Diagnostic value of artificial intelligence-assisted diagnostic system for pulmonary cancer based on CT images: A systematic review and meta-analysis of 4 771 patients / 中国胸心血管外科临床杂志

@#Objective    To evaluate the diagnostic value of artificial intelligence (AI)-assisted diagnostic system for pulmonary cancer based on CT images. Methods    Databases including PubMed, The Cochrane Library, EMbase, CNKI, WanFang Data and Chinese BioMedical Literature Database (CBM) were electronically searched to collect relevant studies on AI-assisted diagnostic system in the diagnosis of pulmonary cancer from 2010 to 2019. The eligible studies were selected according to inclusion and exclusion criteria, and the quality of included studies was assessed and the special information was identified. Then, meta-analysis was performed using RevMan 5.3, Stata 12.0 and SAS 9.4 softwares. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio were pooled and the summary receiver operating characteristic (SROC) curve was drawn. Meta-regression analysis was used to explore the sources of heterogeneity. Results    Totally 18 studies were included with 4 771 patients. Random effect model was used for the analysis due to the heterogeneity among studies. The results of meta-analysis showed that the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnosis odds ratio and area under the SROC curve were   0.87 [95%CI (0.84, 0.90)], 0.89 [95%CI (0.84, 0.92)], 7.70 [95%CI (5.32, 11.15)], 0.14 [95%CI (0.11, 0.19)], 53.54 [95%CI (30.68, 93.42)] and 0.94 [95%CI (0.91, 0.95)], respectively. Conclusion    AI-assisted diagnostic system based on CT images has high diagnostic value for pulmonary cancer, and thus it is worthy of clinical application. However, due to the limited quality and quantity of included studies, above results should be validated by more studies.

Segmentation of organs at risk in nasopharyngeal cancer for radiotherapy using a self-adaptive Unet network / 南方医科大学学报

OBJECTIVE@#To investigate the accuracy of automatic segmentation of organs at risk (OARs) in radiotherapy for nasopharyngeal carcinoma (NPC).@*METHODS@#The CT image data of 147 NPC patients with manual segmentation of the OARs were randomized into the training set (115 cases), validation set (12 cases), and the test set (20 cases). An improved network based on three-dimensional (3D) Unet was established (named as AUnet) and its efficiency was improved through end-to-end training. Organ size was introduced as a priori knowledge to improve the performance of the model in convolution kernel size design, which enabled the network to better extract the features of different organs of different sizes. The adaptive histogram equalization algorithm was used to preprocess the input CT images to facilitate contour recognition. The similarity evaluation indexes, including Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD), were calculated to verify the validity of segmentation.@*RESULTS@#DSC and HD of the test dataset were 0.86±0.02 and 4.0±2.0 mm, respectively. No significant difference was found between the results of AUnet and manual segmentation of the OARs (@*CONCLUSIONS@#AUnet, an improved deep learning neural network, is capable of automatic segmentation of the OARs in radiotherapy for NPC based on CT images, and for most organs, the results are comparable to those of manual segmentation.

Morphometric Study Of Posterior Horn Of Lateral Ventricle And Its Correlation With Age And Side: A Ct Study

Enlargement of lateral ventricle is most striking feature after sixth decade of life. Regression of brain is normalageing process. But there are marked individual variations in this process. Lateral ventricular contours arerelatively constant, except for the occipital horns. Two major changes that may occur in elderly individualwithout neurologic deficits is enlargement of ventricles and cortical atrophy. This study is focusing on thechanges in posterior horn of lateral ventricle in different age groups. Aim of this study is to statistically analysethe length of the posterior horn of lateral ventricle in human and to correlate the changes in relation to age andside. Method: The CT images of 150 adult individuals (age group 20-80yrs) was studied in both males andfemales. Length of posterior horn of lateral ventricle was measured using dicomworks software. Result: Meanvalue of length of the posterior horn increases on both sides as the age increases. Values are larger in 61-80years. In relation to the side, the length of posterior horn is greater on the left side as compared to the right side.

