Medição automática de valva mitral a partir de ecocardiograma utilizando processamento digital de imagens / Automatic measurement of the mitral valve based on echocardiography using digital image processing

Fundamento: A avaliação da área valvar mitral por meio da reconstrução multiplano na ecocardiografia tridimensional é restrita a softwares específicos e à experiência dos ecocardiografistas. Eles precisam selecionar manualmente o frame do vídeo que contenha a área de abertura máxima da valva mitral, dimensão fundamental para a identificação de estenose mitral. Objetivo: Automatizar o processo de determinação da área de abertura máxima da valva mitral, por meio da aplicação de Processamento Digital de Imagens (PDI) em exames de ecocardiograma, desenvolvendo um algoritmo aberto com leitura de vídeo no formato avi. Método: Este estudo piloto observacional transversal foi realizado com vinte e cinco exames diferentes de ecocardiograma, sendo quinze com abertura normal e dez com estenose mitral reumática. Todos os exames foram realizados e disponibilizados por dois especialistas, com autorização do Comitê de Ética em Pesquisa, que utilizaram dois modelos de aparelhos ecocardiográficos: Vivid E95 (GE Healthcare) e Epiq 7 (Philips), com sondas multiplanares transesofágicas. Todos os vídeos em formato avi foram submetidos ao PDI através da técnica de segmentação de imagens. Resultados: As medidas obtidas manualmente por ecocardiografistas experientes e os valores calculados pelo sistema desenvolvido foram comparados utilizando o diagrama de Bland-Altman. Observou-se maior concordância entre valores no intervalo de 0,4 a 2,7 cm². Conclusão: Foi possível determinar automaticamente a área de máxima abertura das valvas mitrais, tanto para os casos advindos da GE quanto da Philips, utilizando apenas um vídeo como dado de entrada. O algoritmo demonstrou economizar tempo nas medições quando comparado com a mensuração habitual. (AU)

Background: The evaluation of mitral valve area through multiplanar reconstruction in 3-dimensional echocardiography is restricted to specific software and to the experience of echocardiographers. They need to manually select the video frame that contains the maximum mitral valve opening area, as this dimension is fundamental to identification of mitral stenosis. Objective: To automate the process of determining the maximum mitral valve opening area, through the application of digital image processing (DIP) in echocardiography tests, developing an open algorithm with video reading in avi format. Method: This cross-sectional observational pilot study was conducted with 25 different echocardiography exams, 15 with normal aperture and 10 with rheumatic mitral stenosis. With the authorization of the Research Ethics Committee, all exams were performed and made available by 2 specialists who used 2 models of echocardiographic devices: Vivid E95 (GE Healthcare) and Epiq 7 (Philips), with multiplanar transesophageal probes. All videos in avi format were submitted to DIP using the image segmentation technique. Results: The measurements obtained manually by experienced echocardiographers and the values calculated by the developed system were compared using a Bland-Altman diagram. There was greater agreement between values in the range from 0.4 to 2.7 cm². Conclusion: It was possible to automatically determine the maximum mitral valve opening area, for cases from both GE and Philips, using only 1 video as input data. The algorithm has been demonstrated to save time on measurements when compared to the usual method. (AU)

A semi-supervised material quantitative intelligent imaging algorithm for spectral CT based on prior information perception learning / 南方医科大学学报

