Image Slices Research Articles

Artificial Intelligence to Determine Fetal Sex.

This proof-of-concept study assessed how confidently an artificial intelligence (AI) model can determine the sex of a fetus from an ultrasound image. Analysis was performed using 19,212 ultrasound image slices from a high-volume fetal sex determination practice. This dataset was split into a training set (11,769) and test set (7,443). A computer vision model was trained using a transfer learning approach with EfficientNetB4 architecture as base. The performance of the computer vision model was evaluated on the hold out test set. Accuracy, Cohen's Kappa and Multiclass Receiver Operating Characteristic area under the curve (AUC) were used to evaluate the performance of the model. The AI model achieved an Accuracy of 88.27% on the holdout test set and a Cohen's Kappa score 0.843. The ROC AUC score for Male was calculated to be 0.896, for Female a score of 0.897, for Unable to Assess a score of 0.916, and for Text Added a score of 0.981 was achieved. This novel AI model proved to have a high rate of fetal sex capture that could be of significant use in areas where ultrasound expertise is not readily available. · This is the first proof-of-concept AI model to determine fetal sex.. · This study adds to the growing research in ultrasound AI.. · Our findings demonstrate AI integration into obstetric care..

A Dual-Branch Framework With Prior Knowledge for Precise Segmentation of Lung Nodules in Challenging CT Scans.

Lung cancer is one of the deadliest cancers globally, and early diagnosis is crucial for patient survival. Pulmonary nodules are the main manifestation of early lung cancer, usually assessed using CT scans. Nowadays, computer-aided diagnostic systems are widely used to assist physicians in disease diagnosis. The accurate segmentation of pulmonary nodules is affected by internal heterogeneity and external data factors. In order to overcome the segmentation challenges of subtle, mixed, adhesion-type, benign, and uncertain categories of nodules, a new mixed manual feature network that enhances sensitivity and accuracy is proposed. This method integrates feature information through a dual-branch network framework and multi-dimensional fusion module. By training and validating with multiple data sources and different data qualities, our method demonstrates leading performance on the LUNA16, Multi-thickness Slice Image dataset, LIDC, and UniToChest, with Dice similarity coefficients reaching 86.89%, 75.72%, 84.12%, and 80.74% respectively, surpassing most current methods for pulmonary nodule segmentation. Our method further improved the accuracy, reliability, and stability of lung nodule segmentation tasks even on challenging CT scans.

Synthetic Brain Images: Bridging the Gap in Brain Mapping With Generative Adversarial Model

Magnetic Resonance Imaging (MRI) is a vital modality for gaining precise anatomical information, and it plays a significant role in medical imaging for diagnosis and therapy planning. Image synthesis problems have seen a revolution in recent years due to the introduction of deep learning techniques, specifically Generative Adversarial Networks (GANs). This work investigates the use of Deep Convolutional Generative Adversarial Networks (DCGAN) for producing high-fidelity and realistic MRI image slices. The suggested approach uses a dataset with a variety of brain MRI scans to train a DCGAN architecture. While the discriminator network discerns between created and real slices, the generator network learns to synthesise realistic MRI image slices. The generator refines its capacity to generate slices that closely mimic real MRI data through an adversarial training approach. The outcomes demonstrate that the DCGAN promise for a range of uses in medical imaging research, since they show that it can effectively produce MRI image slices if we train them for a consequent number of epochs. This work adds to the expanding corpus of research on the application of deep learning techniques for medical image synthesis. The slices that are could be produced possess the capability to enhance datasets, provide data augmentation in the training of deep learning models, as well as a number of functions are made available to make MRI data cleaning easier, and a three ready to use and clean dataset on the major anatomical plans.

Relationships between Size-specific Dose Estimate and Signal to Noise Ratio under Chest CT Examinations with Tube Current Modulation.

