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VVC as the latest generation of video coding standards, further improves video compression quality by introducing a variety of efficient coding tools. However, the VVC standard introduces the QTMT division structure and expands the intra prediction modes from 35 to 67, resulting in a sharp increase in coding complexity. Firstly, a fast algorithm for intra-frame coding unit (CU) division based on graph neural network was proposed, in order to reduce the complexity of intra-frame coding of VVC. An efficient graph neural network model was used to directly predict the optimal partition mode of CU, thus skipping redundant CU partition traversal. Secondly, a fast algorithm for intraframe mode selection based on spatial correlation and texture features was proposed. The average direction variance and Sobel gradient operator were used to determine the texture direction, some angle prediction modes were skipped, and the correlation between prediction modes to streamline the rate-distortion mode list were combined. Experimental results show that this algorithm can save 64.04% of encoding time at the cost of increasing BDBR by 2.29%. [ABSTRACT FROM AUTHOR]

Due to the small difference between the focus area of pulmonary tuberculosis and the normal lung area, it is difficult to accurately detect the pulmonary tuberculosis disease. To solve this problems, a tuberculosis disease detection algorithm based on the combination of deep separable convolution and graph convolution is proposed. Firstly, the depth separable convolution module is used to extract the local features of the image. Secondly, the graph convolution module is used to obtain the global features of the image. Then, using a shortcut branching operation, the extracted local and global features are fused. Finally, the fused features are output through the linear layer to output the detection results. The algorithm model has been fully experimented and verified on the publicly available positive chest X-Ray dataset collected by the Third Hospital of Shenzhen City, Guangdong Province, China. Experimental results show that compared with the benchmark model, the proposed algorithm model improves by 2.98, 3.23, 2.94 and 3.08 percentage points on the four evaluation indicators of Accuracy, Precision, Recall and F1-Score, respectively, which proves the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In computer vision, automatically identifying and locating text in images or videos is an important task. Traditional text detection methods are not effective in detecting complex scenes in irregular rectangular areas such as text bending, spacing, and special shapes, which are mainly reflected in the fragmentation of text detection areas. In this paper, a text detection method based on edge attention mechanism is proposed to better adapt to complex scenes. The proposed method takes Encoder–Decoder as the core idea. First of all, an edge attention module is designed, including global attention and local attention. The global attention module is used to perceive the features of text regions and nontext regions, while the local attention module is used to learn the information of text boundaries. Then a multi-scale feature fusion process is designed, which can strengthen the edge information and key information of text regions. Finally, the model outputs probability maps and threshold maps, and generates high-precision binary maps of text regions. After experimental verification, the proposed method on the public data set significantly reduces the fragmentation of the detection area, improves the detection accuracy of the text area, and has better robustness for text detection scenes with unconventional rectangular areas. [ABSTRACT FROM AUTHOR]

This study aims to use a deep learning method to develop a signature extract from preoperative magnetic resonance imaging (MRI) and to evaluate its ability as a non-invasive recurrence risk prognostic marker in patients with advanced high-grade serous ovarian cancer (HGSOC). Our study comprises a total of 185 patients with pathologically confirmed HGSOC. A total of 185 patients were randomly assigned in a 5:3:2 ratio to a training cohort (n = 92), validation cohort 1 (n = 56), and validation cohort 2 (n = 37). We built a new deep learning network from 3839 preoperative MRI images (T2-weighted images and diffusion-weighted images) to extract HGSOC prognostic indicators. Following that, a fusion model including clinical and deep learning features is developed to predict patients' individual recurrence risk and 3-year recurrence likelihood. In the two validation cohorts, the consistency index of the fusion model was higher than both the deep learning model and the clinical feature model (0.752, 0.813 vs. 0.625, 0.600 vs. 0.505, 0.501). Among the three models, the fusion model had a higher AUC than either the deep learning model or the clinical model in validation cohorts 1 or 2 (AUC = was 0.986, 0.961 vs. 0.706, 0.676/0.506, 0.506). Using the DeLong method, the difference between them was statistically significant (p < 0.05). The Kaplan–Meier analysis distinguished two patient groups with high and low recurrence risk (p = 0.0008 and 0.0035, respectively). Deep learning may be a low-cost, non-invasive method for predicting risk for advanced HGSOC recurrence. Deep learning based on multi-sequence MRI serves as a prognostic biomarker for advanced HGSOC, which provides a preoperative model for predicting recurrence in HGSOC. Additionally, using the fusion model as a new prognostic analysis means that can use MRI data can be used without the need to follow-up the prognostic biomarker. [ABSTRACT FROM AUTHOR]

