Mean Intersection Over Union Research Articles

The Segment Anything foundation model achieves favorable brain tumor auto-segmentation accuracy in MRI to support radiotherapy treatment planning.

Promptable foundation auto-segmentation models like Segment Anything (SA, Meta AI, New York, USA) represent anovel class of universal deep learning auto-segmentation models that could be employed for interactive tumor auto-contouring in RT treatment planning. Segment Anything was evaluated in an interactive point-to-mask auto-segmentation task for glioma brain tumor auto-contouring in 16,744 transverse slices from 369 MRI datasets (BraTS 2020 dataset). Up to nine interactive point prompts were automatically placed per slice. Tumor boundaries were auto-segmented on contrast-enhanced T1w sequences. Out of the three auto-contours predicted by SA, accuracy was evaluated for the contour with the highest calculated IoU (Intersection over Union, "oracle mask," simulating interactive model use with selection of the best tumor contour) and for the tumor contour with the highest model confidence ("suggested mask"). Mean best IoU (mbIoU) using the best predicted tumor contour (oracle mask) in full MRI slices was 0.762 (IQR 0.713-0.917). The best 2D mask was achieved after amean of 6.6 interactive point prompts (IQR 5-9). Segmentation accuracy was significantly better for high- compared to low-grade glioma cases (mbIoU 0.789 vs. 0.668). Accuracy was worse using the suggested mask (0.572). Stacking best tumor segmentations from transverse MRI slices, mean 3D Dice score for tumor auto-contouring was 0.872, which was improved to 0.919 by combining axial, sagittal, and coronal contours. The Segment Anything foundation segmentation model can achieve high accuracy for glioma brain tumor segmentation in MRI datasets. The results suggest that foundation segmentation models could facilitate RT treatment planning when properly integrated in aclinical application.

TIA-UNet: Transformer-Enhanced Deep Learning for Adolescent Idiopathic Scoliosis Spinal X-Ray Image Segmentation

Abstract Adolescent Idiopathic Scoliosis (AIS) is a common spinal deformity where precise diagnosis is crucial for developing effective treatment strategies. Traditional manual X-ray image analysis is time-consuming and highly dependent on the operator’s expertise, thus constraining diagnostic efficiency and accuracy.This study aimed to develop an automated thoracolumbar spine segmentation method utilizing deep learning to enhance the efficiency and accuracy of AIS diagnosis. We introduced TIA-UNet, an innovative network architecture that combines Convolutional Neural Networks (CNN) with Transformer models.By integrating the IR Block and DA Block, TIA-UNet was optimized for feature extraction and multi-scale information fusion. The model underwent training and validation using our established Adolescent Scoliosis Medical Dataset (ASMD).TIA-UNet attained a Dice similarity coefficient of 90.02%, a Mean Intersection over Union (MIoU) of 81.96%, and a Hausdorff Distance (HD) of 4.09, significantly surpassing current state-of-the-art methods such as UNet and TransUNet. Moreover, TIA-UNet exhibited superior computational efficiency regarding parameter count, inference time, and floatingpoint operations (FLOPs).As an automated medical image segmentation algorithm, TIA-UNet enhanced segmentation accuracy while maintaining high computational efficiency, demonstrating significant potential for clinical diagnostic applications. This study provides compelling evidence supporting the utilization of deep learning
techniques in medical image analysis.

Research on Traffic Marking Segmentation Detection Algorithm Based on Feature Fusion