OBJECTIVE@#To propose a semi-supervised material quantitative intelligent imaging algorithm based on prior information perception learning (SLMD-Net) to improve the quality and precision of spectral CT imaging.@*METHODS@#The algorithm includes a supervised and a self- supervised submodule. In the supervised submodule, the mapping relationship between low and high signal-to-noise ratio (SNR) data was constructed through mean square error loss function learning based on a small labeled dataset. In the self- supervised sub-module, an image recovery model was utilized to construct the loss function incorporating the prior information from a large unlabeled low SNR basic material image dataset, and the total variation (TV) model was used to to characterize the prior information of the images. The two submodules were combined to form the SLMD-Net method, and pre-clinical simulation data were used to validate the feasibility and effectiveness of the algorithm.@*RESULTS@#Compared with the traditional model-driven quantitative imaging methods (FBP-DI, PWLS-PCG, and E3DTV), data-driven supervised-learning-based quantitative imaging methods (SUMD-Net and BFCNN), a material quantitative imaging method based on unsupervised learning (UNTV-Net) and semi-supervised learning-based cycle consistent generative adversarial network (Semi-CycleGAN), the proposed SLMD-Net method had better performance in both visual and quantitative assessments. For quantitative imaging of water and bone materials, the SLMD-Net method had the highest PSNR index (31.82 and 29.06), the highest FSIM index (0.95 and 0.90), and the lowest RMSE index (0.03 and 0.02), respectively) and achieved significantly higher image quality scores than the other 7 material decomposition methods (P < 0.05). The material quantitative imaging performance of SLMD-Net was close to that of the supervised network SUMD-Net trained with labeled data with a doubled size.@*CONCLUSIONS@#A small labeled dataset and a large unlabeled low SNR material image dataset can be fully used to suppress noise amplification and artifacts in basic material decomposition in spectral CT and reduce the dependence on labeled data-driven network, which considers more realistic scenario in clinics.

Metal artifact reduction and clinical verification in oral and maxillofacial region based on deep learning / 中华口腔医学杂志

Objective: To construct a kind of neural network for eliminating the metal artifacts in CT images by training the generative adversarial networks (GAN) model, so as to provide reference for clinical practice. Methods: The CT data of patients treated in the Department of Radiology, West China Hospital of Stomatology, Sichuan University from January 2017 to June 2022 were collected. A total of 1 000 cases of artifact-free CT data and 620 cases of metal artifact CT data were obtained, including 5 types of metal restorative materials, namely, fillings, crowns, titanium plates and screws, orthodontic brackets and metal foreign bodies. Four hundred metal artifact CT data and 1 000 artifact-free CT data were utilized for simulation synthesis, and 1 000 pairs of simulated artifacts and metal images and simulated metal images (200 pairs of each type) were constructed. Under the condition that the data of the five metal artifacts were equal, the entire data set was randomly (computer random) divided into a training set (800 pairs) and a test set (200 pairs). The former was used to train the GAN model, and the latter was used to evaluate the performance of the GAN model. The test set was evaluated quantitatively and the quantitative indexes were root-mean-square error (RMSE) and structural similarity index measure (SSIM). The trained GAN model was employed to eliminate the metal artifacts from the CT data of the remaining 220 clinical cases of metal artifact CT data, and the elimination results were evaluated by two senior attending doctors using the modified LiKert scale. Results: The RMSE values for artifact elimination of fillings, crowns, titanium plates and screws, orthodontic brackets and metal foreign bodies in test set were 0.018±0.004, 0.023±0.007, 0.015±0.003, 0.019±0.004, 0.024±0.008, respectively (F=1.29, P=0.274). The SSIM values were 0.963±0.023, 0.961±0.023, 0.965±0.013, 0.958±0.022, 0.957±0.026, respectively (F=2.22, P=0.069). The intra-group correlation coefficient of 2 evaluators was 0.972. For 220 clinical cases, the overall score of the modified LiKert scale was (3.73±1.13), indicating a satisfactory performance. The scores of modified LiKert scale for fillings, crowns, titanium plates and screws, orthodontic brackets and metal foreign bodies were (3.68±1.13), (3.67±1.16), (3.97±1.03), (3.83±1.14), (3.33±1.12), respectively (F=1.44, P=0.145). Conclusions: The metal artifact reduction GAN model constructed in this study can effectively remove the interference of metal artifacts and improve the image quality.

