RESUMEN

Carotid cavernous fistula is a rare but clinically important vascular abnormality that is challenging to diagnose and treat. The clinical data of a patient with bilateral carotid cavernous fistula diagnosed by CT images were retrospectively analyzed. Through the analysis of CT images, the patient was accurately located and the diagnosis was confirmed. CT images can provide detailed anatomical information and accurately show the location, morphology and hemodynamic characteristics of carotid cavernous fistula. Through CT image examination, we successfully diagnosed bilateral carotid cavernous fistula patients, and can provide an important reference for surgical treatment. Therefore, CT image examination can provide accurate diagnosis and surgical planning information, and provide support for the formulation of individual treatment plans for patients. The application of this method is helpful to improve the early diagnosis rate and treatment effect of carotid cavernous fistula.

RESUMEN

CycleGAN has been leveraged to synthesize a CT image from an available MR image after trained on unpaired data. Due to the lack of direct constraints between the synthetic and the input images, CycleGAN cannot guarantee structural consistency and often generates inaccurate mappings that shift the anatomy, which is highly undesirable for downstream clinical applications such as MRI-guided radiotherapy treatment planning and PET/MRI attenuation correction. In this paper, we propose a cycle-consistent and semantics-preserving generative adversarial network, referred as CycleSGAN, for unpaired MR-to-CT image synthesis. Our design features a novel and generic way to incorporate semantic information into CycleGAN. This is done by designing a pair of three-player games within the CycleGAN framework where each three-player game consists of one generator and two discriminators to formulate two distinct types of adversarial learning: appearance adversarial learning and structure adversarial learning. These two types of adversarial learning are alternately trained to ensure both realistic image synthesis and semantic structure preservation. Results on unpaired hip MR-to-CT image synthesis show that our method produces better synthetic CT images in both accuracy and visual quality as compared to other state-of-the-art (SOTA) unpaired MR-to-CT image synthesis methods.

RESUMEN

Lung cancer is a predominant cause of cancer-related mortality worldwide, necessitating precise tumor segmentation of medical images for accurate diagnosis and treatment. However, the intrinsic complexity and variability of tumor morphology pose substantial challenges to segmentation tasks. To address this issue, we propose a multitask connected U-Net model with a teacher-student framework to enhance the effectiveness of lung tumor segmentation. The proposed model and framework integrate PET knowledge into the segmentation process, leveraging complementary information from both CT and PET modalities to improve segmentation performance. Additionally, we implemented a tumor area detection method to enhance tumor segmentation performance. In extensive experiments on four datasets, the average Dice coefficient of 0.56, obtained using our model, surpassed those of existing methods such as Segformer (0.51), Transformer (0.50), and UctransNet (0.43). These findings validate the efficacy of the proposed method in lung tumor segmentation tasks.

RESUMEN

PURPOSE: Accurate segmentation of tubular structures is crucial for clinical diagnosis and treatment but is challenging due to their complex branching structures and volume imbalance. The purpose of this study is to propose a 3D deep learning network that incorporates skeleton information to enhance segmentation accuracy in these tubular structures. METHODS: Our approach employs a 3D convolutional network to extract 3D tubular structures from medical images such as CT volumetric images. We introduce a skeleton-guided module that operates on extracted features to capture and preserve the skeleton information in the segmentation results. Additionally, to effectively train our deep model in leveraging skeleton information, we propose a sigmoid-adaptive Tversky loss function which is specifically designed for skeleton segmentation. RESULTS: We conducted experiments on two distinct 3D medical image datasets. The first dataset consisted of 90 cases of chest CT volumetric images, while the second dataset comprised 35 cases of abdominal CT volumetric images. Comparative analysis with previous segmentation approaches demonstrated the superior performance of our method. For the airway segmentation task, our method achieved an average tree length rate of 93.0%, a branch detection rate of 91.5%, and a precision rate of 90.0%. In the case of abdominal artery segmentation, our method attained an average precision rate of 97.7%, a recall rate of 91.7%, and an F-measure of 94.6%. CONCLUSION: We present a skeleton-guided 3D convolutional network to segment tubular structures from 3D medical images. Our skeleton-guided 3D convolutional network could effectively segment small tubular structures, outperforming previous methods.