Exploring the relationship between the signal-to-noise ratio (SNR) of organs and size-specific dose estimate (SSDE) in tube current modulation (TCM) chest CT examination. Forty patients who received TCM chest CT scanning were retrospectively collected and divided into four groups according to the tube voltage and sexes. We chose to set up the region of interest (ROI) at the tracheal bifurcation and its upper and lower parts in slice images of the heart, aorta, lungs, paracranial muscles, and female breast, and the SNR of each organ was calculated. We also calculated the corresponding axial volume CT dose index (CTDIvolz) and axial size-specific dose estimate (SSDEz). The correlation analysis showed that the correlation between the SNR of the slice images of most organs and SSDEz was more significant than 0.8, and that between the SNR and CTDIvol was more significant than 0.7. The simple linear regression analysis results showed that when the sex is the same, the SNR of the same organ at 100kVp was higher than 120kVp, except for the lung. In multiple regression analysis, the result indicated that the determination coefficients of the SNR and SSDEz of the four groups were 0.934, 0.971, 0.905, and 0.709, respectively. In chest CT examinations with TCM, the correlation between the SNR of each organ in slice images and SSDEz was better than that of CTDIvolz. And when the SSDEz was the same, the SNR at 100 kVp was better than that at 120 kVp.

Manufacturing errors detection and visualization system for parts with complex internal cavity structural through industrial CT image

To address the challenge of detection and visualization manufacturing errors in part with complex internal cavity structures, this paper present a method based on industrial computed tomography (CT) image. First, a two-dimensional (2D) CT sequence slice images are obtained using fan-beam high energy industrial CT scanning, which is then reconstructed into a three-dimensional (3D) measurement model. The measurement 3D model is registered with the original design model, and the surface information of the part is obtained by segmenting the surfaces. The error between reference surface of original design model and test surface of measurement model is calculated. By comparing the error value with the tolerance, the detection result is obtained and analyzed. The developed software system enables areas that do not meet the tolerance requirements to be visualized. This method can detect manufacturing errors for part with complex internal cavity structures, such as planeness error, cylindricity error, verticality error, and parallelism error. This method provides a basis for assessing the manufacturing and processing quality of part with complex internal cavity structures.

Vagotomy blunts cardiorespiratory responses to vagal afferent stimulation via pre- and postsynaptic effects in the nucleus tractus solitarii.

Viscerosensory information travels to the brain via vagal afferents, where it is first integrated within the brainstem nucleus tractus solitarii (nTS), a critical contributor to cardiorespiratory function and site of neuroplasticity. We have shown that decreasing input to the nTS via unilateral vagus nerve transection (vagotomy) induces morphological changes in nTS glia and reduces sighs during hypoxia. The mechanisms behind post-vagotomy changes are not well understood. We hypothesized that chronic vagotomy alters cardiorespiratory responses to vagal afferent stimulation via blunted nTS neuronal activity. Male Sprague-Dawley rats (6weeks old) underwent right cervical vagotomy caudal to the nodose ganglion, or sham surgery. After 1week, rats were anaesthetized, ventilated and instrumented to measure mean arterial pressure (MAP), heart rate (HR), and splanchnic sympathetic and phrenic nerve activity (SSNA and PhrNA, respectively). Vagal afferent stimulation (2-50Hz) decreased cardiorespiratory parameters and increased neuronal Ca2+ measured by in vivo photometry and in vitro slice imaging of nTS GCaMP8m. Vagotomy attenuated both these reflex and neuronal Ca2+ responses compared to shams. Vagotomy also reduced presynaptic Ca2+ responses to stimulation (Cal-520 imaging) in the nTS slice. The decrease in HR, SSNA and PhrNA due to nTS nanoinjection of exogenous glutamate also was tempered following vagotomy. This effect was not restored by blocking excitatory amino acid transporters. However, the blunted responses were mimicked by NMDA, not AMPA, nanoinjection and were associated with reduced NR1 subunits in the nTS. Altogether, these results demonstrate that vagotomy induces multiple changes within the nTS tripartite synapse that influence cardiorespiratory reflex responses to afferent stimulation. KEY POINTS: Multiple mechanisms within the nucleus tractus solitarii (nTS) contribute to functional changes following vagal nerve transection. Vagotomy results in reduced cardiorespiratory reflex responses to vagal afferent stimulation and nTS glutamate nanoinjection. Blunted responses occur via reduced presynaptic Ca2+ activation and attenuated NMDA receptor expression and function, leading to a reduction in nTS neuronal activation. These results provide insight into the control of autonomic and respiratory function, as well as the plasticity that can occur in response to nerve damage and cardiorespiratory disease.