Segmentation of prostate region in magnetic resonance images based on improved V-Net / 生物医学工程学杂志

Magnetic resonance (MR) imaging is an important tool for prostate cancer diagnosis, and accurate segmentation of MR prostate regions by computer-aided diagnostic techniques is important for the diagnosis of prostate cancer. In this paper, we propose an improved end-to-end three-dimensional image segmentation network using a deep learning approach to the traditional V-Net network (V-Net) network in order to provide more accurate image segmentation results. Firstly, we fused the soft attention mechanism into the traditional V-Net's jump connection, and combined short jump connection and small convolutional kernel to further improve the network segmentation accuracy. Then the prostate region was segmented using the Prostate MR Image Segmentation 2012 (PROMISE 12) challenge dataset, and the model was evaluated using the dice similarity coefficient (DSC) and Hausdorff distance (HD). The DSC and HD values of the segmented model could reach 0.903 and 3.912 mm, respectively. The experimental results show that the algorithm in this paper can provide more accurate three-dimensional segmentation results, which can accurately and efficiently segment prostate MR images and provide a reliable basis for clinical diagnosis and treatment.

CT and MRI fusion based on generative adversarial network and convolutional neural networks under image enhancement / 生物医学工程学杂志

Aiming at the problems of missing important features, inconspicuous details and unclear textures in the fusion of multimodal medical images, this paper proposes a method of computed tomography (CT) image and magnetic resonance imaging (MRI) image fusion using generative adversarial network (GAN) and convolutional neural network (CNN) under image enhancement. The generator aimed at high-frequency feature images and used double discriminators to target the fusion images after inverse transform; Then high-frequency feature images were fused by trained GAN model, and low-frequency feature images were fused by CNN pre-training model based on transfer learning. Experimental results showed that, compared with the current advanced fusion algorithm, the proposed method had more abundant texture details and clearer contour edge information in subjective representation. In the evaluation of objective indicators, Q AB/F, information entropy (IE), spatial frequency (SF), structural similarity (SSIM), mutual information (MI) and visual information fidelity for fusion (VIFF) were 2.0%, 6.3%, 7.0%, 5.5%, 9.0% and 3.3% higher than the best test results, respectively. The fused image can be effectively applied to medical diagnosis to further improve the diagnostic efficiency.

Development of craniofacial superimposition: a review / Desarrollo de la superposición craneofacial: una revisión

SUMMARY: Craniofacial superimposition is a method for identifying individuals by using secondary data in order to identify a target group of persons before a DNA process can be used, or to identify an individual instead of using primary data in cases where DNA, fingerprint or dental records are not found. Craniofacial superimposition has continued to evolve, with various techniques, including computer-assisted and photography techniques, to help the operation be more convenient, faster and reliable. The knowledge of forensic anthropology is applied, with a comparison between anatomical landmarks. The study of developments in craniofacial superimposition using computer-assistance has yielded satisfactory results.

La superposición craneofacial es un método para identificar individuos mediante el uso de datos secundarios, se utiliza para identificar un grupo objetivo de personas, antes de que se pueda utilizar un proceso de ADN, o para identificar a un individuo en lugar de utilizar datos primarios en los casos en que no se cuenta con registros de ADN, huellas dactilares o dentales. La superposición craneofacial ha seguido evolucionando, con diversas técnicas, incluidas las técnicas fotográficas y asistidas por computador, para ayudar a que la operación sea más conveniente, rápida y confiable. Se aplica el conocimiento de la antropología forense, con una comparación entre hitos anatómicos. El estudio de la evolución de la superposición craneofacial con asistencia informática ha arrojado resultados satisfactorios.

Tomografía computarizada con reconstrucción 3D para el diagnóstico de lesiones traumáticas de uretra / Three-dimensional computed tomography reconstruction for the diagnosis of traumatic urethral injuries

RESUMEN La uretrografía retrógrada es la técnica de referencia (gold standard) utilizada clásicamente para hacer diagnóstico de lesiones de uretra. En este contexto se presenta un caso en el que se realizó tomografía computarizada con reconstrucción 3D con contraste intravenoso y endouretral, pudiendo reconstruir la uretra en toda su extensión en forma tridimensional. De esta manera se arribó al diagnóstico de certeza de la lesión de uretra. Como ventaja del método se menciona la posibilidad de diagnosticar ‒ con un solo estudio por imágenes‒ lesiones de todo el tracto urinario, órganos sólidos, huecos y lesión del anillo pélvico asociados al traumatismo, con una alta sensibilidad y especificidad sin necesidad de requerir otros estudios complementarios.