Benign and malignant classification of renal occupancy lesions CT images using convolutional neural networks / 国际生物医学工程杂志

Objective To investigate the feasibility and application value of the benign and malignant classificational methods of renal occupying CT images based on convolutional neural networks (CNN). Methods An image omics method that can automatically learn the image features and classify CT images was used. Firstly, the CNN model obtained by large-scale natural image training was used to migrate the characteristics of the renal occupancy lesions CT images, and then the fine-tuning of the full connection layer was used to realize the benign and malignant classification of the images. Results The evaluation indexes of the VGG19 model were lower than ResNet50 and Inception V3, and the training result showed obvious overfitting. The accuracy, sensitivity and negative prediction values of the Inception V3 model was 93.8％, 99.5％ and 99.1％, respectively, which were higher than that of the ResNet50 model. Conclusions The benign and malignant classification of renal occupancy lesions CT images using CNN is a reasonable and feasible method, and the fine-tuned Inception V3 model has a better classification performance.

Automatic recognition of pulmonary nodules images based on deep belief networks / 国际生物医学工程杂志

Objective To propose a method based on deep belief network (DBN) to automatically identify pulmonary nodules so as to improve the detection accuracy of pulmonary nodules.Methods To meet the training sample requirements of DBN,a database of 4 000 lung nodule images identified by professional doctors was established,and the sample database was expanded using virtual sample technology.In this technology,new samples of the database were generated from the manually recognized region of interest (ROI) by rotation,scaling and panning,or by a series of combinations of two or more operations of panning,scaling,rotation,and compositing.Finally,some samples from the sample database were input into the convolutional neural network classifier,and the ROI of the suspected pulmonary nodule was output by optimizing the network parameters.Result The sample size of the training sample database was expanded to 40 000 using the virtual sample expansion.Based on the training database obtained by this method,the detection accuracy of DBN for identifying pulmonary nodules was 90％,and the false positive rate was 0.4％.Conclusion Virtual sample technology can effectively improve the efficiency of training database establishment.The accuracy of using DBN-based CAD technology to detect pulmonary nodules is high,allowing doctors to focus only on areas where lung nodules are detected,thus effectively improving the efficiency of diagnosis.

Morphometric Study of Fourth Ventricle By Computerised Tomography.

Background: The two major changes that may occur in elderly individual without neurologic deficits is enlargement of ventricles and cortical atrophy. Aim of the study was to statistically analyse the dimensions of Fourth ventricle in humans and also to study the Changes that occur during ageing. Ventricular size of males and females was compared. METHOD: The CT images of 112 adult individuals (Age Group 21-60) and 88 ageing individuals (Age above 61) was studied in both males and females. Measurements like vertical length, height, anterior-posterior diameter and transverse diameter of fourth ventricle was made by using dicomworks software. RESULT: This study showed positive co-relation of age with dimensions of fourth ventricle and the dimensions of the fourth ventricle were enlarged with physiologic ageing. Also the dimensions of fourth ventricle were more in males as compared to females.

Non-metric Study of the External Occipital Protuberance for Sex Determination in Koreans: Using Three-dimensional Reconstruction Images / 체질인류학회지

The most essential biological profiles in physical and forensic anthropology are age, sex, and populations to be determined. In case of dealing intact skeletons, experts can often determine sex with high accuracy. The external occipital protuberance (EOP) is one of the site among morphologic traits which is used to determine human sex. This study suggests the possibility to determine the sexual dimorphism using the EOP and surrounding anatomical structures in Koreans. After three-dimensional reconstruction of the skull model from Digital Korean Human Databse, the three parts were evaluated using a classification system based on Broca, Gulekon and Turgut. To determine for scoring, this study was used two in two different ways to observe the skull model; one was a lateral view and the other was turning the skull models. In a lateral view, the shape of the occipital area was classified as 'flat' or 'convex' type. After then the scores of the anatomical structures were converted into 4-digits code. In females, the skull was more convex in shape than males but the EOP and inion were lesser projection. In the lateral and turning views, the most common pattern was Type 2 in both sexes. The most common digit code was 2-2-2-0 in males, 2-2-2-1 in females. The digit code is better than simple scoring system for determining sex. The skull in Koreans were more feminine than in other populations in both sexes.

Lung disease detection using feature extraction and extreme learning machine

INTRODUCTION: The World Health Organization estimates that by 2030 the Chronic Obstructive Pulmonary Disease (COPD) will be the third leading cause of death worldwide. Computerized Tomography (CT) images of lungs comprise a number of structures that are relevant for pulmonary disease diagnosis and analysis. METHODS: In this paper, we employ the Adaptive Crisp Active Contour Models (ACACM) for lung structure segmentation. And we propose a novel method for lung disease detection based on feature extraction of ACACM segmented images within the cooccurrence statistics framework. The spatial interdependence matrix (SIM) synthesizes the structural information of lung image structures in terms of three attributes. Finally, we perform a classification experiment on this set of attributes to discriminate two types of lung diseases and health lungs. We evaluate the discrimination ability of the proposed lung image descriptors using an extreme learning machine neural network (ELMNN) comprising 4-10 neurons in the hidden layer and 3 neurons in the output layer to map each pulmonary condition. This network was trained and validated by applying a holdout procedure. RESULTS: The experimental results achieved 96% accuracy demonstrating the effectiveness of the proposed method on identifying normal lungs and diseases as COPD and fibrosis. CONCLUSION: Our results lead to conclude that the method is suitable to integrate clinical decision support systems for pulmonary screening and diagnosis.