Identifying road markings is a very important part of the vehicle environment sensing system and plays a crucial role in a vehicle’s correct understanding of a current traffic situation. However, road traffic markings are interfered with by a variety of factors, such as being obscured and the viewpoint of the vehicle sensors, resulting in large errors in the existing detection methods. In order to make the target detection task applicable to irregular objects or to detection tasks with higher accuracy requirements while reducing the waste of computational resources, this paper improves the accuracy of traffic marking segmentation detection by designing a multi-type traffic marking segmentation detection model based on image segmentation algorithms and designing a segmentation guidance matrix module based on a rank guidance matrix computation method. By constructing a comprehensive traffic marking detection model, a unified road traffic marking detection is achieved. Finally, the new traffic marking datasets ApolloScape-Precise and ApolloScape-Large are constructed based on the existing ApolloScape dataset, and experimental validation is carried out on these two datasets. The results show that the index MIoU (Mean Intersection over Union) of traffic marking segmentation detection reaches 61.44% and 70.15%, thus achieving a more perfect road traffic marking detection and right-of-way information perception and proving the effectiveness of the integrated traffic marking detection method designed in this paper.

Hybrid Feature Extraction for Breast Cancer Classification Using the Ensemble Residual VGG16 Deep Learning Model

Introduction: Breast Cancer (BC) is a significant cause of high mortality amongst women globally and probably will remain a disease posing challenges about its detectability. Advancements in medical imaging technology have improved the accuracy and efficiency of breast cancer classification. However, tumor features' complexity and imaging data variability still pose challenges. Method: This study proposes the Ensemble Residual-VGG-16 model as a novel combination of the Deep Residual Network (DRN) and VGG-16 architecture. This model is purposely engineered with maximal precision for the task of breast cancer diagnosis based on mammography images. We assessed its performance by accuracy, recall, precision, and the F1-Score. All these metrics indicated the high performance of this Residual-VGG-16 model. The diagnostic residual-VGG16 performed exceptionally well with an accuracy of 99.6%, precision of 99.4%, recall of 99.7%, F1 score of 98.6%, and Mean Intersection over Union (MIoU) of 99.8% with MIAS datasets. Result: Similarly, the INBreast dataset achieved an accuracy of 93.8%, a precision of 94.2%, a recall of 94.5%, and an F1-score of 93.4% Conclusion: The proposed model is a significant advancement in breast cancer diagnosis, with high accuracy and potential as an automated grading.

A semi‐supervised segmentation network fusing pseudo‐label with multi‐level feature consistency correction for hard exudates

AbstractTimely detection of hard exudates in fundus images can effectively avoid the severity of the disease, but the labelling of small and numerous lesion areas requires a lot of labour costs. This paper proposes a semi‐supervised segmentation network, which integrates pseudo‐labels and multi‐level features consistency correction. It achieves accurate segmentation of hard exudates by making full use of a small amount of labelled data and a large amount of unlabelled data. The network effectively extracts features from the unlabelled data through knowledge transfer of the teacher‐student model, and incorporates a Transformer network for auxiliary training to promote the quality of transfer. In addition, three unsupervised losses are introduced to improve the performance: the perturbation loss improves the robustness of the model to noise by adding different noises to the same input; the multi‐level feature consistency correction loss ensures the consistency of features of the student model at different scales; and the pseudo‐labelling cross‐supervision loss utilizes the generated pseudo‐labels for supervision between CNN and Transformer. By comparing the segmentation results with different proportion of the labelled data, it has better segmentation performance compared to other methods. The proposed methods can totally increase dice by 16.56% and mean intersection over union (MIoU) by 25.11%.

FG-UNet: fine-grained feature-guided UNet for segmentation of weeds and crops in UAV images.

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.

HSP-UNet: An Accuracy and Efficient Segmentation Method for Carbon Traces of Surface Discharge in the Oil-Immersed Transformer