Deep learning for head and neck semi-supervised semantic segmentation

Objective. Radiation therapy (RT) represents a prevalent therapeutic modality for head and neck (H&N) cancer. A crucial phase in RT planning involves the precise delineation of organs-at-risks (OARs), employing computed tomography (CT) scans. Nevertheless, the manual delineation of OARs is a labor-intensive process, necessitating individual scrutiny of each CT image slice, not to mention that a standard CT scan comprises hundreds of such slices. Furthermore, there is a significant domain shift between different institutions’ H&N data, which makes traditional semi-supervised learning strategies susceptible to confirmation bias. Therefore, effectively using unlabeled datasets to support annotated datasets for model training has become a critical issue for preventing domain shift and confirmation bias. Approach. In this work, we proposed an innovative cross-domain orthogon-based-perspective consistency (CD-OPC) strategy within a two-branch collaborative training framework, which compels the two sub-networks to acquire valuable features from unrelated perspectives. More specifically, a novel generative pretext task cross-domain prediction (CDP) was designed for learning inherent properties of CT images. Then this prior knowledge was utilized to promote the independent learning of distinct features by the two sub-networks from identical inputs, thereby enhancing the perceptual capabilities of the sub-networks through orthogon-based pseudo-labeling knowledge transfer. Main results. Our CD-OPC model was trained on H&N datasets from nine different institutions, and validated on the four local intuitions’ H&N datasets. Among all datasets CD-OPC achieved more advanced performance than other semi-supervised semantic segmentation algorithms. Significance. The CD-OPC method successfully mitigates domain shift and prevents network collapse. In addition, it enhances the network’s perceptual abilities, and generates more reliable predictions, thereby further addressing the confirmation bias issue.

SSA‐UNet: Whole brain segmentation by U‐Net with squeeze‐and‐excitation block and self‐attention block from the 2.5D slice image

AbstractWhole brain segmentation from magnetic resonance images (MRI) is crucial in diagnosing brain diseases and analyzing neuroimaging data. Despite advances through deep learning, challenges such as uneven gray distribution and the presence of artifacts still present hurdles in medical image processing. These limitations are often a result of insufficient spatial contextual information and lack of attention to important regions within existing models. To address these issues, this paper presents SSA‐UNet (Squeeze‐and‐Excitation and Self‐Attention UNet), a uniquely designed deep convolutional neural network that integrates spatial constraints by converting three consecutive 2D MRI slices into a single 2.5D image. This facilitates capturing inter‐slice dependencies effectively. Additionally, the newly formulated SSA block, which sequentially incorporates channel attention and Self‐Attention mechanisms, is placed before the decoders in the conventional U‐Net architecture. This enables the network to automatically weight different feature maps and focus more effectively on regions requiring precise segmentation. Rigorous evaluations on LPBA40 and IBSR18 datasets substantiate the remarkable improvements in accuracy and stability achieved by SSA‐UNet. Results indicate Dice coefficients of 98.38% and 97.47%, specificity of 99.69% and 99.57%, and sensitivity of 98.5% and 97.98% for the respective datasets. Compared to other existing models, SSA‐UNet shows significant improvements on both the LPBA40 and IBSR18 datasets. On the LPBA40 dataset, SSA‐UNet's Dice coefficient improved by 0.33% compared to the sub‐optimal model, while on the IBSR18 dataset, the improvement reached 1.78%. These empirical findings demonstrate SSA‐UNet's heightened capability in addressing the long‐standing challenges in MRI‐based whole‐brain segmentation.

A CNN-based denoising method trained with images acquired with electron density phantoms for thin-sliced coronary artery calcium scans.

This work proposed a convolutional neural network (CNN)-based method trained with images acquired with electron density phantoms to reduce quantum noise for coronary artery calcium (CAC) scans reconstructed with slice thickness less than 3mm. A DenseNet model was used to estimate quantum noise for CAC scans reconstructed with slice thickness of 0.5, 1.0 and 1.5mm. Training data was acquired using electron density phantoms in three different sizes. The label images of the CNN model were real noise maps, while the input images of the CNN model were pseudo noise maps. Image denoising was conducted by subtracting the CNN output images from thin-sliced CAC scans. The efficacy of the proposed method was verified through both phantom study and patient study. By means of phantom study, the proposed method was proven effective in reducing quantum noise in CAC scans reconstructed with 1.5-mm slice thickness without causing significant texture change or variation in HU values. With regard to patient study, calcifications were more clear on the denoised CAC scans reconstructed with slice thickness of 0.5, 1.0 and 1.5mm than on 3-mm slice images, while over-smooth changes were not observed in the denoised CAC scans reconstructed with 1.5-mm slice thickness. Our results demonstrated that the electron density phantoms can be used to generate training data for the proposed CNN-based denoising method to reduce quantum noise for CAC scans reconstructed with 1.5-mm slice thickness. Because anthropomorphic phantom is not a necessity, our method could make image denoising more practical in routine clinical practice.