ABSTRACT Retrograde urethrography is the gold standard method for the diagnosis of urethral injuries. In this setting, we report the use of computed tomography with intravenous injection and urethral administration of contrast medium and 3D reconstruction of the entire urethra. The definitive diagnosis of urethral injury was made. The advantage of this method is the possibility of making the diagnosis of traumatic injuries of the entire urinary tract, solid organs, hollow viscera and of the pelvic ring within a single imaging test, with high sensitivity and specificity, with no need to perform other complementary tests.

A generative adversarial network-based unsupervised domain adaptation method for magnetic resonance image segmentation / 生物医学工程学杂志

Intelligent medical image segmentation methods have been rapidly developed and applied, while a significant challenge is domain shift. That is, the segmentation performance degrades due to distribution differences between the source domain and the target domain. This paper proposed an unsupervised end-to-end domain adaptation medical image segmentation method based on the generative adversarial network (GAN). A network training and adjustment model was designed, including segmentation and discriminant networks. In the segmentation network, the residual module was used as the basic module to increase feature reusability and reduce model optimization difficulty. Further, it learned cross-domain features at the image feature level with the help of the discriminant network and a combination of segmentation loss with adversarial loss. The discriminant network took the convolutional neural network and used the labels from the source domain, to distinguish whether the segmentation result of the generated network is from the source domain or the target domain. The whole training process was unsupervised. The proposed method was tested with experiments on a public dataset of knee magnetic resonance (MR) images and the clinical dataset from our cooperative hospital. With our method, the mean Dice similarity coefficient (DSC) of segmentation results increased by 2.52% and 6.10% to the classical feature level and image level domain adaptive method. The proposed method effectively improves the domain adaptive ability of the segmentation method, significantly improves the segmentation accuracy of the tibia and femur, and can better solve the domain transfer problem in MR image segmentation.

Fiber direction estimation using constrained spherical deconvolution based on multi-model response function / 生物医学工程学杂志

Constrained spherical deconvolution can quantify white matter fiber orientation distribution information from diffusion magnetic resonance imaging data. But this method is only applicable to single shell diffusion magnetic resonance imaging data and will provide wrong fiber orientation information in white matter tissue which contains isotropic diffusion signals. To solve these problems, this paper proposes a constrained spherical deconvolution method based on multi-model response function. Multi-shell data can improve the stability of fiber orientation, and multi-model response function can attenuate isotropic diffusion signals in white matter, providing more accurate fiber orientation information. Synthetic data and real brain data from public database were used to verify the effectiveness of this algorithm. The results demonstrate that the proposed algorithm can attenuate isotropic diffusion signals in white matter and overcome the influence of partial volume effect on fiber direction estimation, thus estimate fiber direction more accurately. The reconstructed fiber direction distribution is stable, the false peaks are less, and the recognition ability of cross fiber is stronger, which lays a foundation for the further research of fiber bundle tracking technology.

Deep Learning aplicado en imágenes fotoacústicas para la Identificación del cáncer de seno / Deep Learning Applied in Photoacoustic Images for the Identification of Breast Cancer

La Imagen Fotoacústica (PAI por sus siglas en inglés), es una modalidad de imagen híbrida que fusiona la iluminación óptica y la detección por ultrasonido. Debido a que los métodos de imágenes ópticas puras no pueden mantener una alta resolución, la capacidad de lograr imágenes de contraste óptico de alta resolución en tejidos biológicos hace que la fotoacústica (PA por sus siglas en inglés) sea una técnica prometedora para varias aplicaciones de imágenes clínicas. En la actualidad el Aprendizaje Profundo (Deep Learning) tiene el enfoque más reciente en métodos basados en la PAI, donde existe una gran cantidad de aplicaciones en análisis de imágenes, en especial en el área del campo biomédico, como lo es la adquisición, segmentación y reconstrucciones de imágenes de tomografía computarizada. Esta revisión describe las últimas investigaciones en PAI y un análisis sobre las técnicas y métodos basados en Deep Learning, aplicado en diferentes modalidades para el diagnóstico de cáncer de seno(AU)