Restricted by a metal-enclosed structure, the internal defects of large transformers are difficult to visually detect. In this paper, a micro-robot is used to visually inspect the interior of a transformer. For the micro-robot to successfully detect the discharge level and insulation degradation trend in the transformer, it is essential to segment the carbon trace accurately and rapidly from the complex background. However, the complex edge features and significant size differences of carbon traces pose a serious challenge for accurate segmentation. To this end, we propose the Hadamard production-Spatial coordinate attention-PixelShuffle UNet (HSP-UNet), an innovative architecture specifically designed for carbon trace segmentation. To address the pixel over-concentration and weak contrast of carbon trace image, the Adaptive Histogram Equalization (AHE) algorithm is used for image enhancement. To realize the effective fusion of carbon trace features with different scales and reduce model complexity, the novel grouped Hadamard Product Attention (HPA) module is designed to replace the original convolution module of the UNet. Meanwhile, to improve the activation intensity and segmentation completeness of carbon traces, the Spatial Coordinate Attention (SCA) mechanism is designed to replace the original jump connection. Furthermore, the PixelShuffle up-sampling module is used to improve the parsing ability of complex boundaries. Compared with UNet, UNet++, UNeXt, MALUNet, and EGE-UNet, HSP-UNet outperformed all the state-of-the-art methods on both carbon trace datasets. For dendritic carbon traces, HSP-UNet improved the Mean Intersection over Union (MIoU), Pixel Accuracy (PA), and Class Pixel Accuracy (CPA) of the benchmark UNet by 2.13, 1.24, and 4.68 percentage points, respectively. For clustered carbon traces, HSP-UNet improved MIoU, PA, and CPA by 0.98, 0.65, and 0.83 percentage points, respectively. At the same time, the validation results showed that the HSP-UNet has a good model lightweighting advantage, with the number of parameters and GFLOPs of 0.061 M and 0.066, respectively. This study could contribute to the accurate segmentation of discharge carbon traces and the assessment of the insulation condition of the oil-immersed transformer.

USCT-UNet: Rethinking the Semantic Gap in U-Net Network from U-shaped Skip Connections with Multichannel Fusion Transformer.

Medical image segmentation is a crucial component of computer-aided clinical diagnosis, with state-of-the-art models often being variants of U-Net. Despite their success, these models' skip connections introduce an unnecessary semantic gap between the encoder and decoder, which hinders their ability to achieve the high precision required for clinical applications. Awareness of this semantic gap and its detrimental influences have increased over time. However, a quantitative understanding of how this semantic gap compromises accuracy and reliability remains lacking, emphasizing the need for effective mitigation strategies. In response, we present the first quantitative evaluation of the semantic gap between corresponding layers of U-Net and identify two key characteristics: 1) The direct skip connection (DSC) exhibits a semantic gap that negatively impacts models' performance; 2) The magnitude of the semantic gap varies across different layers. Based on these findings, we re-examine this issue through the lens of skip connections. We introduce a Multichannel Fusion Transformer (MCFT) and propose a novel USCT-UNet architecture, which incorporates U-shaped skip connections (USC) to replace DSC, allocates varying numbers of MCFT blocks based on the semantic gap magnitude at different layers, and employs a spatial channel cross-attention (SCCA) module to facilitate the fusion of features between the decoder and USC. We evaluate USCT-UNet on four challenging datasets, and the results demonstrate that it effectively eliminates the semantic gap. Compared to using DSC, our USC and SCCA strategies achieve maximum improvements of 4.79% in the Dice coefficient, 5.70% in mean intersection over union (MIoU), and 3.26 in Hausdorff distance.

A globally sampled high-resolution hand-labeled validation dataset for evaluating surface water extent maps