Detection support of lesions in patients with prostate cancer using -PSMA 1007 PET/CT.

This study proposes a detection support system for primary and metastatic lesions of prostate cancer using -PSMA 1007 positron emission tomography/computed tomography (PET/CT) images with non-image information, including patient metadata and location information of an input slice image. A convolutional neural network with condition generators and feature-wise linear modulation (FiLM) layers was employed to allow input of not only PET/CT images but also non-image information, namely, Gleason score, flag of pre- or post-prostatectomy, and normalized z-coordinate of an input slice. We explored the insertion position of the FiLM layers to optimize the conditioning of the network using non-image information. -PSMA 1007 PET/CT images were collected from 163 patients with prostate cancer and applied to the proposed system in a threefold cross-validation manner to evaluate the performance. The proposed system achieved a Dice score of 0.5732 (per case) and sensitivity of 0.8200 (per lesion), which are 3.87 and 4.16 points higher than the network without non-image information. This study demonstrated the effectiveness of the use of non-image information, including metadata of the patient and location information of the input slice image, in the detection of prostate cancer from -PSMA 1007 PET/CT images. Improvement in the sensitivity of inactive and small lesions remains a future challenge.

Diagnosis of pulmonary tuberculosis with 3D neural network based on multi-scale attention mechanism.

This paper presents a novel multi-scale attention residual network (MAResNet) for diagnosing patients with pulmonary tuberculosis (PTB) by computed tomography (CT) images. First, a three-dimensional (3D) network structure is applied in MAResNet based on the continuity and correlation of nodal features on different slices of CT images. Secondly, MAResNet incorporates the residual module and Convolutional Block Attention Module (CBAM) to reuse the shallow features of CT images and focus on key features to enhance the feature distinguishability of images. In addition, multi-scale inputs can increase the global receptive field of the network, extract the location information of PTB, and capture the local details of nodules. The expression ability of both high-level and low-level semantic information in the network can also be enhanced. The proposed MAResNet shows excellent results, with overall 94% accuracy in PTB classification. MAResNet based on 3D CT images can assist doctors make more accurate diagnosis of PTB and alleviate the burden of manual screening. In the experiment, a called Grad-CAM was employed to enhance the class activation mapping (CAM) technique for analyzing the model's output, which can identify lesions in important parts of the lungs and make transparent decisions.

Detection of normal and abnormal brain tumor MRI images using machine learning approaches

<p>Without image processing, the modern technological world we live in today would be completely different. Different applications can be found in many different areas, including as medicine, remote sensing, computer vision, and more. Brain tumours occur because of the abnormal growth of brain tissue. Therefore, it is crucial to surgically remove the tumor’s component that has spread throughout the brain. The creation of images of the human body’s internal organs and structures depends on numerous factors, one of which is the use of Magnetic Resonance Imaging (MRI), one of the many medical imaging techniques. Segmenting images is a challenging task in the modern medical field. The tumor’s location and sizes can be detected with more accuracy with the help of a segmentation performed on MRI slices. In this paper, we propose the Adaptive Convex Region Contour (ACRC) algorithm, a novel method for achieving such requirements. Here, we use the Support Vector Machine, often known as SVM, to identify slices and determine whether they are normal or abnormal. Once the SVM results are in, the off-kilter slices will be included into the analysis. Since the human body already has a complex anatomy in 3D. It’s disappointing because MRI slices can only provide images in a plain, 2D plane. Because of its 3D structure, the tumor’s actual shape cannot be detected from a surface image. This means that the transformation from 2D to 3D is important, as it provides necessary data to the surgeons doing the operation. The Rapid Mode Image Matching (RMIM) algorithm should be used when constructing a 3D reconstruction model. Immediately after the 3D model is completed being created, a rough estimate of the tumor’s original volume is generated. The methodological consideration was conducted in the simulated MATLAB software. It was determined from the data that the proposed method is better to the current establishment methodologies in terms of accuracy. <strong>Objective:</strong> This paper aims to address the need for accurate segmentation of brain tumors in Magnetic Resonance Imaging (MRI) slices. The primary research question revolves around developing a novel segmentation method, the Adaptive Convex Region Contour (ACRC) algorithm, to improve the precision of tumor detection by incorporating a Support Vector Machine (SVM) for identifying abnormal slices.<strong> </strong><strong>Methods: </strong>The proposed approach involves utilizing the SVM to classify MRI slices as either normal or abnormal. The abnormal slices, detected by SVM, are subjected to further analysis. To overcome the limitation of MRI producing 2D images for the inherently 3D human anatomy, a transformation from 2D to 3D is crucial. The Rapid Mode Image Matching (RMIM) algorithm is adopted to construct a 3D reconstruction model of the brain and its tumor. This model allows surgeons to obtain a more accurate understanding of the tumor’s actual 3D shape. <strong>Results: </strong>The methodology was implemented and evaluated using simulated MATLAB software. The results demonstrate that the proposed ACRC algorithm, combined with SVM-based abnormal slice identification and subsequent 3D reconstruction using RMIM, outperforms existing methodologies in terms of accuracy. The 3D reconstruction provides valuable insights into the tumor’s shape, aiding surgical planning. <strong>Conclusions: </strong>In conclusion, the research contributes to the field of medical image processing by presenting a comprehensive approach for brain tumor segmentation and 3D reconstruction. The ACRC algorithm, in conjunction with SVM and RMIM, enhances the accuracy of tumor detection and provides critical 3D information for surgical procedures. This advancement holds the potential to improve the efficacy of brain tumor surgeries, underscoring the significance of innovative image processing techniques in modern medical applications.</p>