Photoacoustic Imaging (PAI) is a hybrid imaging modality that combines optical illumination and ultrasound detection. Because pure optical imaging methods cannot maintain high resolution, the ability to achieve high resolution optical contrast images in biological tissues makes Photoacoustic (PA) a promising technique for various clinical imaging applications. At present, Deep Learning has the most recent approach of methods based on PAI where there are a large number of applications in image analysis especially in the area of ​​the biomedical field, such as acquisition, segmentation and reconstructions of computed tomography imaging. This review describes the latest research in PAI and an analysis of the techniques and methods based on Deep Learning applied in different modalities for the diagnosis of breast cancer(AU)

Multimodality-based super-resolution reconstruction for routine brain magnetic resonance images / 南方医科大学学报

OBJECTIVE@#To propose a multi-modality-based super-resolution synthesis model for reconstruction of routine brain magnetic resonance images (MRI) with a low resolution and a high thickness into high-resolution images.@*METHODS@#Based on real paired low-high resolution MRI data (2D T1, 2D T2 FLAIR and 3D T1), a structure-constrained image mapping network was used to extract important features from the images with different modalities including the whole T1 and subcortical regions of T2 FLAIR to reconstruct T1 images with higher resolutions. The gray scale intensity and structural similarities between the super-resolution images and high-resolution images were used to enhance the reconstruction performance. We used the anatomical information acquired from segment maps of the super-resolution T1 image and the ground truth by a segmentation tool as a significant constraint for adaptive learning of the intrinsic tissue structure characteristics of the brain to improve the reconstruction performance of the model.@*RESULTS@#Our method showed the performance on the testing dataset than other methods with an average PSNR of 33.11 and SSIM of 0.996. The anatomical structure of the brain including the sulcus, gyrus, and subcortex were all reconstructed clearly using the proposed method, which also greatly enhanced the precision of MSCSR for brain volume measurement.@*CONCLUSION@#The proposed MSCSR model shows excellent performance for reconstructing super-resolution brain MR images based on the information of brain tissue structure and multimodality MR images.

An adaptive CT metal artifact reduction algorithm that combines projection interpolation and physical correction / 南方医科大学学报

OBJECTIVE@#To propose an adaptive weighted CT metal artifact reduce algorithm that combines projection interpolation and physical correction.@*METHODS@#A normalized metal projection interpolation algorithm was used to obtain the initial corrected projection data. A metal physical correction model was then introduced to obtain the physically corrected projection data. To verify the effectiveness of the method, we conducted experiments using simulation data and clinical data. For the simulation data, the quantitative indicators PSNR and SSIM were used for evaluation, while for the clinical data, the resultant images were evaluated by imaging experts to compare the artifact-reducing performance of different methods.@*RESULTS@#For the simulation data, the proposed method improved the PSNR value by at least 0.2 dB and resulted in the highest SSIM value among the methods for comparison. The experiment with the clinical data showed that the imaging experts gave the highest scores of 3.616±0.338 (in a 5-point scale) to the images processed using the proposed method, which had significant better artifact-reducing performance than the other methods (P < 0.001).@*CONCLUSION@#The metal artifact reduction algorithm proposed herein can effectively reduce metal artifacts while preserving the tissue structure information and reducing the generation of new artifacts.

