Volumetric Assessment of the Insula in a Normally Functioning Human Brain Using Magnetic Resonance Imaging / Evaluación Volumétrica de la Ínsula en un Cerebro Humano que Funciona Normalmente Utilizando Imágenes de Resonancia Magnética

SUMMARY: Volumetric assessment of brain structures is an important tool in neuroscience research and clinical practice. The volumetric measurement of normally functioning human brain helps detect age-related changes in some regions, which can be observed at varying degrees. This study aims to estimate the insular volume in the normally functioning human brain in both genders, different age groups, and side variations. A cross-sectional retrospective study was conducted on 42 adult Sudanese participants in Al-Amal Hospital, Sudan, between May to August 2022, using magnetic resonance imaging (MRI) and automatic brain segmentation through a software program (BrainSuite). The statistical difference in total insular volume on both sides of the cerebral hemisphere was small. The insular volume on the right side was greater in males, while the left side showed no difference between both genders. A statistically significant difference between males and females was found (p > 0.05), and no statistical difference in different age groups was found according to the one-way ANOVA test (p>0.05). Adult Sudanese males showed a larger insular volume than females. MRI can be used to morphometrically assess the insula to detect any pathological variations based on volume changes.

La evaluación volumétrica de las estructuras cerebrales es una herramienta importante en la investigación y la práctica clínica de la neurociencia. La medición volumétrica del cerebro humano, que funciona normalmente, ayuda a detectar cambios relacionados con la edad en algunas regiones, las cuales se pueden observar en diversos grados. Este estudio tuvo como objetivo estimar el volumen insular en el cerebro humano que funciona normalmente, en ambos sexos, de diferentes grupos de edad y sus variaciones laterales. Se realizó un estudio retrospectivo transversal en 42 participantes sudaneses adultos en el Hospital Al-Amal, Sudán, entre mayo y agosto de 2022, utilizando imágenes de resonancia magnética y segmentación automática del cerebro a través de un software (BrainSuite). Fue pequeña la diferencia estadística en el volumen insular total, en los hemisferios cerebrales. El volumen insular del lado derecho fue mayor en los hombres, mientras que el lado izquierdo no mostró diferencia entre ambos sexos. Se encontró una diferencia estadísticamente significativa entre hombres y mujeres (p > 0,05), y no se encontró diferencia estadística en los diferentes grupos de edad, según la prueba de ANOVA de una vía (p> 0,05). Los hombres sudaneses adultos mostraron un mayor volumen insular que las mujeres. La resonancia magnética se puede utilizar para evaluar morfométricamente la ínsula y para detectar cualquier variación patológica basada en cambios de volumen.

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.

Research status and outlook of deep learning in oral and maxillofacial medical imaging / 中华口腔医学杂志

Artificial intelligence, represented by deep learning, has received increasing attention in the field of oral and maxillofacial medical imaging, which has been widely studied in image analysis and image quality improvement. This narrative review provides an insight into the following applications of deep learning in oral and maxillofacial imaging: detection, recognition and segmentation of teeth and other anatomical structures, detection and diagnosis of oral and maxillofacial diseases, and forensic personal identification. In addition, the limitations of the studies and the directions for future development are summarized.

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.

Discussion on Technical Evaluation of Tongue Diagnosis Equipment of Traditional Chinese Medicine / 中国医疗器械杂志

This study briefly introduces the tongue diagnostic equipment of traditional Chinese medicine. It analyzes and discusses the key points of technical evaluation of tongue diagnostic equipment from the aspects of product name, performance parameters, image processing functions, product use methods, clinical evaluation, etc. It analyzes the safety risks and effectiveness indicators of tongue diagnostic equipment, hoping to bring some help to the gradual standardization of tongue diagnostic equipment and the registration of enterprises.