3D finite element modeling and analysis of the whole cervical spine based on Simpleware / 医用生物力学

Objective To build a 3D finite element model of the whole cervical spine by using Simpleware software, as well as validate and analyze the model, so as to provide a reliable model for exploring the mechanism of cervical spine injury. Methods The 3D entity model of the whole cervical spine C1-7 was established based on CT tomography images, medical image processing software Simpleware, reverse engineering software Geomagic, which was imported to Hypermesh for meshing, adding ligaments and introducing facet joint contact relation, etc., thus to establish the finite element model of the whole cervical spine C1-7. Biomechanical properties of the cervical spine under flexion, extension, lateral bending and torsion were simulated by ANSYS. Results The established model was proved to be accurate and reliable, and its range of motion (ROM) under flexion, extension, lateral bending and axial rotation was similar to in vitro experiment and finite element analysis results in related literatures. The stress of intervertebral disc was concentrated on the compression side of the vertebral body, and the cervical spine C4/5 was more prone to have a stress concentration. Conclusions The finite element model of the whole cervical spine C1-7 can effectively simulate the biomechanical characteristics of the cervical vertebra, which establishes a good foundation for the follow-up studies on whiplash injury of the cervical spine.

Reconstruction and measurement of cranial CT images of safety distance and angle in unilateral nasal transsphenoidal approach for pituitary surgery / 吉林大学学报(医学版)

Objective To measure the reconstructed cranial CT images,and to clarify the safety range of unilateral nasal transsphenoidal approach for pituitary surgery.Methods 100 normal pituitary cranial CT images were randomly selected,and the three-dimensional reconstruction was performed by using the CT images of perpendicular and parallel to the edge of the two eyes as base line, and the distance and angle in unilateral nasal transsphenoidal approach for pituitary surgery from the sagittal plane in the middle of the nasal meatus and the plane through the tip of the nose and both ends of dorsum sellae were measured,respectively. The angles and distances were compared when grouped the data by gender and age. Results Angle A1 (the angle between the tip of the nose and the tuberculum sellae and saddle back root line in the sagittal plane)in the sagittal plane of the middle nasal meatus was (11.22±1.35)°,95% confidence interval was 8.92°-13.76°degrees;the distance D1(the distance on the line between tuberculum sellae and saddle back root, and the line was formed by the plate contained the angle A1 and sellar floor)was (16.71 ± 2.07)mm,95% confidence interval was 13.11-19.93 mm.Angle A2 (the angle between the tip of the nose and the saddle back ends)which was in the plane through the tip of the nose and both ends of dorsum sellae was (8.91±1.19)°,95% confidence interval was 7.12°-10.72°;the distance D2(the distance on the line between the saddle back ends,and the line was formed by the plate contained the angle A2 and sellar floor)was (14.23±2.09)mm,95% confidence interval was 10.81-17.92 mm. The four parameter data was normally distributed,and there was no significantly statistic difference between different gender and ages (P>0.05).Conclusion The angle of the movement for unilateral nasal transsphenoidal approach for pituitary surgery operation in the sagittal plane in the middle of the nasal meatus should be less than (11.22 ± 1.35)°,and the distance of the movement should be less than (16.71±2.07)mm. The angle of the movement in the plane through the tip of the nose and both ends of dorsum sellae should be less than (8.9 1 ± 1.1 9 )°, and the distance of the movement should be less than (14.23±2.09)mm.

