Segmentation Technology of Nucleus Image Based on U-Net Network

Traditional image segmentation methods often encounter problems of low segmentation accuracy and being time-consuming when processing complex tooth Computed Tomography (CT) images. This paper proposes an improved segmentation method for tooth CT images. Firstly, the U-Net network is used to construct a tooth image segmentation model. A large number of feature maps in downsampling are supplemented to downsampling to reduce information loss. At the same time, the problem of inaccurate image segmentation and positioning is solved. Then, the attention module is introduced into the U-Net network to increase the weight of important information and improve the accuracy of network segmentation. Among them, subregion average pooling is used instead of global average pooling to obtain spatial features. Finally, the U-Net network combined with the improved attention module is used to realize the segmentation of tooth CT images. And based on the image collection provided by West China Hospital for experimental demonstration, compared with other algorithms, our method has better segmentation performance and efficiency. The contours of the teeth obtained are clearer, which is helpful to assist the doctor in the diagnosis.

Underwater fish image segmentation is a crucial technique in marine fish monitoring. However, typical underwater fish images often suffer from issues such as color distortion, low contrast, and blurriness, primarily due to the complex and dynamic nature of the marine environment. To enhance the accuracy of underwater fish image segmentation, this paper introduces an innovative neural network model that combines the attention mechanism with a feature pyramid module. After the backbone network processes the input image through convolution, the data pass through the enhanced feature pyramid module, where it is iteratively processed by multiple weighted branches. Unlike conventional methods, the multi-scale feature extraction module that we designed not only improves the extraction of high-level semantic features but also optimizes the distribution of low-level shape feature weights through the synergistic interactions of the branches, all while preserving the inherent properties of the image. This novel architecture significantly boosts segmentation accuracy, offering a new solution for fish image segmentation tasks. To further enhance the model’s robustness, the Mix-up and CutMix data augmentation techniques were employed. The model was validated using the Fish4Knowledge dataset, and the experimental results demonstrate that the model achieves a Mean Intersection over Union (MIoU) of 95.1%, with improvements of 1.3%, 1.5%, and 1.7% in the MIoU, Mean Pixel Accuracy (PA), and F1 score, respectively, compared to traditional segmentation methods. Additionally, a real fish image dataset captured in deep-sea environments was constructed to verify the practical applicability of the proposed algorithm.

Semantic segmentation of weed and crop images is a key component and prerequisite for automated weed management. For weeds in unmanned aerial vehicle (UAV) images, which are usually characterized by small size and easily confused with crops at early growth stages, existing semantic segmentation models have difficulties to extract sufficiently fine features. This leads to their limited performance in weed and crop segmentation of UAV images. We proposed a fine-grained feature-guided UNet, named FG-UNet, for weed and crop segmentation in UAV images. Specifically, there are two branches in FG-UNet, namely the fine-grained feature branch and the UNet branch. In the fine-grained feature branch, a fine feature-aware (FFA) module was designed to mine fine features in order to enhance the model's ability to segment small objects. In the UNet branch, we used an encoder-decoder structure to realize high-level semantic feature extraction in images. In addition, a contextual feature fusion (CFF) module was designed for the fusion of the fine features and high-level semantic features, thus enhancing the feature discrimination capability of the model. The experimental results showed that our proposed FG-UNet, achieved state-of-the-art performance compared to other semantic segmentation models, with mean intersection over union (MIOU) and mean pixel accuracy (MPA) of 88.06% and 92.37%, respectively. The proposed method in this study lays a solid foundation for accurate detection and intelligent management of weeds. It will have a positive impact on the development of smart agriculture. © 2024 Society of Chemical Industry.

Study of bubble behavior in a gas–solid dense-phase fluidized bed based on deep learning

Robust Prediction of Isodose Distribution with a Fully Convolutional Networks (FCN)-Based Deep Learning Model

Single-pixel imaging (SPI) receives widespread attention due to its superior anti-interference capabilities, and image segmentation technology can effectively facilitate its recognition and information extraction. However, the complexity of the target scene and plenty of imaging time in SPI make it challenging to achieve high-quality and concise segmentation. In this paper, we investigate the image-free intricate scene semantic segmentation in SPI. Using “learned”illumination patterns allows for the full extraction of the object’s spatial information, thereby enabling pixel-level segmentation results through the decoding of the received measurements. Simulation and experimentation show that, in the absence of image reconstruction, the mean intersection over union (MIoU) of segmented image can reach higher than 85%, and the Dice coefficient (DICE) close to 90% even at the sampling ratio of 5%. Our approach may be favorable to applications in medical image segmentation and autonomous driving field.