RESUMEN

Introduction: The treatment of COVID-19 patients, especially high-risk patients, remains a large challenge. Glucocorticoids have been accepted as effective medicines for severe COVID-19. However, the glucocorticoid usage guidelines do not cover all the indications for high-risk patients. Objective: To identify more effective treatments for high-risk patients with COVID-19, this retrospective study analyzed routine epidemiological, clinical, and laboratory data from 33 high-risk patients with COVID-19 in Beijing Gobroad Boren Hospital, Beijing, China, most of whom responded well to treatment. Methods: Severe acute respiratory syndrome coronavirus-2 infection was confirmed via real-time reverse transcriptase polymerase chain reaction assays. Outcome measures such as duration of mechanical ventilation, intensive care unit length of stay, and 28-day mortality were analyzed. Patients were divided into two groups: mild to moderate COVID-19 (n = 26) and severe COVID-19 (n = 7). Chest computed tomography images were used to guide methylprednisolone administration or withdrawal. Results: Upon intensive care unit admission, 12.1% of patients were mechanically ventilated with an average partial pressure of oxygen/fraction of inspired oxygen(PaO2/FiO2) ratio of 279 ± 146. No coinfections with other endemic viruses were observed. The duration of mechanical ventilation was 16 days (interquartile range: 8-28); the intensive care unit length of stay was 11 (interquartile range: 2-33) days; and the 28-day total mortality was 3.0%. Conclusion: Multivariate regression analysis revealed that low-dose, timely methylprednisolone administration was associated with a lower severe COVID-19 rate and mortality in high-risk patients. For high-risk patients, once there are ground-glass opacities (GGO) in the computed tomography image, continuous and low-dose methylprednisolone administration promotes inflammation remission and protects them from severe COVID-19 or mortality.

RESUMEN

BACKGROUND: Pneumoconiosis has a significant impact on the quality of patient survival. This study aims to evaluate the performance and application value of improved Unet network technology in the recognition and segmentation of lesion areas of lung CT images in patients with pneumoconiosis. METHODS: A total of 1212 lung CT images of patients with pneumoconiosis were retrospectively included. The improved Unet network was used to identify and segment the CT image regions of the patients' lungs, and the image data of the granular regions of the lungs were processed by the watershed and region growing algorithms. After random sorting, 848 data were selected into the training set and 364 data into the validation set. The experimental dataset underwent data augmentation and were used for model training and validation to evaluate segmentation performance. The segmentation results were compared with FCN-8s, Unet network (Base), Unet (Squeeze-and-Excitation, SE + Rectified Linear Unit, ReLU), and Unet + + networks. RESULTS: In the segmentation of lung CT granular region with the improved Unet network, the four evaluation indexes of Dice similarity coefficient, positive prediction value (PPV), sensitivity coefficient (SC) and mean intersection over union (MIoU) reached 0.848, 0.884, 0.895 and 0.885, respectively, increasing by 7.6%, 13.3%, 3.9% and 6.4%, respectively, compared with those of Unet network (Base), and increasing by 187.5%, 249.4%, 131.9% and 51.0%, respectively, compared with those of FCN-8s, and increasing by 14.0%, 31.2%, 4.7% and 9.7%, respectively, compared with those of Unet network (SE + ReLU), while the segmentation performance was also not inferior to that of the Unet + + network. CONCLUSIONS: The improved Unet network proposed shows good performance in the recognition and segmentation of abnormal regions in lung CT images in patients with pneumoconiosis, showing potential application value for assisting clinical decision-making.

Asunto(s)

RESUMEN

To study plant organs, it is necessary to investigate the three-dimensional (3D) structures of plants. In recent years, non-destructive measurements through computed tomography (CT) have been used to understand the 3D structures of plants. In this study, we use the Chrysanthemum seticuspe capitulum inflorescence as an example and focus on contact points between the receptacles and florets within the 3D capitulum inflorescence bud structure to investigate the 3D arrangement of the florets on the receptacle. To determine the 3D order of the contact points, we constructed slice images from the CT volume data and detected the receptacles and florets in the image. However, because each CT sample comprises hundreds of slice images to be processed and each C. seticuspe capitulum inflorescence comprises several florets, manually detecting the receptacles and florets is labor-intensive. Therefore, we propose an automatic contact point detection method based on CT slice images using image recognition techniques. The proposed method improves the accuracy of contact point detection using prior knowledge that contact points exist only around the receptacle. In addition, the integration of the detection results enables the estimation of the 3D position of the contact points. According to the experimental results, we confirmed that the proposed method can detect contacts on slice images with high accuracy and estimate their 3D positions through clustering. Additionally, the sample-independent experiments showed that the proposed method achieved the same detection accuracy as sample-dependent experiments.