Modelo de Contorno Ativo Crisp Adaptativo 2D aplicado na segmentação dos pulmões em imagens de TC do tórax de voluntários sadios e pacientes com enfisema pulmonar / Adaptive 2D Crisp Active Contour Model applied to lung segmentation in CT images of the thorax of healthy volunteers and patients with pulmonary emphysema

INTRODUÇÃO: Dentre as doenças que afetam a população mundial, destaca-se a preocupação com a Doença Pulmonar Obstrutiva Crônica (DPOC), que, segundo a Organização Mundial de Saúde, pode se constituir na terceira causa de morte mais importante em todo mundo no ano de 2030. Visando contribuir com o auxílio ao diagnóstico médico, esta pesquisa centraliza seus esforços na etapa de segmentação dos pulmões, visto que esta é a etapa básica de sistema de Visão Computacional na area de pneumologia. MÉTODOS: Este trabalho propõe um novo método de segmentação dos pulmões em imagens de Tomografia Computadorizada (TC) do tórax chamado de Método de Contorno Ativo (MCA) Crisp Adaptativo 2D. Este MCA consiste em traçar automaticamente uma curva inicial dentro dos pulmões, que se deforma por iterações sucessivas, minimizando energias que atuam sobre a mesma, deslocando-a até as bordas do objeto. O MCA proposto é o resultado do aperfeiçoamento do MCA Crisp desenvolvido previamente, visando aumentar a sua exatidão, diminuindo o tempo de análise e reduzindo a subjetividade na segmentação e análise dos pulmões dessas imagens pelos médicos especialistas. Este método por iterações sucessivas de minimização de sua energia, segmenta de forma automática os pulmões em imagens de TC do tórax. RESULTADOS: Para sua validação, o MCA Crisp Adaptativo é comparado com os MCAs THRMulti, THRMod, GVF, VFC, Crisp e também com o sistema SISDEP, sendo esta avaliação realizada utilizando como referência 24 imagens, sendo 12 de pacientes com DPOC e 12 de voluntários sadios, segmentadas manualmente por um pneumologista. Os resultados obtidos demonstram que o método proposto é superior aos demais. CONCLUSÃO: Diante dos resultados obtidos, pode-se concluir que este método pode integrar sistemas de auxílio ao diagnóstico médico na área de Pneumologia.

INTRODUCTION: Among the diseases that affect the world's population, there is concern about Chronic Obstructive Pulmonary Disease (COPD), that, according to the World Health Organization, could be the leading cause of death worldwide by the year 2030. Aiming to contribute to aid medical diagnosis, this research focuses its efforts on the segmentation of the lungs, since this is the basic step system in the area of Computer Vision pulmonology. METHODS: This paper proposes a new method for segmentation of lung images in Computed Tomography (CT) of the chest called Active Contour Method (MCA) Crisp Adaptive 2D. This MCA is to draw a curve starting inside an object of interest. This curve is deformed by successive iterations, minimizing energies that act on it, moving it to the edges of the object. The MCA is the improvement of the proposed MCA Crisp previously developed, aiming to increase the accuracy, decreasing analysis time and reducing the subjectivity in the segmentation and analysis of the lungs of these images by pulmonologists. This method is automatically initialized in the lungs and on successive iterations to minimize this energy, this MCA automatically targets the lungs in chest CT images. RESULTS: To evaluate the proposed method, the MCA Adaptive Crisp is compared with MCAs THRMulti, THRMod, GVF, VFC, Crisp and also with the system SISDEP, this assessment is performed using reference images 24, 12 COPD patients and 12 volunteers healthy, manually segmented by a pulmonologist. The results show that the proposed method is superior to the others. CONCLUSION: Based on the results, it can be concluded that this method can integrate systems aid in the medical diagnosis of Pulmonology.

Bone cortex segmentation of CT images based on BP neural network / 医用生物力学

Objective To measure the bone mass, the shape of bones and the bone strength through segmentation of the bone cortex in CT images, and to calculate the corresponding parameters in histomorphometry. Methods CT images were first interpreted through the DCMTK to draw information of the corresponding images, then the OpenCV are used for preprocessing on the basis of ROI (range of interest), and the texture features of the image were extracted as the input vector. Results of the manual segmentation were used as the mentor signal to train BP neural network, which were then used for segmenting the bone cortex in a sequence of CT images. Results of the segmentation were further processed and displayed. Results The segmentation efficiency of the bone cortex in CT images through neural network met the needs of the practical application. The separation results showed an obvious shape of the bone cortex with easy distinguishing from the surrounding tissues, which could satisfy the demand of the clinical diagnosis. Conclusions When the texture features of the bone cortex are evident, this method can achieve a more satisfying segmentation effect with smooth contours, high segmentation accuracy and strong adaptability. With less artificial intervention in the process of the image segmentation, this method can be used for batch CT image segmentation of a complete set of the bone cortex. The inadequacy of the method lies in relatively longer training time demanded for the neural network training.