Evaluation of PET Mainstream Scattering Correction Methods / 中国医疗器械杂志

OBJECTIVE@#Current mainstream PET scattering correction methods are introduced and evaluated horizontally, and finally, the existing problems and development direction of scattering correction are discussed.@*METHODS@#Based on NeuWise Pro PET/CT products of Neusoft Medical System Co. Ltd. , the simulation experiment is carried out to evaluate the influence of radionuclide distribution out of FOV (field of view) on the scattering estimation accuracy of each method.@*RESULTS@#The scattering events produced by radionuclide out of FOV have an obvious impact on the spatial distribution of scattering, which should be considered in the model. The scattering estimation accuracy of Monte Carlo method is higher than single scatter simulation (SSS).@*CONCLUSIONS@#Clinically, if the activity of the adjacent parts out of the FOV is high, such as brain, liver, kidney and bladder, it is likely to lead to the deviation of scattering estimation. Considering the Monte Carlo scattering estimation of the distribution of radionuclide out of FOV, it's helpful to improve the accuracy of scattering distribution estimation.

Application of Novel Down-sampling Method in Retinal Vessel Segmentation / 中国医疗器械杂志

Accurate segmentation of retinal blood vessels is of great significance for diagnosing, preventing and detecting eye diseases. In recent years, the U-Net network and its various variants have reached advanced level in the field of medical image segmentation. Most of these networks choose to use simple max pooling to down-sample the intermediate feature layer of the image, which is easy to lose part of the information, so this study proposes a simple and effective new down-sampling method Pixel Fusion-pooling (PF-pooling), which can well fuse the adjacent pixel information of the image. The down-sampling method proposed in this study is a lightweight general module that can be effectively integrated into various network architectures based on convolutional operations. The experimental results on the DRIVE and STARE datasets show that the F1-score index of the U-Net model using PF-pooling on the STARE dataset improved by 1.98%. The accuracy rate is increased by 0.2%, and the sensitivity is increased by 3.88%. And the generalization of the proposed module is verified by replacing different algorithm models. The results show that PF-pooling has achieved performance improvement in both Dense-UNet and Res-UNet models, and has good universality.

Analysis on characteristic of stage Ⅰ occupational cement pneumoconiosis patients / 中华劳动卫生职业病杂志

Objective: To analyze the clinical and imaging characteristics of stage Ⅰ occupational cement pneumoconiosis patients. Methods: In October 2021, the data of patients with occupational cement pneumoconiosis diagnosed by the Third Hospital of Peking University from 2014 to 2020 were collected, and the data of the patients' initial exposure age, dust exposure duration, diagnosis age, incubation period, chest X-ray findings, lung function and other data were analyzed retrospectively. Spearman grade correlation was used for correlation analysis of grade count data. The influencing factors of lung function were analyzed by binary logistic regression. Results: A total of 107 patients were enrolled in the study. There were 80 male patients and 27 female patients. The inital exposure age was (26.2±7.7) years, the diagnosis age was (59.4±7.9) years, the dust exposure duration was (17.9±8.0) years, and the incubation period was (33.1±10.3) years. The initial dust exposure age and the dust exposure duration in female patients were less than those in men, and the incubation period was longer than that in men (P<0.05). The imaging analysis showed the small opacities as"pp"accounted for 54.2%. 82 patients (76.6%) had small opacities distributed in two lung areas. The lung areas distribution of small opacities in female patients was less than that in male patients (2.04±0.19 vs 2.41±0.69, P<0.001). There were 57 cases of normal pulmonary function, 41 cases of mild abnormality and 9 cases of moderate abnormality. The number of lung regions with small opacities on X-ray was the risk factor for abnormal lung function in cement pneumoconiosis patients (OR=2.491, 95%CI=1.197-5.183, P=0.015) . Conclusion: The patients with occupational cement pneumoconiosis had long dust exposure duration and incubation period, light imaging changes and pulmonary function damage. The abnormal lung function was related to the range of pulmonary involvement.

