Low Computational-Cost Footprint Deformities Diagnosis Sensor through Angles, Dimensions Analysis and Image Processing Techniques.

Manual measurements of foot anthropometry can lead to errors since this task involves the experience of the specialist who performs them, resulting in different subjective measures from the same footprint. Moreover, some of the diagnoses that are given to classify a footprint deformity are based on a qualitative interpretation by the physician; there is no quantitative interpretation of the footprint. The importance of providing a correct and accurate diagnosis lies in the need to ensure that an appropriate treatment is provided for the improvement of the patient without risking his or her health. Therefore, this article presents a smart sensor that integrates the capture of the footprint, a low computational-cost analysis of the image and the interpretation of the results through a quantitative evaluation. The smart sensor implemented required the use of a camera (Logitech C920) connected to a Raspberry Pi 3, where a graphical interface was made for the capture and processing of the image, and it was adapted to a podoscope conventionally used by specialists such as orthopedist, physiotherapists and podiatrists. The footprint diagnosis smart sensor (FPDSS) has proven to be robust to different types of deformity, precise, sensitive and correlated in 0.99 with the measurements from the digitalized image of the ink mat.

Vascular Structure Identification in Intraoperative 3D Contrast-Enhanced Ultrasound Data.

In this paper, a method of vascular structure identification in intraoperative 3D Contrast-Enhanced Ultrasound (CEUS) data is presented. Ultrasound imaging is commonly used in brain tumor surgery to investigate in real time the current status of cerebral structures. The use of an ultrasound contrast agent enables to highlight tumor tissue, but also surrounding blood vessels. However, these structures can be used as landmarks to estimate and correct the brain shift. This work proposes an alternative method for extracting small vascular segments close to the tumor as landmark. The patient image dataset involved in brain tumor operations includes preoperative contrast T1MR (cT1MR) data and 3D intraoperative contrast enhanced ultrasound data acquired before (3D-iCEUS(start) and after (3D-iCEUS(end) tumor resection. Based on rigid registration techniques, a preselected vascular segment in cT1MR is searched in 3D-iCEUS(start) and 3D-iCEUS(end) data. The method was validated by using three similarity measures (Normalized Gradient Field, Normalized Mutual Information and Normalized Cross Correlation). Tests were performed on data obtained from ten patients overcoming a brain tumor operation and it succeeded in nine cases. Despite the small size of the vascular structures, the artifacts in the ultrasound images and the brain tissue deformations, blood vessels were successfully identified.

High-Dimensional Feature Selection for Automatic Classification of Coronary Stenosis Using an Evolutionary Algorithm.

In this paper, a novel strategy to perform high-dimensional feature selection using an evolutionary algorithm for the automatic classification of coronary stenosis is introduced. The method involves a feature extraction stage to form a bank of 473 features considering different types such as intensity, texture and shape. The feature selection task is carried out on a high-dimensional feature bank, where the search space is denoted by O(2n) and n=473. The proposed evolutionary search strategy was compared in terms of the Jaccard coefficient and accuracy classification with different state-of-the-art methods. The highest feature selection rate, along with the best classification performance, was obtained with a subset of four features, representing a 99% discrimination rate. In the last stage, the feature subset was used as input to train a support vector machine using an independent testing set. The classification of coronary stenosis cases involves a binary classification type by considering positive and negative classes. The highest classification performance was obtained with the four-feature subset in terms of accuracy (0.86) and Jaccard coefficient (0.75) metrics. In addition, a second dataset containing 2788 instances was formed from a public image database, obtaining an accuracy of 0.89 and a Jaccard Coefficient of 0.80. Finally, based on the performance achieved with the four-feature subset, they can be suitable for use in a clinical decision support system.

[Comparative analysis of bleed volume with visual technique]. / Análisis comparativo de volumen de sangrado mediante técnica visual.

Background: Blood loss estimation in a surgery is made by anesthesiologists by means of visual technique, which is not reliable because it can change depending on the judgement of every person, or his/her work experience, which is why it is considered something subjective. Therefore, the results obtained could lead to make mistakes with the exact amount of bleeding, mismanaging unnecessary hemoderived transfusions or administering unnecessary drugs. Objective: To compare the blood volume and its visual calculation between Anesthesiology residents and anesthesiologists. Material and methods: Prolective cross-sectional study which included 85 Anesthesiology residents and anesthesiologists. Diverse scenarios of bleeding were set, divided into gauze pads, compresses and jars, and each participant was asked to answer the poll. Results: There is no significant difference in the estimate of bleeding between the estimation made by residents and anesthesiologists. Conclusions: The grade of studies or experience is not significant for the exact estimation of bleeding with visual technique.