RESUMEN

Urinary toxicities are one of the serious complications of radiotherapy for prostate cancer, and dose-volume histogram of prostatic urethra has been associated with such toxicities in previous reports. Previous research has focused on estimating the prostatic urethra, which is difficult to delineate in CT images; however, these studies, which are limited in number, mainly focused on cases undergoing brachytherapy uses low-dose-rate sources and do not involve external beam radiation therapy (EBRT). In this study, we aimed to develop a deep learning-based method of determining the position of the prostatic urethra in patients eligible for EBRT. We used contour data from 430 patients with localized prostate cancer. In all cases, a urethral catheter was placed when planning CT to identify the prostatic urethra. We used 2D and 3D U-Net segmentation models. The input images included the bladder and prostate, while the output images focused on the prostatic urethra. The 2D model determined the prostate's position based on results from both coronal and sagittal directions. Evaluation metrics included the average distance between centerlines. The average centerline distances for the 2D and 3D models were 2.07 ± 0.87 mm and 2.05 ± 0.92 mm, respectively. Increasing the number of cases while maintaining equivalent accuracy as we did in this study suggests the potential for high generalization performance and the feasibility of using deep learning technology for estimating the position of the prostatic urethra.

RESUMEN

Aims/Background Sacroiliitis is a challenging condition to diagnose accurately due to the subtle nature of its presentation in imaging studies. This study aims to improve the diagnostic accuracy of sacroiliitis by applying advanced machine learning techniques to computed tomography (CT) images. Methods We employed five convolutional neural network (CNN) models-Visual Geometry Group 16-layer Network (VGG16), ResNet101, DenseNet, Inception-v4, and ResNeXt-50-to analyze a dataset of 830 CT images, including both sacroiliitis and non-sacroiliitis cases. Each model's performance was evaluated using metrics such as accuracy, precision, recall, F1 score, Receiver Operating Characteristic (ROC), and Area Under the Curve (AUC). The interpretability of the models' decisions was enhanced using Gradient-weighted Class Activation Mapping (Grad-CAM) visualization. Results The ResNeXt-50 and Inception-v4 models demonstrated superior performance, achieving the highest accuracy and F1 scores among the tested models. Grad-CAM visualizations offered insights into the decision-making processes, highlighting the models' focus on relevant anatomical features critical for accurate diagnosis. Conclusion The use of CNN models, particularly ResNeXt-50 and Inception-v4, significantly improves the diagnosis of sacroiliitis from CT images. These models not only provide high diagnostic accuracy but also offer transparency in their decision-making processes, aiding clinicians in understanding and trusting Artificial Intelligence (AI)-driven diagnostics.

Asunto(s)

RESUMEN

Elbow computerized tomography (CT) scans have been widely applied for describing elbow morphology. To enhance the objectivity and efficiency of clinical diagnosis, an automatic method to recognize, segment, and reconstruct elbow joint bones is proposed in this study. The method involves three steps: initially, the humerus, ulna, and radius are automatically recognized based on the anatomical features of the elbow joint, and the prompt boxes are generated. Subsequently, elbow MedSAM is obtained through transfer learning, which accurately segments the CT images by integrating the prompt boxes. After that, hole-filling and object reclassification steps are executed to refine the mask. Finally, three-dimensional (3D) reconstruction is conducted seamlessly using the marching cube algorithm. To validate the reliability and accuracy of the method, the images were compared to the masks labeled by senior surgeons. Quantitative evaluation of segmentation results revealed median intersection over union (IoU) values of 0.963, 0.959, and 0.950 for the humerus, ulna, and radius, respectively. Additionally, the reconstructed surface errors were measured at 1.127, 1.523, and 2.062 mm, respectively. Consequently, the automatic elbow reconstruction method demonstrates promising capabilities in clinical diagnosis, preoperative planning, and intraoperative navigation for elbow joint diseases.

