volumes that belonged to a real PET test, thus the quantification of the volumes was obtained.

Característica de Euler-Poincaré aplicada para identificar baixa densidade óssea a partir de imagens tomográficas de vértebras / The Euler-Poincaré characteristic applied to identify low bone density from vertebral tomographic images

A densidade mineral óssea (DMO) é o exame padrão-ouro para o diagnóstico da osteoporose. No entanto, sabe-se que apenas essa medida não é suficiente para identificar completamente a fragilidade óssea e o conseqüente risco de fratura, tornando-se necessário a investigação da estrutura óssea. OBJETIVOS: Avaliar se a característica de Euler-Poincaré (CEP) para analisar a conectividade do osso trabecular poderia fornecer um suporte adicional na identificação da deterioração da estrutura óssea. MATERIAIS E MÉTODOS: Analisou-se um conjunto de imagens formando disectors, obtidas da tomografia computadorizada de vértebras lombares, a partir dos quais foi estimada a característica Euler-Poincarè.(CEP). Para lidar com o processamento de imagens dos disectors, foi desenvolvido um programa de computador usando o GTK+ para MS-Windows. Os resultados foram comparados com a DMO. RESULTADOS: Verificou-se que a medida da CEP está correlacionada com os resultados obtidos por meio da DMO para as vértebras lombares. Ficou demonstrado também que a área de conectividade das trabéculas que é propagada ao longo dos disectors corrobora para assegurar que os resultados da CEP sejam consistentes com a medida da DMO. CONCLUSÕES: A aplicação da CEP na análise das tomografias vertebrais poderá vir a se constituir num método para avaliar a estrutura óssea trabecular, e sua correlação com a resistência mecânica do osso, sendo necessários porém mais estudos para confirmar esses dados.

The bone mineral density (BMD) is the gold standard test for the diagnosis of osteoporosis. Nevertheless, it is known that only this measurement is not sufficient to completely identify bone fragility and the consequent risk of fracture, becoming necessary the investigation of the bone structure. OBJECTIVES: Evaluate if the Euler-Poincaré characteristic (EPC) for the analysis of the connectivity of the trabecular bone could supply an additional support in identifying the deterioration of the bone structure. MATERIALS AND METHODS: A group of images forming dissectors were analyzed, obtained from the computerized tomography of the lumbar vertebrae, from which the Euler-Poincarè characteristic (EPC) was estimated. To deal with the processing of images of the dissectors, a computer program was developed using the GTK+ for MS-Windows. The results were compared to the BMD. RESULTS: It was verified that the measurement of the EPC is correlated with the results obtained by means of the BMD for the lumbar vertebrae. It was also demonstrated that the area of connectivity of the trabecular that is propagated over the dissectors corroborates to ensure that the results of the EPC are consistent with the BMD measurement. CONCLUSIONS: The application of ECP in the analysis of the vertebral tomographies could constitute a method to evaluate the trabecular bone structure, and its correlation with the mechanical resistance of the bone, however being necessary more studies to confirm this data.