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[Objective] The transformation from RGB to Munsell color space is a relevant issue for different tasks, such as the identification of soil taxonomy, organic materials, rock materials, skin types, among others. This research aims to develop alternatives based on feedforward networks and the convolutional neural networks to predict the hue, value, and chroma in the Munsell soil-color charts (MSCCs) from RGB images. [Methodology] We used images of Munsell soil-color charts from 2000 and 2009 versions taken from Millota et al. (2018) to train and test the models. A division of 2856 images in 10% for testing, 20% for validation, and 70% for training was used to build the models. [Results] The best approach was the convolutional neural networks for classification with 93% of total accuracy of hue, value, and chroma combination; it comprises three CNN, one for the hue prediction, another for value prediction, and the last one for chroma prediction. However, the three best models show closeness between the prediction and real values according to the CIEDE2000 distance. The cases classified incorrectly with this approach had a CIEDE2000 average of 0.27 and a standard deviation of 1.06. [Conclusions] The models demonstrated better color recognition in uncontrolled environments than the Transformation of Centore, which is the classical method to transform from RGB to HVC. The results were promising, but the model should be tested with real images at different applications, such as soil real images, to classify the soil color., [Objetivo] La transformación del espacio de color RGB al de color Munsell es un tema relevante para diferentes tareas como la identificación de: la taxonomía del suelo, materiales orgánicos, materiales rocosos. tipo de piel entre otros. Esta investigación tiene como objetivo desarrollar alternativas basadas en las redes feedforward y las Redes Neuronales Convolucionales para predecir el tono, el valor y el croma en las cartas de color del suelo de Munsell (MSCC) a partir de imágenes RGB. [Metodología] Con el fin de entrenar y probar los modelos, usamos imágenes de los gráficos de colores de suelo de Munsell de las versiones 2000 y 2009 tomadas de Millota et al. (2018). Se utilizó una división de 2856 imágenes en 10% para pruebas, 20% para validación y 70% para entrenamiento con miras a construir los modelos. [Resultados] El mejor enfoque fueron las redes neuronales convolucionales para la clasificación con un 93% de precisión total de la combinación de tono, valor y croma (consta de tres CNN, uno para la predicción de tono, otra para la de valor y la última para la de croma), aunque los tres mejores modelos muestran cercanía entre la predicción y los valores reales según la distancia CIEDE2000. Los casos clasificados incorrectamente con este enfoque tuvieron un promedio CIEDE2000 de 0.27 y una desviación estándar de 1.06. [Conclusiones] Los modelos demostraron un mejor reconocimiento de color en entornos no controlados que la transformación de Centore, la cual es el método clásico para transformar de RGB a HVC. Los resultados fueron prometedores, pero el modelo debe evaluarse ampliamente con imágenes reales del suelo para clasificar su color., [Objetivo] A conversão do espaço de cor RGB para o espaço de cores Munsell é um tema relevante para diferentes tarefas como a identificação: da taxonomia do solo, dos materiais orgânicos, dos materiais rochosos, do tipo de pele, dentre outros. Esta pesquisa tem como objetivo desenvolver alternativas baseadas nas redes feed-forward e nas Redes Neurais Convolucionais (CNN) para prever o matiz, o valor e o croma nas cartas de cores do solo de Munsell (MSCC) a partir de imagens RGB. [Metodologia] Para treinar e testar os modelos, usamos imagens dos gráficos de cores do solo de Munsell das versões 2000 e 2009 tomadas de Millota et al. (2018). Foi usada uma divisão de 2856 imagens em 10% para testes, 20% para validação e 70% para treinamento com o intuito de construir os modelos. [Resultados] O melhor enfoque foram as redes neurais convolucionais para a classificação com 93% de precisão total da combinação de matiz, valor e croma (consta de três CNN, um para a previsão de matiz, outra para a previsão de valor e a última para a previsão de croma), embora três melhores modelos tenham mostrado proximidade entre a previsão e os valores reais dependendo da distância CIEDE2000. Os casos classificados incorretamente com este enfoque tiveram uma média CIEDE2000 de 0,27 e um desvio padrão de 1,06. [Conclusões] Os modelos demonstraram um melhor reconhecimento de cor em ambientes não controlados que a conversão de Centore, que é o método clássico para converter de RGB a HVC. Os resultados foram prometedores, mas o modelo deve ser amplamente avaliado com imagens reais de solo para classificar sua cor.