In this study, we present a novel automatic segmentation method using a neural network model named GAN‐segNet, which can not only identify brain tumors from MRI images but also accurately delineate intratumor regions. Since brain tumors with varying shapes and sizes can appear anywhere in the brain and image quality and contrast of MRI could be inadequate, automatic segmentation remains challenging despite its importance in the clinical workflow. The proposed GAN‐segNet is an innovative modification of the Generative Adversarial Network (GAN) and can efficiently and accurately segment brain tumors. One key innovation of our GAN model is an autoencoder learning representation of input data that were added to the generative network of the above‐mentioned GAN. By doing so, information extracted through convolution operations can be meaningfully regularized. As a result, the scales of extracted features can be controlled by the added autoencoder to preserve detail. Additionally, we propose an innovative loss function based on the concept of focal loss to effectively mitigate the impact of label imbalance. The above‐mentioned combination enables the proposed GAN‐segNet model to improve the segmentation of small intratumor region(s). We demonstrate the proposed method using MRI data available from a public database, that is, Brain Tumor Segmentation Challenge 2018 database (BRATS 2018). Using the proposed GAN‐segNet model, the average Dice scores were 0.8280, 0.9022, and 0.814 for segmenting enhancing tumor core, whole tumor, and tumor core, respectively. Furthermore, positive predictive values for segmenting enhanced tumor core, whole tumor, and tumor core were 0.8496, 0.9270, and 0.8610, respectively. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Hemorrhagic transformation (HT) is one of the most serious complications in patients with acute ischemic stroke (AIS) following reperfusion therapy. The purpose of this study is to develop and validate deep learning (DL) models utilizing multiphase computed tomography angiography (CTA) and computed tomography perfusion (CTP) images for the fully automated prediction of HT., Materials and Methods: In this multicenter retrospective study, a total of 229 AIS patients who underwent reperfusion therapy from June 2019 to May 2022 were reviewed. Data set 1, comprising 183 patients from two hospitals, was utilized for training, tuning, and internal validation. Data set 2, consisting of 46 patients from a third hospital, was employed for external testing. DL models were trained to extract valuable information from multiphase CTA and CTP images. The DenseNet architecture was used to construct the DL models. We developed single-phase, single-parameter models, and combined models to predict HT. The models were evaluated using receiver operating characteristic curves., Results: Sixty-nine (30.1%) of 229 patients (mean age, 66.9 years ± 10.3; male, 144 [66.9%]) developed HT. Among the single-phase models, the arteriovenous phase model demonstrated the highest performance. For single-parameter models, the time-to-peak model was superior. When considering combined models, the CTA-CTP model provided the highest predictive accuracy., Conclusions: DL models for predicting HT based on multiphase CTA and CTP images can be established and performed well, providing a reliable tool for clinicians to make treatment decisions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

High-grade serous ovarian cancer has a high degree of malignancy, and at detection, it is prone to infiltration of surrounding soft tissues, as well as metastasis to the peritoneum and lymph nodes, peritoneal seeding, and distant metastasis. Whether recurrence occurs becomes an important reference for surgical planning and treatment methods for this disease. Current recurrence prediction models do not consider the potential pathological relationships between internal tissues of the entire ovary. They use convolutional neural networks to extract local region features for judgment, but the accuracy is low, and the cost is high. To address this issue, this paper proposes a new lightweight deep learning algorithm model for predicting recurrence of high-grade serous ovarian cancer. The model first uses ghost convolution (Ghost Conv) and coordinate attention (CA) to establish ghost counter residual (SCblock) modules to extract local feature information from images. Then, it captures global information and integrates multi-level information through proposed layered fusion Transformer (STblock) modules to enhance interaction between different layers. The Transformer module unfolds the feature map to compute corresponding region blocks, then folds it back to reduce computational cost. Finally, each STblock module fuses deep and shallow layer depth information and incorporates patient's clinical metadata for recurrence prediction. Experimental results show that compared to the mainstream lightweight mobile visual Transformer (MobileViT) network, the proposed slicer visual Transformer (SlicerViT) network improves accuracy, precision, sensitivity, and F1 score, with only 1/6 of the computational cost and half the parameter count. This research confirms that the proposed algorithm model is more accurate and efficient in predicting recurrence of high-grade serous ovarian cancer. In the future, it can serve as an auxiliary diagnostic technique to improve patient survival rates and facilitate the application of the model in embedded devices.

This paper proposes a technique to denoise the worm artifacts of elastogram using 2-D wavelet shrinkage denoising method. Firstly, strain estimate matrix including worm artifacts was decomposed to 3 levels by 2-D discrete wavelet transform with Sym8 wavelet function, and the thresholds were obtained using Birg6-Massart algorithm. Secondly, all the high frequency coefficients on different levels were quantized by using hard threshold and soft threshold function. Finally, the strain estimate matrix was reconstructed by using the 3rd layer low frequency coefficients and other layer quantized high frequency coefficients. The simulation results illustrated that the present technique could efficiently denoise the worm artifacts, enhance the elastogram performance indices, such as elastographic signal-to-noise ratio (SNRe) and elastographic contrast-to-noise ratio (CNRe), and could increase the correlation coefficient between the denoised elastogram and the ideal elastogram. In comparison with 2-D low-pass filtering, it could also obtain the higher elastographic SNRe and CNRe, and have clearer hard lesion edge. In addition, the results demonstrated that the proposed technique could suppress worm artifacts of elastograms for various applied strains. This work showed that the 2-D wavelet shrinkage denoising could efficiently denoise the worm artifacts of elastogram and enhance the performance of elastogram.

Ultrasonic elastography is an imaging technique providing information about the relative stiffness of biological tissues. In general, elastography suffers from noise artifacts, which degrade lesion detectability and increase the likelihood of misdiagnosis. This paper proposes a method called transmit- side frequency compounding for elastography (TSFC). Beamforming is modified to transmit frames with N alternating center frequencies. Pairs of frames with the same center frequency are used to calculate sub-elastograms that are then averaged to produce one compounded elastogram. Simulation results based on an uniformly elastic tissue model demonstrate the decorrelation among sub-elastograms and the improvement in elastographic signal-to-noise ratio (SNRe) achieved by compounding sub-elastograms. An elastic phantom experiment further validates the noise reduction obtained by the proposed technique.