Introducción: la estimación de sangrado en las cirugías se realiza por medio de la técnica de cálculo visual, que es llevada a cabo por parte del servicio de anestesiología. Se trata de una técnica poco confiable, que puede variar dependiendo del juicio de cada persona y de su experiencia laboral, por lo que es considerada algo subjetivo. Por lo tanto, los datos arrojados hacen que se cometan errores en la cantidad exacta del sangrado y llevan a transfusiones de paquetes globulares o a la administración probablemente innecesaria de medicamentos. Objetivo: comparar la cantidad de un volumen de sangrado determinado y su cálculo visual realizada por residentes de Anestesiología y anestesiólogos. Material y métodos: estudio transversal prolectivo en el cual se incluyeron 85 médicos residentes de Anestesiología y anestesiólogos. Se dispusieron diversos escenarios con sangrado, los cuales se dividieron en gasas, compresas y frascos, y se le pidió a cada médico que contestara la encuesta correspondiente según su criterio. Resultados: de acuerdo con los resultados, no hay diferencia significativa en la estimación realizada del sangrado entre residentes y anestesiólogos para las muestras estudiadas. Conclusiones: el grado de estudios o experiencia no es significativo para la exacta estimación de sangrado con técnica visual.

[Analysis of life quality on patients with thoracolumbar fractures]. / Análisis de calidad de vida en pacientes con fracturas toracolumbares.

Background: Patients with thoracolumbar fractures with TLICS 4 classification are at the limit of surgical fixation with regards to conservative treatment; however, results in our environment are not known, which is why this study has innovative characteristics. Objective: To determine the quality of life in patients with TLICS 4 thoracolumbar fractures using traditional fixation with regards to no fixation in a third level hospital. Material and methods: A cohort prospective study was carried out in patients with TLICS 4 classification thoracolumbar fractures using traditional fixation with regards to no fixation in beneficiaries from the Mexican Institute for Social Security. The SF-12 instrument, which assessed quality of life, was administered; age, sex, days of hospitalization, time of spinal cord injury were searched in the patients' medical history. It was used descriptive and inferential statistics using Student's t or Mann-Whitney U. Results: 20 patients participated and 9 had traditional fixation (45%). All patients had type E spinal cord injuries according to the International Standards for Neurological Classification of Spinal Cord Injury. Mean age of non-fixation was 42.2 ± 12.9 and of fixation 44.9 ± 10.2; in non-fixation 6 (67%) were male. The quality of life score was 29.1 ± 0.9 in the conservative treatment and 28.7 ± 1.3 in the surgical treatment, p < 0.462. Conclusions: No differences in quality of life were observed in patients with TLICS 4 thoracolumbar fractures using traditional fixation with regards to no fixation.

Introducción: los pacientes con fracturas toracolumbares con clasificación TLICS 4 se encuentran en el límite de la fijación quirúrgica con respecto al tratamiento conservador; sin embargo, resultados en nuestro medio no son conocidos, por lo que este estudio tiene características innovadoras. Objetivo: determinar la calidad de vida en pacientes con fracturas toracolumbares clasificación TLICS 4, mediante fijación tradicional con respecto a no fijación en un hospital de tercer nivel. Material y métodos: estudio de cohorte prospectiva en pacientes con fracturas toracolumbares clasificación TLICS 4 mediante fijación tradicional con respecto a no fijación en población derechohabiente del Instituto Mexicano del Seguro Social (IMSS). Se aplicó el instrumento SF-12, que evaluó la calidad de vida; se buscó en los expedientes de los pacientes edad, sexo, días de hospitalización, tiempo de lesión medular. Se usó estadística descriptiva e inferencial mediante t de Student o U de Mann Whitney. Resultados: participaron 20 pacientes y 9 (45%) tuvieron fijación tradicional. Todos los pacientes pertenecían a la clase E según las Normas Internacionales para la Clasificación Neurológica de lesiones de la médula espinal. Edad media de no fijación con 42.2 ± 12.9 y de fijación 44.9 ± 10.2; en no fijación, 6 (67%) eran varones. La puntuación de calidad de vida fue en el tratamiento conservador con 29.1 ± 0.9 y quirúrgico 28.7 ± 1.3, p < 0.462. Conclusiones: no se observaron diferencias en la calidad de vida en pacientes con fracturas toracolumbares clasificación TLICS 4 mediante fijación tradicional y no fijación.