Asunto(s)

RESUMEN

Lung cancer, also known as lung carcinoma, has a high death rate, but an early diagnosis can substantially reduce this risk. In the current era, prediction models face challenges such as low accuracy, excessive noise, and low contrast. To resolve these problems, an advanced lung carcinoma prediction and risk screening model using transfer learning is proposed. Our proposed model initially preprocesses lung computed tomography images for noise removal, contrast stretching, convex hull lung region extraction, and edge enhancement. The next phase segments the preprocessed images using the modified Bates distribution coati optimization (B-RGS) algorithm to extract key features. The PResNet classifier then categorizes the cancer as normal or abnormal. For abnormal cases, further risk screening determines whether the risk is low or high. Experimental results depict that our proposed model performs at levels similar to other state-of-the-art models, achieving enhanced accuracy, precision, and recall rates of 98.21%, 98.71%, and 97.46%, respectively. These results validate the efficiency and effectiveness of our suggested methodology in early lung carcinoma prediction and risk assessment.

RESUMEN

INTRODUCTION: The right intersectional plane and the right hepatic hilum were noted too often exhibit anatomical variations, making difficult the laparoscopic right anterior sectionectomy (LRAS). METHODS: We analyzed the anatomical features employing 3D-CT images of 55 patients, and evaluated these features according to the course of ventral branches of segment VI of the portal vein (PV, P6a) relative to the right hepatic vein (RHV). RESULTS: P6a run on the dorsal side of RHV in 32 patients (58%, Dorsal-P6a) and the ventral side of RHV in 23 (42%, Ventral-P6a). Ventral-P6a had more patients with S6 partially drained by middle hepatic vein (MHV, 39% vs. 0%, P < 0001), the narrower angle between the anterior and posterior branches of PV (73.1° vs. 93.8°, P = 0.006), the wider angle between the RHV and inferior vena cava  (54.3° vs. 44.3°, P < 0.001), and more steeply pitched angle between S6 and S7 along the RHV (140.6° vs. 162.0°, P < 0.001) compared to Dorsal-P6a. CONCLUSION: In LRAS for Dorsal-P6a patients, the transection surface was relatively flat. In LRAS for Ventral-P6a patients, the narrow space between anterior and posterior glissons makes difficult the glissonean approach. The transection plane was steeply pitched, and RHV was partially exposed. S6 was often partially drained to MHV in 39% of the Ventral-P6a patients, which triggers congestion during liver transection of a right intersectional plane after first splitting the confluence of this branch.

Asunto(s)

RESUMEN

PURPOSE: The diagnosis of chronic increased intracranial pressure (IIP)is often based on subjective evaluation or clinical metrics with low predictive value. We aimed to quantify cranial bone changes associated with pediatric IIP using CT images and to identify patients at risk. METHODS: We retrospectively quantified local cranial bone thickness and mineral density from the CT images of children with chronic IIP and compared their statistical differences to normative children without IIP adjusting for age, sex and image resolution. Subsequently, we developed a classifier to identify IIP based on these measurements. Finally, we demonstrated our methods to explore signs of IIP in patients with non-syndromic sagittal craniosynostosis (NSSC). RESULTS: We quantified a significant decrease of bone density in 48 patients with IIP compared to 1,018 normative subjects (P < .001), but no differences in bone thickness (P = .56 and P = .89 for age groups 0-2 and 2-10 years, respectively). Our classifier demonstrated 83.33% (95% CI: 69.24%, 92.03%) sensitivity and 87.13% (95% CI: 84.88%, 89.10%) specificity in identifying patients with IIP. Compared to normative subjects, 242 patients with NSSC presented significantly lower cranial bone density (P < .001), but no differences were found compared to patients with IIP (P = .57). Of patients with NSSC, 36.78% (95% CI: 30.76%, 43.22%) presented signs of IIP. CONCLUSION: Cranial bone changes associated with pediatric IIP can be quantified from CT images to support earlier diagnoses of IIP, and to study the presence of IIP secondary to cranial pathology such as non-syndromic sagittal craniosynostosis.