A nonlocal spectral similarity-induced material decomposition method for noise reduction of dual-energy CT images / 南方医科大学学报

OBJECTIVE@#To propose a nonlocal spectral similarity-induced material decomposition network (NSSD-Net) to reduce the correlation noise in the low-dose spectral CT decomposed images.@*METHODS@#We first built a model-driven iterative decomposition model for dual-energy CT, optimized the objective function solving process using the iterative shrinking threshold algorithm (ISTA), and cast the ISTA decomposition model into the deep learning network. We then developed a novel cost function based on the nonlocal spectral similarity to constrain the training process. To validate the decomposition performance, we established a material decomposition dataset by real patient dual-energy CT data. The NSSD-Net was compared with two traditional model-driven material decomposition methods, one data-based material decomposition method and one data-model coupling-driven material decomposition supervised learning method.@*RESULTS@#The quantitative results showed that compared with the two traditional methods, the NSSD-Net method obtained the highest PNSR values (31.383 and 31.444) and SSIM values (0.970 and 0.963) and the lowest RMSE values (2.901 and 1.633). Compared with the datamodel coupling-driven supervised decomposition method, the NSSD-Net method obtained the highest SSIM values on water and bone decomposed results. The results of subjective image quality assessment by clinical experts showed that the NSSD-Net achieved the highest image quality assessment scores on water and bone basis material (8.625 and 8.250), showing significant differences from the other 4 decomposition methods (P < 0.001).@*CONCLUSION@#The proposed method can achieve high-precision material decomposition and avoid training data quality issues and model unexplainable issues.

Performance of low-dose CT image reconstruction for detecting intracerebral hemorrhage: selection of dose, algorithms and their combinations / 南方医科大学学报

OBJECTIVE@#To investigate the performance of different low-dose CT image reconstruction algorithms for detecting intracerebral hemorrhage.@*METHODS@#Low-dose CT imaging simulation was performed on CT images of intracerebral hemorrhage at 30%, 25% and 20% of normal dose level (defined as 100% dose). Seven algorithms were tested to reconstruct low-dose CT images for noise suppression, including filtered back projection algorithm (FBP), penalized weighted least squares-total variation (PWLS-TV), non-local mean filter (NLM), block matching 3D (BM3D), residual encoding-decoding convolutional neural network (REDCNN), the FBP convolutional neural network (FBPConvNet) and image restoration iterative residual convolutional network (IRLNet). A deep learning-based model (CNN-LSTM) was used to detect intracerebral hemorrhage on normal dose CT images and low-dose CT images reconstructed using the 7 algorithms. The performance of different reconstruction algorithms for detecting intracerebral hemorrhage was evaluated by comparing the results between normal dose CT images and low-dose CT images.@*RESULTS@#At different dose levels, the low-dose CT images reconstructed by FBP had accuracies of detecting intracerebral hemorrhage of 82.21%, 74.61% and 65.55% at 30%, 25% and 20% dose levels, respectively. At the same dose level (30% dose), the images reconstructed by FBP, PWLS-TV, NLM, BM3D, REDCNN, FBPConvNet and IRLNet algorithms had accuracies for detecting intracerebral hemorrhage of 82.21%, 86.80%, 89.37%, 81.43%, 90.05%, 90.72% and 93.51%, respectively. The images reconstructed by IRLNet at 30%, 25% and 20% dose levels had accuracies for detecting intracerebral hemorrhage of 93.51%, 93.51% and 93.06%, respectively.@*CONCLUSION@#The performance of reconstructed low-dose CT images for detecting intracerebral hemorrhage is significantly affected by both dose and reconstruction algorithms. In clinical practice, choosing appropriate dose level and reconstruction algorithm can greatly reduce the radiation dose and ensure the detection performance of CT imaging for intracerebral hemorrhage.