Abstract. Effective monitoring of global water resources is increasingly critical due to climate change and population growth. Advancements in remote sensing technology, specifically in spatial, spectral, and temporal resolutions, are revolutionizing water resource monitoring, leading to more frequent and high-quality surface water extent maps using various techniques such as traditional image processing and machine learning algorithms. However, satellite imagery datasets contain trade-offs that result in inconsistencies in performance, such as disparities in measurement principles between optical (e.g., Sentinel-2) and radar (e.g., Sentinel-1) sensors and differences in spatial and spectral resolutions among optical sensors. Therefore, developing accurate and robust surface water mapping solutions requires independent validations from multiple datasets to identify potential biases within the imagery and algorithms. However, high-quality validation datasets are expensive to build, and few contain information on water resources. For this purpose, we introduce a globally sampled, high-spatial-resolution dataset labeled using 3 m PlanetScope imagery (Planet Team, 2017). Our surface water extent dataset comprises 100 images, each with a size of 1024×1024 pixels, which were sampled using a stratified random sampling strategy covering all 14 biomes. We highlighted urban and rural regions, lakes, and rivers, including braided rivers and coastal regions. We evaluated two surface water extent mapping methods using our dataset – Dynamic World (Brown et al., 2022), based on Sentinel-2, and the NASA IMPACT model (Paul and Ganju, 2021), based on Sentinel-1. Dynamic World achieved a mean intersection over union (IoU) of 72.16 % and F1 score of 79.70 %, while the NASA IMPACT model had a mean IoU of 57.61 % and F1 score of 65.79 %. Performance varied substantially across biomes, highlighting the importance of evaluating models on diverse landscapes to assess their generalizability and robustness. Our dataset can be used to analyze satellite products and methods, providing insights into their advantages and drawbacks. Our dataset offers a unique tool for analyzing satellite products, aiding the development of more accurate and robust surface water monitoring solutions. The dataset can be accessed via https://doi.org/10.25739/03nt-4f29 (Mukherjee et al., 2024).

End-to-End Fully Automated Lung Cancer Volume Estimation System

The volume of the tumor plays a very crucial role in deciding the stage of lung cancer which in turn helps in deciding the best treatment and its schedule. Currently used computer-based volume estimation techniques are semi-automatic with limited accuracy. For any automatic lung cancer segmentation system, lung CT scans of hundreds of patients are required along with their corresponding annotated segmentation masks. It is difficult to get accurately annotated data as cancer segmentation of CT scans done by the radiologists, is a time-consuming manual process. Also, it is subjective and prone to intra and inter-observer variability. Further, owing to the irregular shape of the cancerous tumor, accurate volume estimation becomes a challenge with regular convolution models. This paper proposes an end-to-end automatic tumor volume estimation model that estimates volume using the GPR (Gaussian Process Regression) interpolation method. The proposed modified cancer segmentation model uses deformable convolutions. This modification offers a higher segmentation accuracy in terms of IoU (Intersection over Union) and clearly defined nodule boundaries with correct retention of the nodule shape. The research was undertaken in collaboration with Nanavati Hospital, Mumbai, and all the models were validated on a real dataset obtained from the hospital. The proposed model gives a mean segmentation IoU (Intersection over Union) of 0.9035 and a volume estimation accuracy of 93.13% which are almost 5% and 3% higher than 0.8548 and 90.51% which are the corresponding results obtained using a standard U-net++ algorithm.

Electrical Impedance Tomography-Based Electronic Skin for Multi-Touch Tactile Sensing Using Hydrogel Material and FISTA Algorithm.

Flexible electronic skin (e-skin) can enable robots to have sensory forms similar to human skin, enhancing their ability to obtain more information from touch. The non-invasive nature of electrical impedance tomography (EIT) technology allows electrodes to be arranged only at the edges of the skin, ensuring the stretchability and elasticity of the skin's interior. However, the image quality reconstructed by EIT technology has deteriorated in multi-touch identification, where it is challenging to clearly reflect the number of touchpoints and accurately size the touch areas. This paper proposed an EIT-based flexible tactile sensor that employs self-made hydrogel material as the primary sensing medium. The sensor's structure, fabrication process, and tactile imaging principle were elaborated. To improve the quality of image reconstruction, the fast iterative shrinkage-thresholding algorithm (FISTA) was embedded into the EIDORS toolkit. The performances of the e-skin in aspects of assessing the touching area, quantitative force sensing and multi-touch identification were examined. Results showed that the mean intersection over union (MIoU) of the reconstructed images was improved up to 0.84, and the tactile position can be accurately imaged in the case of the number of the touchpoints up to seven (larger than two to four touchpoints in existing studies), proving that the combination of the proposed sensor and imaging algorithm has high sensitivity and accuracy in multi-touch tactile sensing. The presented e-skin shows potential promise for the application in complex human-robot interaction (HRI) environments, such as prosthetics and wearable devices.