Interactive content-based image retrieval with deep learning for CT abdominal organ recognition

Objective. Recognizing the most relevant seven organs in an abdominal computed tomography (CT) slice requires sophisticated knowledge. This study proposed automatically extracting relevant features and applying them in a content-based image retrieval (CBIR) system to provide similar evidence for clinical use. Approach. A total of 2827 abdominal CT slices, including 638 liver, 450 stomach, 229 pancreas, 442 spleen, 362 right kidney, 424 left kidney and 282 gallbladder tissues, were collected to evaluate the proposed CBIR in the present study. Upon fine-tuning, high-level features used to automatically interpret the differences among the seven organs were extracted via deep learning architectures, including DenseNet, Vision Transformer (ViT), and Swin Transformer v2 (SwinViT). Three images with different annotations were employed in the classification and query. Main results. The resulting performances included the classification accuracy (94%–99%) and retrieval result (0.98–0.99). Considering global features and multiple resolutions, SwinViT performed better than ViT. ViT also benefited from a better receptive field to outperform DenseNet. Additionally, the use of hole images can obtain almost perfect results regardless of which deep learning architectures are used. Significance. The experiment showed that using pretrained deep learning architectures and fine-tuning with enough data can achieve successful recognition of seven abdominal organs. The CBIR system can provide more convincing evidence for recognizing abdominal organs via similarity measurements, which could lead to additional possibilities in clinical practice.

Distinguishing true from pseudo hematoma in the cervical spinal canal using post-mortem computed tomography

Spinal cord injury is difficult to detect directly on postmortem computed tomography (PMCT) and it is usually diagnosed by indirect findings such as a hematoma in the spinal canal. However, we have encountered cases where the hematoma-like high-attenuation area in the cervical spinal canal was visible on PMCT, while no hematoma was observed at autopsy; we called it a “pseudo hematoma in the cervical spinal canal (pseudo-HCSC).” In this retrospective study, we performed statistical analysis to distinguish true from pseudo-HCSC. The cervical spinal canal was dissected in 35 autopsy cases with a hematoma-like high-attenuation area (CT values 60-100 Hounsfield Unit (HU)) in the spinal canal from the first to the fourth cervical vertebrae in axial slices of PMCT images. Of these 22 had a hematoma and 13 did not (pseudo-HCSC). The location and length of the hematoma-like high-attenuation and spinal cord areas were assessed on reconstructed PMCT images, true HCSC cases had longer the posterior hematoma-like area and shorter the spinal cord area in the midline of the spinal canal (P < 0.05). Furthermore, we found that true HCSC cases were more likely to have fractures and gases on PMCT while pseudo-HCSC cases were more likely to have significant facial congestion (P < 0.05). We suggest that pseudo-HCSC on PMCT is related to congestion of the internal vertebral venous plexus. This study raises awareness about the importance of distinguishing true HCSC from pseudo-HCSC in PMCT diagnosis, and it also presents methods for differentiation between these two groups.