RESUMEN

Background: Diagnosis of mediastinal lesions on computed tomography (CT) images is challenging for radiologists, as numerous conditions can present as mass-like lesions at this site. This study aimed to develop a self-attention network-based algorithm to detect mediastinal lesions on CT images and to evaluate its efficacy in lesion detection. Methods: In this study, two separate large-scale open datasets [National Institutes of Health (NIH) DeepLesion and Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022 Mediastinal Lesion Analysis (MELA) Challenge] were collected to develop a self-attention network-based algorithm for mediastinal lesion detection. We enrolled 921 abnormal CT images from the NIH DeepLesion dataset into the pretraining stage and 880 abnormal CT images from the MELA Challenge dataset into the model training and validation stages in a ratio of 8:2 at the patient level. The average precision (AP) and confidence score on lesion detection were evaluated in the validation set. Sensitivity to lesion detection was compared between the faster region-based convolutional neural network (R-CNN) model and the proposed model. Results: The proposed model achieved an 89.3% AP score in mediastinal lesion detection and could identify comparably large lesions with a high confidence score >0.8. Moreover, the proposed model achieved a performance boost of almost 2% in the competition performance metric (CPM) compared to the faster R-CNN model. In addition, the proposed model can ensure an outstanding sensitivity with a relatively low false-positive rate by setting appropriate threshold values. Conclusions: The proposed model showed excellent performance in detecting mediastinal lesions on CT. Thus, it can drastically reduce radiologists' workload, improve their performance, and speed up the reporting time in everyday clinical practice.

RESUMEN

Background: Intra-abdominal infectious complications (IAICs) following minimally invasive gastrectomy (MIG) for cancer sometimes worsen short- and long-term outcomes. In this study, we focused on the minimum umbilicus-vertebra diameter (MUVD) in preoperative computed tomography (CT) images and robotic surgery to prevent severe IAIC occurrence. Patients and Methods: A total of 400 patients with gastric cancer who underwent 204 laparoscopic gastrectomy (LG) and 196 robotic gastrectomy (RG) procedures were enrolled in this study. We retrospectively investigated the significance of the MUVD and robotic surgery for preventing severe IAICs following MIG using multivariate and propensity score matching analysis. Results: The MUVD cutoff value was 84 mm by receiver operating characteristic (ROC) curve using severe IAICs as the end point. The MUVD and visceral fat area (VFA) had significantly higher area under the curve (AUC) than BMI (MUVD vs. BMI, p = 0.032; VFA vs. BMI, p < 0.01). In the multivariate analysis, high MUVD (HR, 9.46; p = 0.026) and laparoscopic surgery (HR, 3.35; p = 0.042) were independent risk factors for severe IAIC occurrence. In the propensity matching analysis between robotic and laparoscopic surgery in the high MUVD group, the RG group tended to have a lower severe IAIC rate than the LG group (0% vs. 9.8%, p = 0.056). Conclusion: The MUVD was a novel and easy-measuring predictor of severe IAICs following MIG. Robotic surgery should be considered first in patients with gastric cancer having an MUVD value of 84 mm or higher from the perspective of severe IAIC occurrence.

RESUMEN

Objective.Multi-phase computed tomography (CT) has become a leading modality for identifying hepatic tumors. Nevertheless, the presence of misalignment in the images of different phases poses a challenge in accurately identifying and analyzing the patient's anatomy. Conventional registration methods typically concentrate on either intensity-based features or landmark-based features in isolation, so imposing limitations on the accuracy of the registration process.Method.We establish a nonrigid cycle-registration network that leverages semi-supervised learning techniques, wherein a point distance term based on Euclidean distance between registered landmark points is introduced into the loss function. Additionally, a cross-distillation strategy is proposed in network training to further improve registration performance which incorporates response-based knowledge concerning the distances between feature points.Results.We conducted experiments using multi-centered liver CT datasets to evaluate the performance of the proposed method. The results demonstrate that our method outperforms baseline methods in terms of target registration error. Additionally, Dice scores of the warped tumor masks were calculated. Our method consistently achieved the highest scores among all the comparing methods. Specifically, it achieved scores of 82.9% and 82.5% in the hepatocellular carcinoma and the intrahepatic cholangiocarcinoma dataset, respectively.Significance.The superior registration performance indicates its potential to serve as an important tool in hepatic tumor identification and analysis.