[Analysis of life quality on patients with thoracolumbar fractures]. / Análisis de calidad de vida en pacientes con fracturas toracolumbares.

Background: Patients with thoracolumbar fractures with TLICS 4 classification are at the limit of surgical fixation with regards to conservative treatment; however, results in our environment are not known, which is why this study has innovative characteristics. Objective: To determine the quality of life in patients with TLICS 4 thoracolumbar fractures using traditional fixation with regards to no fixation in a third level hospital. Material and methods: A cohort prospective study was carried out in patients with TLICS 4 classification thoracolumbar fractures using traditional fixation with regards to no fixation in beneficiaries from the Mexican Institute for Social Security. The SF-12 instrument, which assessed quality of life, was administered; age, sex, days of hospitalization, time of spinal cord injury were searched in the patients' medical history. It was used descriptive and inferential statistics using Student's t or Mann-Whitney U. Results: 20 patients participated and 9 had traditional fixation (45%). All patients had type E spinal cord injuries according to the International Standards for Neurological Classification of Spinal Cord Injury. Mean age of non-fixation was 42.2 ± 12.9 and of fixation 44.9 ± 10.2; in non-fixation 6 (67%) were male. The quality of life score was 29.1 ± 0.9 in the conservative treatment and 28.7 ± 1.3 in the surgical treatment, p < 0.462. Conclusions: No differences in quality of life were observed in patients with TLICS 4 thoracolumbar fractures using traditional fixation with regards to no fixation.

Introducción: los pacientes con fracturas toracolumbares con clasificación TLICS 4 se encuentran en el límite de la fijación quirúrgica con respecto al tratamiento conservador; sin embargo, resultados en nuestro medio no son conocidos, por lo que este estudio tiene características innovadoras. Objetivo: determinar la calidad de vida en pacientes con fracturas toracolumbares clasificación TLICS 4, mediante fijación tradicional con respecto a no fijación en un hospital de tercer nivel. Material y métodos: estudio de cohorte prospectiva en pacientes con fracturas toracolumbares clasificación TLICS 4 mediante fijación tradicional con respecto a no fijación en población derechohabiente del Instituto Mexicano del Seguro Social (IMSS). Se aplicó el instrumento SF-12, que evaluó la calidad de vida; se buscó en los expedientes de los pacientes edad, sexo, días de hospitalización, tiempo de lesión medular. Se usó estadística descriptiva e inferencial mediante t de Student o U de Mann Whitney. Resultados: participaron 20 pacientes y 9 (45%) tuvieron fijación tradicional. Todos los pacientes pertenecían a la clase E según las Normas Internacionales para la Clasificación Neurológica de lesiones de la médula espinal. Edad media de no fijación con 42.2 ± 12.9 y de fijación 44.9 ± 10.2; en no fijación, 6 (67%) eran varones. La puntuación de calidad de vida fue en el tratamiento conservador con 29.1 ± 0.9 y quirúrgico 28.7 ± 1.3, p < 0.462. Conclusiones: no se observaron diferencias en la calidad de vida en pacientes con fracturas toracolumbares clasificación TLICS 4 mediante fijación tradicional y no fijación.

[Atherogenic index and its association with acute coronary ischemic syndrome mortality]. / Índice aterogénico y su relación con la mortalidad del síndrome isquémico coronario agudo.

Background: Several indexes have been developed to define the risk attributable to lipid metabolism with a single value. The total cholesterol/high-density lipoprotein (TC/HDL-C) and low-density lipoprotein/high-density lipoprotein (LDL-C/HDL-C) ratios are the most used. The higher the value of these ratios, the greater the probability of cardiovascular events. Objective: To identify whether the TC/HDL-C and LDL-C/HDL-C ratios are early prognostic markers of mortality and major cardiovascular events in patients with ST-elevation acute coronary syndrome. Material and methods: 265 patients with ST-segment elevation acute coronary ischemic syndrome were included, divided into 4 groups according to the values of the atherogenic indices. Mortality and major cardiovascular events at 30-day follow-up were analyzed. Comparison of the groups was performed using the chi-squared test or ANOVA, depending on the case (p < 0.05). Results: The cut-off point for the TC/HDL-C index was 6.9 and for the LDL-C/HDL-C it was 2.7. The comparative analysis of groups showed that cardiovascular death and arrhythmia were higher in group 3 (p = 0.006 and p = 0.003, respectively). Conclusions: TC/HDL-C and LDL-C/HDL-C indexes can be used as prognostic markers of cardiovascular mortality in the first 30 days of follow-up.

