Accurate Beef Image Segmentation via Self-Prompting Guided Semantic Anything Model

Objective. Cardiovascular disease (CVD) is a group of diseases affecting cardiac and blood vessels, and short-axis cardiac magnetic resonance (CMR) images are considered the gold standard for the diagnosis and assessment of CVD. In CMR images, accurate segmentation of cardiac structures (e.g. left ventricle) assists in the parametric quantification of cardiac function. However, the dynamic beating of the heart renders the location of the heart with respect to other tissues difficult to resolve, and the myocardium and its surrounding tissues are similar in grayscale. This makes it challenging to accurately segment the cardiac images. Our goal is to develop a more accurate CMR image segmentation approach. Approach. In this study, we propose a regional perception and multi-scale feature fusion network (RMFNet) for CMR image segmentation. We design two regional perception modules, a window selection transformer (WST) module and a grid extraction transformer (GET) module. The WST module introduces a window selection block to adaptively select the window of interest to perceive information, and a windowed transformer block to enhance global information extraction within each feature window. The WST module enhances the network performance by improving the window of interest. The GET module grids the feature maps to decrease the redundant information in the feature maps and enhances the extraction of latent feature information of the network. The RMFNet further introduces a novel multi-scale feature extraction module to improve the ability to retain detailed information. Main results. The RMFNet is validated with experiments on three cardiac data sets. The results show that the RMFNet outperforms other advanced methods in overall performance. The RMFNet is further validated for generalizability on a multi-organ data set. The results also show that the RMFNet surpasses other comparison methods. Significance. Accurate medical image segmentation can reduce the stress of radiologists and play an important role in image-guided clinical procedures.

Breast cancer is one of the most prevalent diseases for women worldwide. Early and accurate ultrasound image segmentation plays a crucial role in reducing mortality. Although deep learning methods have demonstrated remarkable segmentation potential, they still struggle with challenges in ultrasound images, including blurred boundaries and speckle noise. To generate accurate ultrasound image segmentation, this paper proposes the Edge-Aware Multi-Scale Group-Mix Attention Network (EMGANet), which generates accurate segmentation by integrating deep and edge features. The Multi-Scale Group Mix Attention block effectively aggregates both sparse global and local features, ensuring the extraction of valuable information. The subsequent Edge Feature Enhancement block then focuses on cancer boundaries, enhancing the segmentation accuracy. Therefore, EMGANet effectively tackles unclear boundaries and noise in ultrasound images. We conduct experiments on two public datasets (Dataset-B, BUSI) and one private dataset which contains 927 samples from Renmin Hospital of Wuhan University (BUSI-WHU). EMGANet demonstrates superior segmentation performance, achieving an overall accuracy (OA) of 98.56%, a mean IoU (mIoU) of 90.32%, and an ASSD of 6.1 pixels on the BUSI-WHU dataset. Additionally, EMGANet performs well on two public datasets, with a mIoU of 88.2% and an ASSD of 9.2 pixels on Dataset-B, and a mIoU of 81.37% and an ASSD of 18.27 pixels on the BUSI dataset. EMGANet achieves a state-of-the-art segmentation performance of about 2% in mIoU across three datasets. In summary, the proposed EMGANet significantly improves breast cancer segmentation through Edge-Aware and Group-Mix Attention mechanisms, showing great potential for clinical applications.

The quality of dental pulp cone beam computed tomography (CBCT) images significantly impacts the accuracy of disease diagnosis. However, existing CBCT images often suffer from deficiencies such as artifacts, noise, resolution limitations and data processing issues, which frequently lead to misdiagnoses and missed diagnoses. This study aims to construct a highly accurate dental pulp CBCT image segmentation model based on the pulse-coupled neural network (PCNN) approach. By optimizing the PCNN model, this work sought to enhance the segmentation accuracy of dental pulp CBCT images and to evaluate the clinical efficacy of dental pulp revascularization surgery through quantitative analysis of the CBCT images, thereby improving the diagnostic accuracy of imaging data and the reliability of treatment outcome assessments. To achieve this, a cross-entropy thresholding method was introduced to optimize the PCNN model, establishing the Otsu-PCNN model for CBCT pulp segmentation. The model performance was evaluated using accuracy, sensitivity, positive predictive value (PPV) and the area under the receiver operating characteristic curve (ROC). The study involved 43 patients with pulp diseases who underwent dental pulp revascularization, with follow-ups conducted preoperatively and at 3, 6, 9, 12 and 18 months postoperatively. Changes in root image area (RIA), tooth root length (TRL) and apical diameter (AHd) were recorded, along with the classification of root development types. The results showed that the sensitivity and PPV of the Otsu-PCNN model were significantly higher than those of the optimized PCNN model ([Formula: see text]). The detection time for the Otsu-PCNN model was 13.80 s, with an ROC area of 0.93. The overall effective treatment rate for all patients was 100%. RIA, TRL and AHd increased from 30.55%, 2.58% and 19.95% at 3 months postoperatively to 84.85%, 25.18% and 51.62% at 18 months, respectively. Of the roots examined, 22 (48.84%) exhibited Type I root development. This study demonstrates that the optimization of the PCNN model significantly enhances the segmentation accuracy of dental pulp CBCT images, and that dental pulp revascularization surgery can effectively improve root development, yielding significant results in the treatment of pulp diseases.