A survey of loss function of medical image segmentation algorithms / 生物医学工程学杂志

Medical image segmentation based on deep learning has become a powerful tool in the field of medical image processing. Due to the special nature of medical images, image segmentation algorithms based on deep learning face problems such as sample imbalance, edge blur, false positive, false negative, etc. In view of these problems, researchers mostly improve the network structure, but rarely improve from the unstructured aspect. The loss function is an important part of the segmentation method based on deep learning. The improvement of the loss function can improve the segmentation effect of the network from the root, and the loss function is independent of the network structure, which can be used in various network models and segmentation tasks in plug and play. Starting from the difficulties in medical image segmentation, this paper first introduces the loss function and improvement strategies to solve the problems of sample imbalance, edge blur, false positive and false negative. Then the difficulties encountered in the improvement of the current loss function are analyzed. Finally, the future research directions are prospected. This paper provides a reference for the reasonable selection, improvement or innovation of loss function, and guides the direction for the follow-up research of loss function.

Colorectal polyp segmentation method based on fusion of transformer and cross-level phase awareness / 生物医学工程学杂志

In order to address the issues of spatial induction bias and lack of effective representation of global contextual information in colon polyp image segmentation, which lead to the loss of edge details and mis-segmentation of lesion areas, a colon polyp segmentation method that combines Transformer and cross-level phase-awareness is proposed. The method started from the perspective of global feature transformation, and used a hierarchical Transformer encoder to extract semantic information and spatial details of lesion areas layer by layer. Secondly, a phase-aware fusion module (PAFM) was designed to capture cross-level interaction information and effectively aggregate multi-scale contextual information. Thirdly, a position oriented functional module (POF) was designed to effectively integrate global and local feature information, fill in semantic gaps, and suppress background noise. Fourthly, a residual axis reverse attention module (RA-IA) was used to improve the network's ability to recognize edge pixels. The proposed method was experimentally tested on public datasets CVC-ClinicDB, Kvasir, CVC-ColonDB, and EITS, with Dice similarity coefficients of 94.04%, 92.04%, 80.78%, and 76.80%, respectively, and mean intersection over union of 89.31%, 86.81%, 73.55%, and 69.10%, respectively. The simulation experimental results show that the proposed method can effectively segment colon polyp images, providing a new window for the diagnosis of colon polyps.

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.

A meta-learning based method for segmentation of few-shot magnetic resonance images / 生物医学工程学杂志

When applying deep learning algorithms to magnetic resonance (MR) image segmentation, a large number of annotated images are required as data support. However, the specificity of MR images makes it difficult and costly to acquire large amounts of annotated image data. To reduce the dependence of MR image segmentation on a large amount of annotated data, this paper proposes a meta-learning U-shaped network (Meta-UNet) for few-shot MR image segmentation. Meta-UNet can use a small amount of annotated image data to complete the task of MR image segmentation and obtain good segmentation results. Meta-UNet improves U-Net by introducing dilated convolution, which can increase the receptive field of the model to improve the sensitivity to targets of different scales. We introduce the attention mechanism to improve the adaptability of the model to different scales. We introduce the meta-learning mechanism, and employ a composite loss function for well-supervised and effective bootstrapping of model training. We use the proposed Meta-UNet model to train on different segmentation tasks, and then use the trained model to evaluate on a new segmentation task, where the Meta-UNet model achieves high-precision segmentation of target images. Meta-UNet has a certain improvement in mean Dice similarity coefficient (DSC) compared with voxel morph network (VoxelMorph), data augmentation using learned transformations (DataAug) and label transfer network (LT-Net). Experiments show that the proposed method can effectively perform MR image segmentation using a small number of samples. It provides a reliable aid for clinical diagnosis and treatment.

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.