Automatic analysis of dendritic spines incorporating machine learning on widefield fluorescent images of thick brain slices

Automatic analysis of dendritic spines incorporating machine learning on widefield fluorescent images of thick brain slices

Abstract TP234: A Predictive Model for Early Neurologic Deterioration in Small Subcortical Infarcts

Introduction: Roughly a quarter of patients with an acute small subcortical infarct may develop early neurologic deterioration (END) hallmarked by progressive or stuttering neurologic deficits. Importantly, END results in worse short- and long-term functional outcomes. However, despite its importance, there is an inability to identify those at risk for END, thereby creating a barrier to designing future treatment and preventative trials. We aimed to generate a simple predictive scoring model for END in patients with an acute SSI. Methods: We performed a retrospective chart review of all adult patients admitted to Rhode Island Hospital with an acute SSI between 2015 and 2022. Clinical and demographic data were extracted. Radiographic variables included: 1)infarct signal intensity ratio ≤ 1.15 (analogous to the MR-WITNESS trial), 2) infarct morphology, and 3) infarct size. The primary outcome was END, defined as a ≥ 2-point increase in NIHSS or progressive disabling stroke-related deficits while hospitalized attributed to the index acute SSI. Standard descriptive statistical techniques were used. We then employed a backward stepwise logistic regression with a 0.05 threshold to discern factors associated with END, with each factor contributing one point to the model. An external validation cohort was ascertained from Columbia University. The area under the curve (AUC) was used to assess the utility of the predictive model. Results: A total of 252 patients met inclusion criteria. Demographic and clinical histories did not differ amongst the primary or validation cohorts. A signal intensity ratio ≤ 1.15, “finger-like” SSI morphology, and the presence of SSI on &gt; 3 axial diffusion weighted imaging slices were predictive of END. Allocating one point for each domain (minimum 0, maximum 3) yielded an AUC of 0.88. A score of 2 points captured 91.6% of all END cases. Applying this model to the external validation cohort (n=43) yielded an AUC of 0.68. Conclusion: In sum, we found that a radiographic ordinal model including signal intensity ratio ≤ 1.15, “finger-like” SSI morphology, and infarct presence on &gt; 3 axial diffusion weight imaging slices was helpful in predicting END. Future studies are needed to expand the external cohort size to test this model’s validity.

Abstract TP143: Longitudinal Evaluation of Vessel Wall MRI Demonstrates Bilaterally Symmetric Evolution of Carotid Atherosclerosis

Introduction: Carotid atherosclerosis is a leading cause of stroke worldwide. Previous studies demonstrated bilateral symmetry in atherosclerotic plaque burden and calcification scores. By leveraging longitudinal 3D vessel wall MRI, this study aims to investigate if there is a symmetrical trend of bilateral plaque progression. Methods: Asymptomatic patients with confirmed carotid stenosis were prospectively recruited in the study. Subjects underwent baseline and follow-up 3D vessel wall imaging for a time span up to 8 years. A deep learning-based pipeline was used to bilaterally segment the carotid lumen and vessel wall on axial image slices. Within each artery, the range of slices with wall thickness &gt; 2mm on any scan was included as potential atherosclerotic plaque. Plaque burden, positively associated with stroke risk, was defined as the percent wall volume (wall volume / total vessel volume х 100%). For each plaque, a linear model was fitted across all available time points to determine the growth rate of plaque burden. We used generalized estimating equation-based linear regression models to evaluate associations of the growth rate of each side with the contralateral side, adjusted for clinical risk factors and medications. Effect sizes were summarized using the partial correlation coefficient. Results: 20 subjects (mean age 73.5 years, 3 females, 14 on statins) who underwent at least three scans with bilateral carotid plaque were included in the analysis. Each subject underwent an average of 4.8 scans over 5.2 years. Overall, the plaque growth rate of one side was moderately correlated with the contralateral side (r=0.50, p=0.004), even after adjusting for age, sex and statins use. Then, for each subject, the larger of the two plaques at baseline (left or right side) was identified. The growth rate of the larger plaque was also predictive of the growth rate of the smaller plaque over the same time period (r=0.66, p=0.004). Conclusion: The systemic nature of atherosclerotic disease entails an association in symmetrical disease evolution for left and right carotid arteries. In bilaterally diseased patients, the longitudinal monitoring of the more advanced carotid lesion may provide prognostic information on the less advanced contralateral plaque.

Segmentation and classification of lungs CT-scan for detecting COVID-19 abnormalities by deep learning technique: U-Net model.