Clasificación no supervisada de imágenes médicas y minería de datos. Algoritmo S3 vs K-medias / Unsupervised classification of medical images and data mining: S3 algorithm vs. k-means

Uno de los desafíos que los programadores tienen que enfrentar es la alta dimensión de grupos de datos. El proceso de reconocimiento de patrones en imagen y la minería de datos para los volúmenes grandes de información son ejemplos de ellos, optimizar la cantidad de veces que se recorre el conjunto de datos, disminuye el tiempo de procesamiento. Éste documento tiene el objetivo de caracterizar el algoritmo de tres pasos (S3), paralelo a K-medias, como una alternativa para afrontar la alta dimensión del conjunto de datos, en la clasificación no supervisada de imagen. Para el análisis de la concurrencia, se escoge, flujo de datos y el esquema instrucción única con datos múltiples. El resultado obtenido confirma que la concurrencia en ambos es posible, S3 no depende de la selección inicial de los representantes y puede ser el proceso de escogimiento de los primeros vectores centrales en K-medias. S3 es una alternativa a ser tenida en cuenta en la clasificación no supervisada de imágenes médicas y procesos de minería de datos(AU)

One of the challenges to be faced by programmers is the large dimensions of data groups. The process of pattern recognition in images and data mining for great volumes of information is an example. Optimizing the number of times that the set of data is run saves processing time. The purpose of the study was to characterize the three-step (S3) algorithm, parallel to k-means, as an alternative to cope with the large dimension of the data set in unsupervised image classification. Concurrence analysis is based on data flow and the single instruction multiple data scheme. The result obtained confirms that concurrence of both is possible. S3 does not depend on initial selection of representatives, and may be the process for selection of the first central vectors in k-means. S3 is an alternative to be considered in the unsupervised classification of medical images and data mining(AU)

¡Estudiando el Desarrollo Embrionario y Fetal con el Microscopio Virtual! En Tiempos de Covid-19 / Studying Embryonic and Fetal Development with the Virtual Microscope! In the Times of Covid-19

RESUMEN: La Microscopía Virtual es una herramienta tecnológica que permite la visualización de imágenes digitales microscópicas de gran resolución a través de un computador imitando la funcionalidad de un microscopio óptico tradicional. El presente trabajo presenta nuestra experiencia en el uso de esta modalidad de trabajo, útil hoy en día, en medio de la pandemia por Covid-19.

SUMMARY: Virtual Microscopy is a technological tool that allows the visualization of high resolution microscopic digital images through a computer, imitating the functionality of a traditional light microscope. The present work presents our experience in the use of this working modality, useful today, in the midst of the Covid-19 pandemic.

Research on a segmentation and evaluation method combining tooth morphology features / Investigación sobre un método de segmentación y evaluación que combina características morfológicas dentales

SUMMARY: To explore a new semi-automatic method to segment the teeth from the three-dimensional volume data which acquired from cone beam computed tomography (CBCT) scanner. Scanned dental cast models are used to evaluate the segmentation accuracy. The CBCT data are loaded to ORS software. Based on gray value, a semi-automatic method was used to segment teeth and then the segmented teeth were saved in STL format data. Smooth the mesh data in the Geomagic Studio software. The upper and lower dental cast models were scanned by a white light scanner and the data was saved in STL format too. After registering the model data to teeth data, the deviation between them was analyzed in the Geomagic Qualify. All teeth could be obtained, the method is simple to use and applied in orthodontic biomechanics. The entire process took less than 30 minutes. The actual measured Root Mean Square (RMS) value is 0.39 mm, less than 0.4 mm. This method can segment teeth from the jaw quickly and reliably with a little user intervention. The method has important significance for dental orthodontics, virtual jaw surgery simulation and other stomatology applications.