Intelligent classification of water bodies with different turbidity levels based on Gaofen-1 multispectral imagery

The turbidity of water is crucial for the health of river and lake ecosystems, necessitating efficient monitoring for effective water management. Existing methods for studying water turbidity's spatial and temporal distribution rely mostly on measured data. There is limited research on the classification of water bodies with different turbidity levels. The main challenge lies in determining the boundaries of liquid water bodies at various turbidity levels, making it challenging to classify them accurately using traditional remote sensing image classification methods. This paper proposes and validates an intelligent turbidity classification method based on deep learning using GaoFen-1 multispectral remote sensing imagery. An adaptive threshold water extraction method based on the Normalized Difference Water Index is proposed to capture water boundaries more accurately to improve the accuracy of extracting nearshore water bodies. A semi-automatic semantic annotation method for water turbidity is introduced to reduce manual labeling costs. The paper applies mode filtering to address edge noise issues and establishes a high-quality training sample dataset. After comparing the accuracy of various neural network models, DeepLab V3+ is selected for intelligent turbidity classification. The results show high accuracy, with mean intersection over union (MIoU), mean F1 score (MF1), and overall accuracy (OA) reaching 94.73%, 97.29%, and 97.54%, respectively. The proposed method and experiments demonstrate the feasibility of intelligent classification of water bodies with different turbidity levels using deep learning networks. This provides a new approach for large-scale and efficient remote sensing water turbidity monitoring.

Lightweight multi-scale feature dense cascade neural network for scene understanding of intelligent autonomous platform

Understanding the surrounding environment is an essential part of intelligent autonomous platform to complete autonomous driving or other autonomous tasks. Thereinto, scene understanding is a key technology to understand environmental information. For intelligent autonomous platform, it not only requires high accuracy, but also has a strict requirement for the inference speed. Moreover, it should be able to perform normally in complex environments such as target occlusion, the influence of illumination, and objects at different sizes. For tackling these problems, in this study, a lightweight multi-scale feature dense cascade neural network (LMFDCNet) is proposed in real-time scene understanding in complex scenes. Furthermore, trilateral fusion module is adopted to enhance low-level detailed features or high-level semantic features of each branch to describe the object better in different environments. A multi-scale upsampling module that fuses contextual information at different scales allows objects to be represented at different size. The results indicate that LMFDCNet produces 99.1 % classification accuracy on the tobacco dataset. On the Cityscapes dataset, we achieve 74.2 % Mean Intersection over Union (MIoU) with 13.6 M parameters at the speed of 38.2 FPS on a single 1070Ti card. To test the performance on the real-time application in the real world, LMFDCNet is deployed on the autonomous platform for real-time vision task such as classification of tobacco leaf state in curing process and semantic segmentation in the driving process of autonomous platform. The results reveal that LMFDCNet perform well on the AI embedded device and could be applied into intelligent autonomous platform.

Lightweight deep neural network for radio frequency interference detection and segmentation in synthetic aperture radar

Radio frequency interference (RFI) poses challenges in the analysis of synthetic aperture radar (SAR) images. Existing RFI suppression systems rely on prior knowledge of the presence of RFI. This paper proposes a lightweight neural network-based algorithm for detecting and segmenting RFI (LDNet) in the time-frequency domain. The network accurately delineates RFI pixel regions in time-frequency spectrograms. To mitigate the impact on the operational speed of the entire RFI suppression system, lightweight modules and pruning operations are introduced. Compared to threshold-based RFI detection algorithms, deep learning-based segmentation networks, and AC-UNet specifically designed for RFI detection, LDNet achieves improvements in mean intersection over union (MIoU) by 24.56%, 13.29%, and 7.54%, respectively.Furthermore, LDNet reduces model size by 99.03% and inference latency by 24.53% compared to AC-UNet.