Introducción: se han elaborado diferentes índices para definir el riesgo atribuible al metabolismo lipídico con un solo valor. Los coeficientes colesterol total/lipoproteínas de alta densidad (CT/C-HDL) y lipoproteínas de baja densidad/lipoproteínas de alta densidad (C-LDL/C-HDL) son los más utilizados. A mayor valor de estos cocientes, la probabilidad de eventos cardiovasculares es mayor. Objetivo: identificar si los índices CT/C-HDL y C-LDL/C-HDL son marcadores pronósticos tempranos de mortalidad y evento cardiovascular mayor en pacientes con síndrome isquémico coronario agudo con elevación del ST. Material y métodos: se incluyeron 265 pacientes con síndrome isquémico coronario agudo con elevación del segmento ST, divididos en 4 grupos según los valores de los índices aterogénicos. Se analizó la mortalidad y el evento cardiovascular mayor en los 30 días de seguimiento. Se identificó el punto de corte de cada índice mediante un análisis de curva ROC. La comparación de los grupos se hizo con chi cuadrada o ANOVA, según fuera el caso (p < 0.05). Resultados: el punto de corte para el índice CT/C-HDL fue de 6.9 y para el C-LDL/C-HDL de 2.7. El análisis comparativo de los grupos demostró que la muerte cardiovascular y arritmia fue mayor en el grupo 3 (p = 0.006 y p = 0.003, respectivamente). Conclusiones: los índices CT/C-HDL y C-LDL/C-HDL pueden ser utilizados como marcadores pronósticos de mortalidad cardiovascular en los primeros 30 días de seguimiento.

Improving convolutional neural network learning based on a hierarchical bezier generative model for stenosis detection in X-ray images.

BACKGROUND AND OBJECTIVE: Automatic detection of stenosis on X-ray Coronary Angiography (XCA) images may help diagnose early coronary artery disease. Stenosis is manifested by a buildup of plaque in the arteries, decreasing the blood flow to the heart, increasing the risk of a heart attack. Convolutional Neural Networks (CNNs) have been successfully applied to identify pathological, regular, and featured tissues on rich and diverse medical image datasets. Nevertheless, CNNs find operative and performing limitations while working with small and poorly diversified databases. Transfer learning from large natural image datasets (such as ImageNet) has become a de-facto method to improve neural networks performance in the medical image domain. METHODS: This paper proposes a novel Hierarchical Bezier-based Generative Model (HBGM) to improve the CNNs training process to detect stenosis. Herein, artificial image patches are generated to enlarge the original database, speeding up network convergence. The artificial dataset consists of 10,000 images containing 50% stenosis and 50% non-stenosis cases. Besides, a reliable Fréchet Inception Distance (FID) is used to evaluate the generated data quantitatively. Therefore, by using the proposed framework, the network is pre-trained with the artificial datasets and subsequently fine-tuned using the real XCA training dataset. The real dataset consists of 250 XCA image patches, selecting 125 images for stenosis and the remainder for non-stenosis cases. Furthermore, a Convolutional Block Attention Module (CBAM) was included in the network architecture as a self-attention mechanism to improve the efficiency of the network. RESULTS: The results showed that the pre-trained networks using the proposed generative model outperformed the results concerning training from scratch. Particularly, an accuracy, precision, sensitivity, and F1-score of 0.8934, 0.9031, 0.8746, 0.8880, 0.9111, respectively, were achieved. The generated artificial dataset obtains a mean FID of 84.0886, with more realistic visual XCA images. CONCLUSIONS: Different ResNet architectures for stenosis detection have been evaluated, including attention modules into the network. Numerical results demonstrated that by using the HBGM is obtained a higher performance than training from scratch, even outperforming the ImageNet pre-trained models.

Segmentation of brain tumour in 3D Intraoperative Ultrasound imaging.