Based on the dynamic structure, we design a system that can perform accurate stromule image segmentation, branch tip detection and tracking automatically. We substitute the user constraints in active contour segmentation by spatial fuzzy c-means clustering for providing more precise segmentation result. Based on the segmented contour after smoothing, we create a surface normal based feature that can accurately detect the branch tips. We further combine normal information together with tip position coordinate to apply ICP to track the branch tips moving path.

Accurate and robust medical image segmentation is crucial for assisting disease diagnosis, making treatment plan, and monitoring disease progression. Adaptive to different scale variations and regions of interest is essential for high accuracy in automatic segmentation methods. Existing methods based on the U-shaped architecture respectively tackling intra- and inter-scale problem with a hierarchical encoder, however, are restricted by the scope of multi-scale modeling. In addition, global attention and scaling attention in regions of interest have not been appropriately adopted, especially for the salient features. To address these two issues, we propose a ConvNet-Transformer hybrid framework named SSCFormer for accurate and versatile medical image segmentation. The intra-scale ResInception and inter-scale transformer bridge are designed to collaboratively capture the intra- and inter-scale features, facilitating the interaction of small-scale disparity information at a single stage with large-scale from multiple stages. Global attention and scaling attention are cleverly integrated from a spatial-channel-aware perspective. The proposed SSCFormer is tested on four different medical image segmentation tasks. Comprehensive experimental results show that SSCFormer outperforms the current state-of-the-art methods.

Artificial intelligence recognition of pathological T stage and tumor invasion in rectal cancer based on large panoramic pathological sections

Image segmentation of brain low-grade glioma (LGG) magnetic resonance imaging (MRI) contributes tremendously to diagnosis, classification and treatment of the disease. A tangible, accurate, reliable and fast image segmentation technique is demanded in clinical diagnosis and research. The emerging machine learning technique has been demonstrated its unique capability in the field of medical image processing, including medical image segmentation. Support vector machine (SVM) and convolutional neural network (CNN) are two widely used machine learning methods. In this work, image segmentation tools based on SVM and CNN are developed and evaluated for brain LGG MR image segmentation studies. The segmentation performance in terms of accuracy and cost is quantitatively analyzed and compared between the SVM and CNN techniques developed. Computed on the Google CoLab, each of the 109 SVM models represents an individual patient, is trained using a single image of that patient and takes a few seconds to complete. The CNN model is trained on a drastically larger dataset of 19,760 data augmented images and takes approximately 2 hours to obtain the most optimal result. The SVM models achieved an average and median accuracy of 0.937 and 0.976 respectively, precision of 0.456 and 0.535 respectively, recall of 0.878 and 0.906 respectively, and F1 score of 0.546 and 0.662 respectively. Although the CNN model required a significantly longer calculation time, it surpassed the SVM models in performance in LGG MR image segmentation, achieving an accuracy of 0.998, a precision of 0.999, a recall of 0.999 and an F1 score of 0.999. This study shows that SVM with appropriate filtering techniques is capable of obtaining reliable and fast segmentation of brain LGG MR images with sufficient accuracy and limited image data. CNN technique outperforms SVM in the accuracy of segmentation with requirements of significantly enlarged data set, long computation time and high-performance computer.