An Optimized Semi-Supervised Generative Adversarial Network Rice Extraction Method Based on Time-Series Sentinel Images

In response to the limitations of meteorological conditions in global rice growing areas and the high cost of annotating samples, this paper combines the Vertical-Vertical (VV) polarization and Vertical-Horizontal (VH) polarization backscatter features extracted from Sentinel-1 synthetic aperture radar (SAR) images and the NDVI, NDWI, and NDSI spectral index features extracted from Sentinel-2 multispectral images. By leveraging the advantages of an optimized Semi-Supervised Generative Adversarial Network (optimized SSGAN) in combining supervised learning and semi-supervised learning, rice extraction can be achieved with fewer annotated image samples. Within the optimized SSGAN framework, we introduce a focal-adversarial loss function to enhance the learning process for challenging samples; the generator module employs the Deeplabv3+ architecture, utilizing a Wide-ResNet network as its backbone while incorporating dropout layers and dilated convolutions to improve the receptive field and operational efficiency. Experimental results indicate that the optimized SSGAN, particularly when utilizing a 3/4 labeled sample ratio, significantly improves rice extraction accuracy, leading to a 5.39% increase in Mean Intersection over Union (MIoU) and a 2.05% increase in Overall Accuracy (OA) compared to the highest accuracy achieved before optimization. Moreover, the integration of SAR and multispectral data results in an OA of 93.29% and an MIoU of 82.10%, surpassing the performance of single-source data. These findings provide valuable insights for the extraction of rice information in global rice-growing regions.

Multi-label deep learning for comprehensive optic nerve head segmentation through data of fundus images

Early diagnosis and continuous monitoring of patients with eye diseases are critical in computer-aided detection (CAD) techniques. Semantic segmentation, a key component in computer vision, enables pixel-level classification and provides detailed information about objects within images. In this study, we present three U-Net models designed for multi-class semantic segmentation, leveraging the U-Net architecture with transfer learning. To generate ground truth for the HRF dataset, we combine two U-Net models, namely MSU-Net and BU-Net, to predict probability maps for the optic disc and cup regions. Binary masks are then derived from these probability maps to extract the optic disc and cup regions from retinal images. The dataset used in this study includes pre-existing blood vessels and manually annotated peripapillary atrophy zones (alpha and beta) provided by expert ophthalmologists. This comprehensive dataset, integrating existing blood vessels and expert-marked peripapillary atrophy zones, fulfills the study's objectives. The effectiveness of the proposed approach is validated by training nine pre-trained models on the HRF dataset comprising 45 retinal images, successfully segmenting the optic disc, cup, blood vessels, and peripapillary atrophy zones (alpha and beta). The results demonstrate 87.7 % pixel accuracy, 87 % Intersection over Union (IoU), 86.9 % F1 Score, 85 % mean IoU (mIoU), and 15 % model loss, significantly contributing to the early diagnosis and monitoring of glaucoma and optic nerve disorders.

Clinical validation of artificial intelligence-based preoperative virtual reduction for Neer 3- or 4-part proximal humerus fractures