BACKGROUND: Intraoperative ultrasound (iUS), using a navigation system and preoperative magnetic resonance imaging (pMRI), supports the surgeon intraoperatively in identifying tumour margins. Therefore, visual tumour enhancement can be supported by efficient segmentation methods. METHODS: A semi-automatic and two registration-based segmentation methods are evaluated to extract brain tumours from 3D-iUS data. The registration-based methods estimated the brain deformation after craniotomy based on pMRI and 3D-iUS data. Both approaches use the normalised gradient field and linear correlation of linear combinations metrics. Proposed methods were evaluated on 66 B-mode and contrast-mode 3D-iUS data with metastasis and glioblastoma. RESULTS: The semi-automatic segmentation achieved superior results with dice similarity index (DSI) values between [85.34, 86.79]% and contour mean distance values between [1.05, 1.11] mm for both modalities and tumour classes. CONCLUSIONS: Better segmentation results were obtained for metastasis detection than glioblastoma, preferring 3D-intraoperative B-mode over 3D-intraoperative contrast-mode.

A Novel Multiscale Gaussian-Matched Filter Using Neural Networks for the Segmentation of X-Ray Coronary Angiograms.

The accurate and efficient segmentation of coronary arteries in X-ray angiograms represents an essential task for computer-aided diagnosis. This paper presents a new multiscale Gaussian-matched filter (MGMF) based on artificial neural networks. The proposed method consists of two different stages. In the first stage, MGMF is used for detecting vessel-like structures while reducing image noise. The results of MGMF are compared with those obtained using six GMF-based detection methods in terms of the area (Az) under the receiver operating characteristic (ROC) curve. In the second stage, ten thresholding methods of the state of the art are compared in order to classify the magnitude of the multiscale Gaussian response into vessel and nonvessel pixels, respectively. The accuracy measure is used to analyze the segmentation methods, by comparing the results with a set of 100 X-ray coronary angiograms, which were outlined by a specialist to form the ground truth. Finally, the proposed method is compared with seven state-of-the-art vessel segmentation methods. The vessel detection results using the proposed MGMF method achieved an Az = 0.9357 with a training set of 50 angiograms and Az = 0.9362 with the test set of 50 images. In addition, the segmentation results using the intraclass variance thresholding method provided a segmentation accuracy of 0.9568 with the test set of coronary angiograms.

Coronary artery segmentation in X-ray angiograms using gabor filters and differential evolution.

Segmentation of coronary arteries in X-ray angiograms represents an essential task for computer-aided diagnosis, since it can help cardiologists in diagnosing and monitoring vascular abnormalities. Due to the main disadvantages of the X-ray angiograms are the nonuniform illumination, and the weak contrast between blood vessels and image background, different vessel enhancement methods have been introduced. In this paper, a novel method for blood vessel enhancement based on Gabor filters tuned using the optimization strategy of Differential evolution (DE) is proposed. Because the Gabor filters are governed by three different parameters, the optimal selection of those parameters is highly desirable in order to maximize the vessel detection rate while reducing the computational cost of the training stage. To obtain the optimal set of parameters for the Gabor filters, the area (Az) under the receiver operating characteristics curve is used as objective function. In the experimental results, the proposed method achieves an Az=0.9388 in a training set of 40 images, and for a test set of 40 images it obtains the highest performance with an Az=0.9538 compared with six state-of-the-art vessel detection methods. Finally, the proposed method achieves an accuracy of 0.9423 for vessel segmentation using the test set. In addition, the experimental results have also shown that the proposed method can be highly suitable for clinical decision support in terms of computational time and vessel segmentation performance.

Fast Parabola Detection Using Estimation of Distribution Algorithms.

This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about 93.61% on synthetic images and 89% on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications.

Segmentation of Coronary Angiograms Using Gabor Filters and Boltzmann Univariate Marginal Distribution Algorithm.

This paper presents a novel method for improving the training step of the single-scale Gabor filters by using the Boltzmann univariate marginal distribution algorithm (BUMDA) in X-ray angiograms. Since the single-scale Gabor filters (SSG) are governed by three parameters, the optimal selection of the SSG parameters is highly desirable in order to maximize the detection performance of coronary arteries while reducing the computational time. To obtain the best set of parameters for the SSG, the area (Az ) under the receiver operating characteristic curve is used as fitness function. Moreover, to classify vessel and nonvessel pixels from the Gabor filter response, the interclass variance thresholding method has been adopted. The experimental results using the proposed method obtained the highest detection rate with Az = 0.9502 over a training set of 40 images and Az = 0.9583 with a test set of 40 images. In addition, the experimental results of vessel segmentation provided an accuracy of 0.944 with the test set of angiograms.