In this work, we assess the impact of the adaptive unsharp mask filter as a preprocessing stage for breast tumour multi-class classification with histopathological images, evaluating two state-of-the-art architectures, not tested so far for this problem to our knowledge: DenseNet, SqueezeNet and a 5-layer baseline deep learning architecture. SqueezeNet is an efficient architecture, which can be useful in environments with restrictive computational resources. According to the results, the filter improved the accuracy from 2% to 4% in the 5-layer baseline architecture, on the other hand, DenseNet and SqueezeNet show a negative impact, losing from 2% to 6% accuracy. Hence, simpler deep learning architectures can take more advantage of filters than complex architectures, which are able to learn the preprocessing filter implemented. Squeeze net yielded the highest per parameter accuracy, while DenseNet achieved a 96% accuracy, defeating previous state of the art architectures by 1% to 5%, making DenseNet a considerably more efficient architecture for breast tumour classification.

Objective The aim of the present review was to describe the evolution of the damage control concept in neurotrauma, including the surgical technique and medical postoperative care, from the lessons learned from civilian and military neurosurgeons who have applied the concept regularly in practice at military hospitals and civilian institutions in areas with limited resources. Methods The present narrative review was based on the experience of a group of neurosurgeons who participated in the development of the concept from their practice working in military theaters and low-resources settings with an important burden of blunt and penetrating cranial neurotrauma. Results Damage control surgery in neurotrauma has been described as a sequential therapeutic strategy that supports physiological restoration before anatomical repair in patients with critical injuries. The application of the concept has evolved since the early definitions in 1998. Current strategies have been supported by military neurosurgery experience, and the concept has been applied in civilian settings with limited resources. Conclusion Damage control in neurotrauma is a therapeutic option for severe traumatic brain injury management in austere environments. To apply the concept while using an appropriate approach, lessons must be learned from experienced neurosurgeons who use this technique regularly.

Introduction The spinal trauma (ST) presents 82.8% in men around 32 years' old; the 56.5% has spinal cord injury (SCI), whose word incidence is 25.5 million/year. The main cause is traffic accidents. In Colombia, 55% of ST are cervical and 45% has complete SCI. Overall, 80 to 85% are men between 20 to 30 years' old and 25% is related to alcohol consumption. The American Spinal Injury Association (ASIA) classifies the SCI in: (1) complete sensitive and motor function; (2) incomplete: normal sensibility, no motor function (MF); (3) incomplete: MF under the lesson less than 3/5; (4) incomplete: MF more than 3/5; and (5) normal. Spine divides itself in upper cervical: C0–C2 (UCS), low cervical C3–C7 (LCS), thoracic, and lumbar. Objectives This study aims to classify the ST according its place, SCI, and surgical treatment. This study also characterizes the population under surgery by ST; establish its level, describe the SCI associated with ST, and stablish the surgical treatment according the level of the ST. Patients and Methods Retrospective, analytic, and descriptive study, using the clinical registers. Patients were admitted in the emergency of “Clínica de la Unidad de la Sabana” (CUS). Both sexes, no age limit age, between January 2009 and July 2014, who suffered spine arthrodesis caused by ST. Those who did not need surgery were excluded. The collection data form included the following: identification number, age, sex, level of the ST, SCI according ASIA scale, and surgical treatment. The data were analyzed in a descriptive and comparative way. Results The population comprised of 60 patients, 46 men and 14 women. The more frequent age was the second decade of life (17 patients, 11 M:6 W), followed by the third one (14 patients, 11 M:3 F). The principal segment affected was the cervical (24 patients: 5 UCS and 19 LCS), followed by lumbar (22 patients). The most frequent place was L1 (13) and C6–C7 (10). There were 29 patients with SCI ASIA A, 10 with ASIA E, and 21 patients with incomplete SCI. The lesions in the UCS did not have SCI; in the LCS were 15 ASIA A, 2 in ASIA E, and 2 incomplete SCI; thoracic had 10 ASIA A, 1 ASIA E, and 3 incomplete SCI, lumbar had 4 ASIA A, 2 ASIA E, and 16 incomplete SCI. The surgeries performed in the UCS were one 360 degrees arthrodesis, four posterior arthrodesis (PA), LCS: 6 corpectomy, 13 PA; thoracic and lumbar: 14 and 22 PA, respectively. Conclusion In our study, 76.6% were male patients, 51.6% were between the second and the third decade of life, as reported in the literature. Overall, 40% of the ST was cervical, a little lower than reported nationally, 48.33% had SCI ASIA A, very similar to the word statistics. The relation of the ST and SCI reported that the group with the highest SCI ASIA A was the LCS (78.94%) and thoracic (71.4%), the incomplete SCI was more frequent in the lumbar spine (72.7%), and the UCS has no SCI. The most frequent surgical approach was the posterior, in 88.3% of patients.