Aplicación de modelos 3D en cirugía hepática / Implementation of three-dimensional printed models in hepatic surgery

RESUMEN La impresión de modelos tridimensionales (M3D) implica obtener una estructura sólida y formada a partir de un modelo digital. Para la reconstrucción 3D se utilizó tomografía computarizada contrastada, realizándose impresión de modelos sobre la base de las principales estructuras anatómicas hepáticas. Se utilizaron M3D en dos pacientes con indicación quirúrgica, una mujer con trombocitopenia familiar y metástasis hepática de adenocarcinoma rectal, sin respuesta a quimioterapia, y un hombre con hepatopatía infecciosa crónica y diagnóstico de carcinoma hepatocelular. La aplicación de M3D resultó de gran utilidad, pues permitió un mejor entendimiento de la relación espacial de las estructuras anatómicas en ambos casos. En nuestra experiencia, la aplicación de M3D fue muy útil para planificar la cirugía y dar una aproximación más certera de los reparos anatómicos. El modelo se obtuvo en 7 días y costó 380 dólares, un valor elevado para nuestro medio.

ABSTRACT Three-dimensional (3D) printing is the construction of a solid structure from a digital model. 3D reconstruction was performed using contrast-enhanced computed tomography scan, and 3D-printed models were built based on the main anatomic structures of the liver. 3D-printed models were used in two patients with indication of surgery; one woman with inherited thrombocytopenia and liver metastases from colorectal adenocarcinoma with no response to chemotherapy, and one man with chronic liver infection and hepatocellular carcinoma. The implementation of 3D printing technology was very useful, as it facilitated the understanding of the spatial relationships among the anatomical structures in both cases. In our experience, the use of 3D-printed models was very useful for preoperative planning and for understanding the anatomic landmarks. The model was built in 7 days, with a cost of 380 dollars which is elevated in our environment.

Impact of different evaluation methods for diagnostic tasks in panoramic radiography / Impacto de diferentes métodos de evaluación durante el diagnóstico en la radiografía panorámica

Abstract The aim of this study was to evaluate the observers diagnostic performance in panoramic radiography using monitor, tablet, X-ray image view box, and against window daylight as a visualization method in different diagnostic tasks. Thirty panoramic radiography were assessed by three calibrated observers for each visualization method, in standardized light conditions, concerning dental caries, widened periodontal ligament space, and periapical bone defects from the four first molars; mucosal thickening and retention cysts in maxillary sinus; and stylo-hyoid ligament calcification and atheroma. A five-point confidence scale was used. The standard-reference was performed by two experienced observers. Diagnostic values using window light were significantly lower for caries and periapical bone defect and retention cyst, stylo-hyoid ligament calcification detection (p<0.05). For atheroma detection, X-ray image view box, tablet, and widow light had lower accuracy than the evaluation on the monitor (p<0.05). Observers diagnostic performances are worsened using window light as an evaluation method for panoramic radiography for dental, sinus, and calcification disorders, while the monitor was the most reliable method.

Resumen El objetivo de este estudio fue evaluar el desempeño diagnóstico de los observadores en la radiografía panorámica utilizando monitor, tablet, caja de visualización de imágenes de rayos X y contra la luz del día de la ventana como método de visualización en diferentes tareas de diagnóstico. Treinta radiografías panorámicas fueron evaluadas por tres observadores calibrados para cada método de visualización, en condiciones de luz estandarizadas, con respecto a caries dental, espacio del ligamento periodontal ensanchado y defectos óseos periapicales de los cuatro primeros molares; engrosamiento de la mucosa y quistes de retención en el seno maxilar; y calcificación y ateroma del ligamento estilohioideo. Se utilizó una escala de confianza de cinco puntos. La referencia estándar fue realizada por dos observadores experimentados. Los valores diagnósticos con luz de ventana fueron significativamente menores para caries y defecto óseo periapical y quiste de retención, detección de calcificación del ligamento estilohioideo (p <0.05). Para la detección de ateroma, la caja de visualización de imágenes de rayos X, el tablet y la luz de viuda tuvieron una precisión menor que la evaluación en el monitor (p <0.05). El rendimiento diagnóstico del observador empeora al utilizar la luz de la ventana como método de evaluación de la radiografía panorámica para los trastornos dentales, de los senos nasales y de la calcificación, mientras que el monitor fue el método más fiable.

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)