With the leaps and bounds of computer performance and the advent of the era of big data, deep learning has drawn more and more attention from all walks of life. It can combine low-level features to form more abstract high-level features and describe the data more essentially. Therefore, it is widely used in various fields such as computer vision. Image segmentation is one of the most basic research topics in the field of computer vision. The main purpose is to extract regions of interest from images for later image processing research. In 3D reconstruction based on sequence images, the segmentation accuracy and speed of sequence images determine the quality and efficiency of target reconstruction. Therefore, when facing large-scale sequence images, the biggest problem is how to improve the segmentation speed while ensuring accuracy. Based on the above background, the research content of this article is an indoor scene segmentation algorithm based on full convolutional neural network. According to the characteristics of indoor application scenes, this paper proposes a fast convolutional neural network image segmentation method to segment the indoor scene image and construct the fast fully convolutional networks (FFCN) for indoor scene image segmentation uses inter-layer fusion to reduce the amount of network calculation parameters and avoid the loss of picture feature information by continuous convolution. In order to verify the effectiveness of the network, in this paper, a basic living object data set (XAUT data set) in an indoor environment is created. The XAUT data set is used to train the FFCN network under the Caffe framework to obtain an indoor scene segmentation model. In order to compare the effectiveness of the model, the structure of the worn FCN8s, FCN16s, and FCN32s models was fine-tuned, and the corresponding algorithm model for indoor scene segmentation was obtained by training with the XAUT data set. The experimental results show that the pixel recognition accuracy of all types of networks has reached 86%, and the mean IU ratio has reached more than 63%. The mean IU of the FCN8s network is the highest at 70.38%, but its segmentation speed is only 1/5 of FFCN. On the premise that other types of indicators are not much different, the average segmentation speed on FFCN fast segmentation convolutional neural network reaches 40 fps. It can be seen that the scale fusion technology can well avoid the loss of image feature information in the network convolution and reddening process. Compared with other FCN networks, it has a faster speed and is conducive to real-time image preprocessing.

This article presents the application of a new approach to segmentation of images of the iris and pupil of the human eye using the pretrained neural network model YoLoV5. Despite the use of modern neural network technologies and computer vision methods for image processing and eye segmentation, not all of them guarantee obtaining a highly accurate result of the desired circles. Examples of incorrect segmentation of the iris and pupil obtained using the Mediapipe framework and the FloodFill algorithm are given. Smoothing filters were previously used to eliminate artifacts, noise and outliers in the data. At the same time, the use of smoothing filters contributes to the distortion of the data they process, especially when working in real-time systems with the property of observation duration. Since the diagnosis of the functional state of a person by the pupillometry method is sensitive to the quality of the original data, the use of smoothing filters in a number of diagnostic cases is not acceptable. To improve the accuracy of iris and eye image segmentation and avoid data distortion by smoothing filters, the authors developed a computer vision module based on the eye circumference segmentation mechanism using the pre-trained YoLoV5 neural network model. Examples of eye circumference segmentation by the YoLoV5 neural network are given. The accuracy of iris and pupil image segmentation was assessed by comparing the number of outliers and omissions in time series values obtained using the approaches discussed in the article. The approach to using the YoLoV5 neural network model proposed in the article is implemented in solving the problem of driver fatigue monitoring and is used in a prototype of a specialized device developed by the authors.

Rethinking the unpretentious U-net for medical ultrasound image segmentation

Accurate segmentation of ore images plays a significant role in automatic geometric parameter detecting and composition analyzing of ore dressing progress. Semantic segmentation based on deep learning is a promising method for accurate ore image segmentation. However, the similar appearance with low contrast and blurry boundary of ores in image hamper segmentation accuracy. Moreover, it is difficult to train a deep network due to limited available ore images. In this work, an improved encoder-decoder network based on U-Net is proposed to handle above challenges. A contour awareness loss (CAL) is proposed to improve model sensitivity to misclassified pixels, pixels of similar appearance, and pixels near the boundary. The proposed scheme is verified on ore images by benchmarking against state-of-the-art segmentation methods. Experiment results show that the proposed scheme achieves 88.6% pixel-wise accuracy (PA) and 66.0% mean intersection over union (mIoU).

This study investigates the value of magnetic resonance imaging (MRI) based on a deep learning algorithm in the diagnosis of diabetic macular edema (DME) patients. A total of 96 patients with DME were randomly divided into the experimental group (N = 48) and the control group (N = 48). A deep learning 3D convolutional neural network (3D-CNN) algorithm for MRI images of patients with DME was designed. The application value of this algorithm was comprehensively evaluated by MRI image segmentation Dice value, sensitivity, specificity, and other indicators and diagnostic accuracy. The results showed that the quality of MRI images processed by the 3D-CNN algorithm based on deep learning was significantly improved, and the Dice value, sensitivity, and specificity index data were significantly better than those of the traditional CNN algorithm (P < 0.05). In addition, the diagnostic accuracy of MRI images processed by this algorithm was 93.78 ± 5.32%, which was significantly better than the diagnostic accuracy of 64.25 ± 10.24% of traditional MRI images in the control group (P < 0.05). In summary, the 3D-CNN algorithm based on deep learning can significantly improve the accuracy and sensitivity of MRI image recognition and segmentation in patients with DME, can significantly improve the diagnostic accuracy of MRI in patients with DME, and has a good clinical application value.