Artificial intelligence (AI) techniques have been ascertained useful in the analysis and description of infectious areas in radiological images promptly. Our aim in this study was to design a web-based application for detecting and labeling infected tissues on CT (computed tomography) lung images of patients based on the deep learning (DL) method as a type of AI. The U-Net architecture, one of the DL networks, is used as a hybrid model with pre-trained densely connected convolutional network 121 (DenseNet121) architecture for the segmentation process. The proposed model was constructed on 1031 persons' CT-scan images from Ibn Sina Hospital of Iran in 2021 and some publicly available datasets. The network was trained using 6000 slices, validated on 1000 slices images, and tested against the 150 slices. Accuracy, sensitivity, specificity, and area under the receiver operating characteristics (ROC) curve (AUC) were calculated to evaluate model performance. The results indicate the acceptable ability of the U-Net-DenseNet121 model in detecting COVID-19 abnormality (accuracy = 0.88 and AUC = 0.96 for thresholds of 0.13 and accuracy = 0.88 and AUC = 0.90 for thresholds of 0.2). Based on this model, we developed the "Imaging-Tech" web-based application for use at hospitals and clinics to make our project's output more practical and attractive in the market. We designed a DL-based model for the segmentation of COVID-19 CT scan images and, based on this model, constructed a web-based application that, according to the results, is a reliable detector for infected tissue in lung CT-scans. The availability of such tools would aid in automating, prioritizing, fastening, and broadening the treatment of COVID-19 patients globally.

Diagnostics and treatment of insufficiency fractures of the pelvis

Insufficiency fractures of the pelvis have increased in recent years, primarily due to the demographic change and the incidence will continue to rise. In addition to conventional X‑rays, the diagnostics always require slice imaging. Unlike high-energy trauma magnetic resonance imaging (MRI) plays an important role in insufficiency fractures. Once the fracture has been diagnosed, in addition to the extent of instability in the anterior and posterior pelvic rings, the pain symptoms are crucial for the decision on surgical treatment. The basic principle is to stabilize as little as possible but as much as necessary. There are currently avariety of procedures that can be applied as a minimally invasive procedure, especially for the often slightly or displaced insufficiency fractures. The decisive factor for treatment is that it enables early mobilization of the patients. All of these measures must be accompanied by thorough diagnostics of osteoporosis and the appropriate treatment.

Deep learning-based predictive model for pathological complete response to neoadjuvant chemotherapy in breast cancer from biopsy pathological images: a multicenter study.

Introduction: Early predictive pathological complete response (pCR) is beneficial for optimizing neoadjuvant chemotherapy (NAC) strategies for breast cancer. The hematoxylin and eosin (HE)-stained slices of biopsy tissues contain a large amount of information on tumor epithelial cells and stromal. The fusion of pathological image features and clinicopathological features is expected to build a model to predict pCR of NAC in breast cancer. Methods: We retrospectively collected a total of 440 breast cancer patients from three hospitals who underwent NAC. HE-stained slices of biopsy tissues were scanned to form whole-slide images (WSIs), and pathological images of representative regions of interest (ROI) of each WSI were selected at different magnifications. Based on several different deep learning models, we propose a novel feature extraction method on pathological images with different magnifications. Further, fused with clinicopathological features, a multimodal breast cancer NAC pCR prediction model based on a support vector machine (SVM) classifier was developed and validated with two additional validation cohorts (VCs). Results: Through experimental validation of several different deep learning models, we found that the breast cancer pCR prediction model based on the SVM classifier, which uses the VGG16 model for feature extraction of pathological images at ×20 magnification, has the best prediction efficacy. The area under the curve (AUC) of deep learning pathological model (DPM) were 0.79, 0.73, and 0.71 for TC, VC1, and VC2, respectively, all of which exceeded 0.70. The AUCs of clinical model (CM), a clinical prediction model established by using clinicopathological features, were 0.79 for TC, 0.73 for VC1, and 0.71 for VC2, respectively. The multimodal deep learning clinicopathological model (DPCM) established by fusing pathological images and clinicopathological features improved the AUC of TC from 0.79 to 0.84. The AUC of VC2 improved from 0.71 to 0.78. Conclusion: Our study reveals that pathological images of HE-stained slices of pre-NAC biopsy tissues can be used to build a pCR prediction model. Combining pathological images and clinicopathological features can further enhance the predictive efficacy of the model.