In the past ten years, deep learning has achieved remarkable results in the area of natural image segmentation, and has gradually turned to the field of medical image segmentation. The precise segmentation of spine images can be used for early screening of spondylopathy, which is convenient for early detection and treatment of patients. Aiming at the segmentation of the spine image by the U-Net network, which structure will lead to large model calculation, network overfitting, image size, noise information and other issues. This paper introduces a new method based on spatial pyramid pooling module ASpp on U-Net network and applies the proposed network structure to the segmentation of the spine image. The model is first enhanced by the Spp module and Densenet data; secondly, the U-Net encoding and decoding structure is adopted, and the deep separable convolution is used. This method greatly reduces the complexity and computation of the model, and uses a rectangular convolution kernel to increase the computation of the model in a small amount. Based on the volume, the receptive field of the convolution operation is expanded; finally, in order to effectively enhance the segmentation area of the image, the SE-Net attention mechanism module is added to the lateral connection. The method proposed in this paper conducts ablation experiments on a public dataset of spine images and compares it with the U-Net network. The method proposed IOU in this paper can reach 72%, and the F1-score can reach 0.92. The comparison of the experimental results of spine medical image datasets shows that the method proposed in this paper can effectively improve the accuracy and accuracy of spine image segmentation.

Pre-treatment processing technology plays a crucial role in the overall freshwater fish processing procedure, and automatic head and tail cutting stands out as a significant pre-treatment technique within the industry. The system for removing the head and tail of freshwater fish comprised a Cartesian coordinate manipulator, a fish transfer device, a control system, and an image acquisition device. In the vision system, five image segmentation methods were utilized for fish head and tail image segmentation comparison tests. These methods include U-Net (U-shaped Deep Neural Network), DeeplabV3, PSPNet (Pyramid Scene Parsing Network), FastSCNN (Fast Semantic Segmentation Network), and ICNet (Image Cascade Network), all of which were employed to evaluate their performance. Among the tested segmentation methods, the ICNet demonstrated the most excellent segmentation capability. The experimental results indicated a segmentation accuracy of 99.01%, a mean intersection over union (MIoU) of 82.50%, and an image processing time of 15.25 ms. The results showed that the fish head and tail were successfully cut off using this model for recognition with a circular knife. Consequently, the segmentation model employed in the machine vision system within this study has demonstrated successful applicability in automatically cutting the heads and tails of freshwater fish of various sizes.

Dual attention-based deep learning network for multi-class object semantic segmentation of tunnel point clouds

Aiming at the problems of low detection accuracy, small proportion of crack pixels in images and sensitivity to noise in highway pavement crack detection, an improved UNet model based on multi-scale and attention mechanism (UNet-MSAM) was proposed. In the encoding stage, in order to sense the crack information of different scales in pavement images, the UNet-MSAM added multi-scale feature fusion mechanism. In addition, UNet-MSAM introduced an attention mechanism to model the relationship between the channel and position on the low-level features of the crack, instead of simply integrating the low-level and high-level features of the crack like UNet. The F1 score of UNet-MSAM on DeepCrack dataset was 84.13%, and the Mean Intersection over Union (MIOU) was 85.61%, which was better than the other three methods such as UNet. The above experimental results show that the proposed network model can accurately detect cracks.

Obtaining animal regions and the relative position relationship of animals in the scene is conducive to further studying animal habits, which is of great significance for smart animal farming. However, the complex breeding environment still makes detection difficult. To address the problems of poor target segmentation effects and the weak generalization ability of existing semantic segmentation models in complex scenes, a semantic segmentation model based on an improved DeepLabV3+ network (Imp-DeepLabV3+) was proposed. Firstly, the backbone network of the DeepLabV3+ model was replaced by MobileNetV2 to enhance the feature extraction capability of the model. Then, the layer-by-layer feature fusion method was adopted in the Decoder stage to integrate high-level semantic feature information with low-level high-resolution feature information at multi-scale to achieve more precise up-sampling operation. Finally, the SENet module was further introduced into the network to enhance information interaction after feature fusion and improve the segmentation precision of the model under complex datasets. The experimental results demonstrate that the Imp-DeepLabV3+ model achieved a high pixel accuracy (PA) of 99.4%, a mean pixel accuracy (MPA) of 98.1%, and a mean intersection over union (MIoU) of 96.8%. Compared to the original DeepLabV3+ model, the segmentation performance of the improved model significantly improved. Moreover, the overall segmentation performance of the Imp-DeepLabV3+ model surpassed that of other commonly used semantic segmentation models, such as Fully Convolutional Networks (FCNs), Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP), and U-Net. Therefore, this study can be applied to the field of scene segmentation and is conducive to further analyzing individual information and promoting the development of intelligent animal farming.