RESUMEN: El objetivo de este estudio fue explorar un nuevo método semiautomático para segmentar los dientes a partir de datos de volumen tridimensional adquiridos mediante escáner de tomografía computarizada de haz cónico (CBCT). Los modelos escaneados de moldes dentales se utilizan para evaluar la precisión de la segmentación. Para los datos CBCT se utilizó el software ORS, y basado en el valor gris, se usó un método semiautomático para segmentar los dientes los que posteriormente se guardaron en datos de formato STL. Los datos se ingresaron en el software Geomagic Studio. Los modelo dentales superior e inferior se escanearon con un escáner de luz blanca y la información también se guardó en formato STL. Después del registro y comparación de los datos del modelo y los datos de los dientes, la desviación entre estos se analizó en el programa Geomagic Qualify. Usando este método fue posible obtener de forma fácil todos los dientes y además aplicar en la biomecánica de ortodoncia. El proceso completo demoró menos de 30 minutos. El valor real medido de la raíz cuadrada media fue de 0,39 mm, menos de 0,4 mm. Este método puede segmentar los dientes mandibulares de forma rápida y confiable, con una mínima intervención del usuario. El método tiene una importancia crítica para la ortodoncia, simulaciones virtuales de las cirugías de la mandíbula y otras aplicaciones en estomatología.

Padronização do Sperm Class Analyzer® (SCA®) para avaliação de espermatozoides equinos criopreservados / [Standardization of the Sperm Class Analyzer® (SCA®) for the evaluation of cryopreserved equine sperm]

Objetivou-se, no primeiro experimento, avaliar o efeito da velocidade de captura de imagens de 25Hz, 30Hz e 50Hz na cinética dos espermatozoides equinos criopreservados. Todas as velocidades mostraram-se adequadas para capturar o movimento espermático (P>0,05). No segundo experimento, objetivou-se avaliar o efeito da deposição de sêmen em lâmina sob lamínula, Leja®10 e 20, na cinética espermática. O uso de lâmina e lamínula foi superior às lejas para manter a LIN e o WOB (P<0,05). No terceiro experimento, objetivou-se avaliar o efeito das concentrações de 25, 50 e 100x106 na cinética espermática. As concentrações de 25 e 50 x106 foram superiores a 100x106 para preservar a LIN, a STR e a BCF e não afetar negativamente a motilidade (P<0,05). No quarto experimento, objetivou-se avaliar o efeito dos diluidores BotuCrio®, BotuSêmen®, TALP sperm e da solução fisiológica na cinética espermática. O BotuCrio® foi superior a todos os diluidores em preservar a BCF e os hiperativos (P<0,05). Conclui-se que o emprego da velocidade de captura entre 25 e 50Hz, a deposição do sêmen entre lâmina e lamínula e a rediluição em diluidor de congelação para atingir 25 a 50x106 de espermatozoides/mL são ideais para o SCA® avaliar, de forma fidedigna, o sêmen equino criopreservado.(AU)

The objective of the first experiment was to evaluate the effect of 25, 30 and 50Hz frame acquisition rate on equine cryopreserved sperm. All frame acquisition rates tested were adequate to capture the sperm movement (P>0.05). The aim of the second experiment was to evaluate the effect of chambers, slide-coverslip, Leja®10 and 20 on sperm movement. The use of slide-coverslip was superior to maintain LIN and WOB (P<0.05). The aim of the third experiment was to evaluate the effect of 25, 50 and 100x106 sperm/mL concentration on sperm movement. Concentrations of 25 and 50x106 sperm/mL were greater than 100x106 to preserve LIN, STR and BCF and did not adversely affect motility (P<0.05). The aim of the fourth experiment was to evaluate the effect of BotuCrio®, BotuSêmen®, TALP sperm and physiological solution on sperm movement. BotuCrio® was superior among other extenders in preserving BCF and hyperactive (P<0.05). It is concluded that the use of the frame acquisition rate between 25 and 50 Hz; the deposition of semen between slide and coverslip and new dilution in the freezing extender to 25-50x106 of sperm/mL is ideal to reliably evaluate cryopreserved equine semen by SCA®.(AU)

Wide area digital dermoscopy applied to basal cell carcinoma

Abstract In situations in when a dermoscopic record of a large lesion is desirable, the resulting images are usually restricted to a small field of view due to the limited diameter of dermatoscope lenses. This limitation often produces several photographs separately, thus losing the possibility of a single-image global evaluation. In these case reports, we show examples of a recently published image montage technique called Wide Area Digital Dermoscopy, in this case, applied to basal cell carcinomas.