Asunto(s)

RESUMEN

We address the problem of lung CT image registration, which underpins various diagnoses and treatments for lung diseases. The main crux of the problem is the large deformation that the lungs undergo during respiration. This physiological process imposes several challenges from a learning point of view. In this paper, we propose a novel training scheme, called stochastic decomposition, which enables deep networks to effectively learn such a difficult deformation field during lung CT image registration. The key idea is to stochastically decompose the deformation field, and supervise the registration by synthetic data that have the corresponding appearance discrepancy. The stochastic decomposition allows for revealing all possible decompositions of the deformation field. At the learning level, these decompositions can be seen as a prior to reduce the ill-posedness of the registration yielding to boost the performance. We demonstrate the effectiveness of our framework on Lung CT data. We show, through extensive numerical and visual results, that our technique outperforms existing methods.

Asunto(s)

RESUMEN

BACKGROUND AND OBJECTIVE: The metabolic syndrome induced by obesity is closely associated with cardiovascular disease, and the prevalence is increasing globally, year by year. Obesity is a risk marker for detecting this disease. However, current research on computer-aided detection of adipose distribution is hampered by the lack of open-source large abdominal adipose datasets. METHODS: In this study, a benchmark Abdominal Adipose Tissue CT Image Dataset (AATCT-IDS) containing 300 subjects is prepared and published. AATCT-IDS publics 13,732 raw CT slices, and the researchers individually annotate the subcutaneous and visceral adipose tissue regions of 3213 of those slices that have the same slice distance to validate denoising methods, train semantic segmentation models, and study radiomics. For different tasks, this paper compares and analyzes the performance of various methods on AATCT-IDS by combining the visualization results and evaluation data. Thus, verify the research potential of this data set in the above three types of tasks. RESULTS: In the comparative study of image denoising, algorithms using a smoothing strategy suppress mixed noise at the expense of image details and obtain better evaluation data. Methods such as BM3D preserve the original image structure better, although the evaluation data are slightly lower. The results show significant differences among them. In the comparative study of semantic segmentation of abdominal adipose tissue, the segmentation results of adipose tissue by each model show different structural characteristics. Among them, BiSeNet obtains segmentation results only slightly inferior to U-Net with the shortest training time and effectively separates small and isolated adipose tissue. In addition, the radiomics study based on AATCT-IDS reveals three adipose distributions in the subject population. CONCLUSION: AATCT-IDS contains the ground truth of adipose tissue regions in abdominal CT slices. This open-source dataset can attract researchers to explore the multi-dimensional characteristics of abdominal adipose tissue and thus help physicians and patients in clinical practice. AATCT-IDS is freely published for non-commercial purpose at: https://figshare.com/articles/dataset/AATTCT-IDS/23807256.

Asunto(s)

RESUMEN

Intraoperative navigation is critical during spine surgery to ensure accurate instrumentation placement. From the early era of fluoroscopy to the current advancement in robotics, spinal navigation has continued to evolve. By understanding the variations in system protocols and their respective usage in the operating room, the surgeon can use and maximize the potential of various image guidance options more effectively. At the same time, maintaining navigation accuracy throughout the procedure is of the utmost importance, which can be confirmed intraoperatively by using an internal fiducial marker, as demonstrated herein. This technology can reduce the need for revision surgeries, minimize postoperative complications, and enhance the overall efficiency of operating rooms.

RESUMEN

Currently, slow-release gel therapy is considered to be an effective treatment for fundus macular disease, but the lack of effective evaluation methods limits its clinical application. Therefore, the purpose of this study was to investigate the application and clinical effect of slow-release gel based on CT image examination in the treatment of diabetic fundus macular disease. CT images of fundus macular lesions were collected in a group of diabetic patients. Then the professional image processing software is used to process and analyze the image and extract the key parameters. A slow-release gel was designed and prepared, and applied to the treatment of diabetic fundus macular disease. CT images before and after treatment were compared and analyzed, and the effect of slow-release gel was evaluated. In a certain period of time after treatment, the lesion size and lesion degree of diabetic fundus macular disease were significantly improved by using slow-release gel therapy with CT image examination. No significant adverse reactions or complications were observed during the treatment. This indicates that the slow-release gel based on CT image examination is a safe, effective and feasible treatment method for diabetic fundus macular disease. This method can help improve the vision and quality of life of patients, and provide a new idea and plan for clinical treatment.

Asunto(s)