FRET-SAM: SAM_Med2D-based automatic FRET two-hybrid analysis.

Accurate extraction of the therapeutic target region in the monitoring ultrasonic image is necessary for high intensity focused ultrasound (HIFU) therapy. However, due to the interference of complex background and large amount of noise in the ultrasonic image, traditional image segmentation algorithms have limitations on the target region segmentation of ultrasonic image. In order to extract the target region in ultrasonic image, a semantic segmentation method combining improved DeepLabv3+ network and improved loss function is proposed. On the basis of DeepLabv3+ network, dense atrous spatial pyramid pooling (DenseASPP) enhanced by attention mechanism through series connection of DenseASPP structure and channel attention mechanism is added into the DeepLabv3+ encoder, and the feature map weighting module is added into the DeepLabv3+ decoder to improve the network ability of feature extraction and the segmentation accuracy of the target region edge. In terms of the loss function, the improved loss obtained by combining the Huber loss and the noise-robust Dice loss is used to solve the proportional imbalance problem of multi-category pixel number, while improving the robustness against noise. The ablation experimental results show that the mean intersection over union (MIoU) of segmentation results with improved DeepLabv3+ network and with improved DeepLabv3+ network trained by the improved loss function increase by 0.46 percent and 0.99 percent respectively compared with the original DeepLabv3+ network. Proposed method effectively improves the segmentation accuracy of the target region in the monitoring ultrasonic image for HIFU therapy and has strong robustness against noise.

This article addresses the semantic segmentation of laparoscopic surgery images, placing special emphasis on the segmentation of structures with a smaller number of observations. As a result of this study, adjustment parameters are proposed for deep neural network architectures, enabling a robust segmentation of all structures in the surgical scene. The U-Net architecture with five encoder-decoders (U-Net5ed), SegNet-VGG19, and DeepLabv3+ employing different backbones are implemented. Three main experiments are conducted, working with Rectified Linear Unit (ReLU), Gaussian Error Linear Unit (GELU), and Swish activation functions. The applied loss functions include Cross Entropy (CE), Focal Loss (FL), Tversky Loss (TL), Dice Loss (DiL), Cross Entropy Dice Loss (CEDL), and Cross Entropy Tversky Loss (CETL). The performance of Stochastic Gradient Descent with momentum (SGDM) and Adaptive Moment Estimation (Adam) optimizers is compared. It is qualitatively and quantitatively confirmed that DeepLabv3+ and U-Net5ed architectures yield the best results. The DeepLabv3+ architecture with the ResNet-50 backbone, Swish activation function, and CETL loss function reports a Mean Accuracy (MAcc) of 0.976 and Mean Intersection over Union (MIoU) of 0.977. The semantic segmentation of structures with a smaller number of observations, such as the hepatic vein, cystic duct, Liver Ligament, and blood, verifies that the obtained results are very competitive and promising compared to the consulted literature. The proposed selected parameters were validated in the YOLOv9 architecture, which showed an improvement in semantic segmentation compared to the results obtained with the original architecture.

Despite the success of crack segmentation, the traditional segmentation strategy and networks are not necessarily suitable for fatigue cracks with unique visual features. Towards the indistinct crack features, a network named Fatigue Crack Unet (FC-Unet) was proposed, where the novel split attention mechanism was incorporated to boost diverse feature extraction. Scaled by various ratios, raw images were cropped and constituted three datasets to represent different segmentation strategies. FC-Unet was trained and tested on each dataset, where the class imbalance problem was alleviated by the adjustment of loss functions. Results showed that the segmentation performance is mainly limited to noise interference. Preserving abundant global contexts to distinguish noises, the global inference is the superior strategy to the previous local inference. Leveraging the wide receptive field in global inference, the down-sampling rate can be decreased to save resolution losses. Combining the focus-based and region-based loss functions, the accuracy of imbalanced data was further improved. Compared with nine classical networks, the proposed FC-Unet enjoyed a powerful backbone and achieved a superior metric of 83.1% mean intersection over union (MIoU).