Precise and rapid ultrasound-based breast cancer diagnosis is essential for effective treatment. However, existing ultrasound image segmentation methods often fail to capture both global contextual features and fine-grained boundary details. This study proposes a dual-branch network architecture that combines the Swin Transformer and Segment Anything Model (SAM) to enhance breast ultrasound image (BUSI) segmentation accuracy and reliability. Our network integrates the global attention mechanism of the Swin Transformer with fine-grained boundary detection from SAM through a multi-stage feature fusion module. We evaluated our method against state-of-the-art methods on two datasets: the Breast Ultrasound Images dataset from Wuhan University (BUSI-WHU), which contains 927 images (560 benign and 367 malignant) with ground truth masks annotated by radiologists, and the public BUSI dataset. Performance was evaluated using mean Intersection-over-Union (mIoU), 95th percentile Hausdorff Distance (HD95) and Dice Similarity coefficients, with statistical significance assessed using two-tailed independent t-tests with Holm-Bonferroni correction ( ). On our proposed dataset, the network achieved a mIoU of 90.82% and a HD95 of 23.50 pixels, demonstrating significant improvements over current state-of-the-art methods with effect sizes for mIoU ranging from 0.38 to 0.61 (p 0.05). On the BUSI dataset, the network achieved a mIoU of 82.83% and a HD95 of 71.13 pixels, demonstrating comparable improvements with effect sizes for mIoU ranging from 0.45 to 0.58 (p 0.05). Our dual-branch network leverages the complementary strengths of Swin Transformer and SAM through a fusion mechanism, demonstrating superior breast ultrasound segmentation performance. Our code is publicly available at https://github.com/Skylanding/DSATNet.

HighlightsWhat are the main findings?Combining domain-generalized pre-training with test-time adaptation substantially enhances segmentation accuracy for medical images in out-of-domain scenarios.Generalizing image descriptors and intensity augmentation during training and adaptation outperformed complex existing methods in several scenarios.What is the implication of the main finding?We provide a powerful tool to achieve high segmentation accuracy out-of-domain, even without access to source data.Our efficient method is applicable to many top public 2D and 3D segmentation models trained on large, private datasets.Applying pre-trained medical deep learning segmentation models to out-of-domain images often yields predictions of insufficient quality. In this study, we propose using a robust generalizing descriptor, along with augmentation, to enable domain-generalized pre-training and test-time adaptation, thereby achieving high-quality segmentation in unseen domains. In this study, five different publicly available datasets, including 3D CT and MRI images, are used to evaluate segmentation performance in out-of-domain scenarios. The settings include abdominal, spine, and cardiac imaging. Domain-generalized pre-training on source data is used to obtain the best initial performance in the target domain. We introduce a combination of the generalizing SSC descriptor and GIN intensity augmentation for optimal generalization. Segmentation results are subsequently optimized at test time, where we propose adapting the pre-trained models for every unseen scan using a consistency scheme with the augmentation–descriptor combination. The proposed generalized pre-training and subsequent test-time adaptation improve model performance significantly in CT to MRI cross-domain prediction for abdominal (+46.2 and +28.2 Dice), spine (+72.9), and cardiac (+14.2 and +55.7 Dice) scenarios (p < 0.001). Our method enables the optimal, independent use of source and target data, successfully bridging domain gaps with a compact and efficient methodology.

In recent years, accurate segmentation of images is a very challenging task for image processing applications. Image segmentation can also be stated as clustering problem in which image pixels are clustered according to the homogeneity of their feature values. Crisp K-means clustering algorithm can achieve the solution of this problem. But it is not suitable for coinciding partition and it is unable to handle noisy data. Fuzzy form of C-means clustering can manage overlapping partition problem, but traditional FCM is also sensitive to noise pixels. In this paper, neighborhood-influenced Fuzzy C-means (NFCM) algorithm is proposed where spatial neighborhood information of pixels is incorporated with the traditional fuzzy c-means algorithm. NFCM is giving more accurate segmentation result compared to hard c-means and fuzzy c-means based segmentation techniques.