BackgroundIf reduction images of fractures can be provided in advance with artificial-intelligence (AI)-based technology, it can assist with preoperative surgical planning. Recently, we developed the AI-based preoperative virtual reduction model for orthopedic trauma, which can provide an automatic segmentation and reduction of fractured fragments. The purpose of this study was to validate a quality of reduction model of Neer 3- or 4-part proximal humerus fractures established by AI-based technology.MethodsTo develop the AI-based preoperative virtual reduction model, deep learning performed the segmentation of fracture fragments, and a Monte Carlo simulation completed the virtual reduction to determine the best model. A total of 20 pre/postoperative three-dimensional computed tomography (CT) scans of proximal humerus fracture were prepared. The preoperative CT scans were employed as the input of AI-based automated reduction (AI-R) to deduce the reduction models of fracture fragments, meanwhile, the manual reduction (MR) was conducted using the same CT images. Dice similarity coefficient (DSC) and intersection over union (IoU) between the reduction model from the AI-R/MR and postoperative CT scans were evaluated. Working times were compared between the two groups. Clinical validity agreement (CVA) and reduction quality score (RQS) were investigated for clinical validation outcomes by 20 orthopedic surgeons.ResultsThe mean DSC and IoU were better when using AI-R that when using MR (0.78 ± 0.13 vs. 0.69 ± 0.16, p < 0.001 and 0.65 ± 0.16 vs. 0.55 ± 0.18, p < 0.001, respectively). The working time of AI-R was, on average, 1.41% of that of MR. The mean CVA of all cases was 81%±14.7% (AI-R, 82.25%±14.27%; MR, 76.75%±14.17%, p = 0.06). The mean RQS was significantly higher when AI-R compared with MR was used (91.47 ± 1.12 vs. 89.30 ± 1.62, p = 0.045).ConclusionThe AI-based preoperative virtual reduction model showed good performance in the reduction model in proximal humerus fractures with faster working times. Beyond diagnosis, classification, and outcome prediction, the AI-based technology can change the paradigm of preoperative surgical planning in orthopedic surgery.Level of evidenceLevel IV.

Deep learning based approach: automated gingival inflammation grading model using gingival removal strategy

Gingival inflammation grade serves as a well-established index in periodontitis. The aim of this study was to develop a deep learning network utilizing a novel feature extraction method for the automatic assessment of gingival inflammation. T-distributed Stochastic Neighbor Embedding (t-SNE) was utilized for dimensionality reduction. A convolutional neural network (CNN) model based on DenseNet was developed for the identification and evaluation of gingival inflammation. To enhance the performance of the deep learning (DL) model, a novel teeth removal algorithm was implemented. Additionally, a Grad-CAM + + encoder was applied to generate heatmaps for computer visual attention analysis. The mean Intersection over Union (MIoU) for the identification of gingivitis was 0.727 ± 0.117. The accuracy rates for the five inflammatory degrees were 77.09%, 77.25%, 74.38%, 73.68% and 79.22%. The Area Under the Receiver Operating Characteristic (AUROC) values were 0.83, 0.80, 0.81, 0.81 and 0.84, respectively. The attention ratio towards gingival tissue increased from 37.73% to 62.20%, and within 8 mm of the gingival margin, it rose from 21.11% to 38.23%. On the gingiva, the overall attention ratio increased from 51.82% to 78.21%. The proposed DL model with novel feature extraction method provides high accuracy and sensitivity for identifying and grading gingival inflammation.

Accurate and Efficient Event-Based Semantic Segmentation Using Adaptive Spiking Encoder-Decoder Network.

Spiking neural networks (SNNs), known for their low-power, event-driven computation, and intrinsic temporal dynamics, are emerging as promising solutions for processing dynamic, asynchronous signals from event-based sensors. Despite their potential, SNNs face challenges in training and architectural design, resulting in limited performance in challenging event-based dense prediction tasks compared with artificial neural networks (ANNs). In this work, we develop an efficient spiking encoder-decoder network (SpikingEDN) for large-scale event-based semantic segmentation (EbSS) tasks. To enhance the learning efficiency from dynamic event streams, we harness the adaptive threshold which improves network accuracy, sparsity, and robustness in streaming inference. Moreover, we develop a dual-path spiking spatially adaptive modulation (SSAM) module, which is specifically tailored to enhance the representation of sparse events and multimodal inputs, thereby considerably improving network performance. Our SpikingEDN attains a mean intersection over union (MIoU) of 72.57% on the DDD17 dataset and 58.32% on the larger DSEC-Semantic dataset, showing competitive results to the state-of-the-art ANNs while requiring substantially fewer computational resources. Our results shed light on the untapped potential of SNNs in event-based vision applications. The source codes are publicly available at https://github.com/EMI-Group/spikingedn.

Application of improved Unet network in the recognition and segmentation of lung CT images in patients with pneumoconiosis

BackgroundPneumoconiosis 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.MethodsA 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.ResultsIn 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.ConclusionsThe 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.