Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. Accurate segmentation is required for volume determination, 3D rendering, radiation therapy, and su...Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. Accurate segmentation is required for volume determination, 3D rendering, radiation therapy, and surgery planning. In medical images, segmentation has traditionally been done by human experts. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore, automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. Many methods have been proposed to detect and segment 2D shapes, most of which involve template matching. Advanced segmentation techniques called Snakes or active contours have been used, considering deformable models or templates. The main purpose of this work is to apply segmentation techniques for the definition of 3D organs (anatomical structures) when big data information has been stored and must be organized by the doctors for medical diagnosis. The processes would be implemented in the CT images from patients with COVID-19.展开更多
Rice leaf diseases have an important impact on modern farming,threatening crop health and yield.Accurate semantic segmentation techniques are crucial for segmenting diseased leaf parts and assisting farmers in disease...Rice leaf diseases have an important impact on modern farming,threatening crop health and yield.Accurate semantic segmentation techniques are crucial for segmenting diseased leaf parts and assisting farmers in disease identification.However,the diversity of rice growing environments and the complexity of leaf diseases pose challenges.To address these issues,this study introduces an innovative semantic segmentation algorithm for rice leaf pests and diseases based on the Transformer architecture AISOA-SSformer.First,it features the sparse global-update perceptron for real-time parameter updating,enhancing model stability and accuracy in learning irregular leaf features.Second,the salient feature attention mechanism is introduced to separate and reorganize features using the spatial reconstruction module(SRM)and channel reconstruction module(CRM),focusing on salient feature extraction and reducing background interference.Additionally,the annealing-integrated sparrow optimization algorithm fine-tunes the sparrow algorithm,gradually reducing the stochastic search amplitude to minimize loss.This enhances the model's adaptability and robustness,particularly against fuzzy edge features.The experimental results show that AISOA-SSformer achieves an 83.1%MIoU,an 80.3%Dice coefficient,and a 76.5%recall on a homemade dataset,with a model size of only 14.71 million parameters.Compared with other popular algorithms,it demonstrates greater accuracy in rice leaf disease segmentation.This method effectively improves segmentation,providing valuable insights for modern plantation management.The data and code used in this study will be open sourced at .展开更多
【正】INTRODUCTION Anterior ciliary arteries provide 70%of the vascular supply of the anterior segment.A significant interruption of the vascular flow of these arteries increases the risk for anterior ischemia.Althoug...【正】INTRODUCTION Anterior ciliary arteries provide 70%of the vascular supply of the anterior segment.A significant interruption of the vascular flow of these arteries increases the risk for anterior ischemia.Although the frequency of this special condition is low after strabismus surgery(1:13 000)[1],its effects may involve substantial visual problems[2].We report the successful outcome of a new surgical approach for strabismus management in a case of high risk for anterior ischemia.Specifically,we show the correction of the horizontal ocular deviation by means of an adjustable muscle展开更多
We present an automatic kidney segmentation method using ultrasound images.This method employs a coarse-to-fine approach to tackle the challenge of unclear and fuzzy boundaries.Four key innovations are introduced to e...We present an automatic kidney segmentation method using ultrasound images.This method employs a coarse-to-fine approach to tackle the challenge of unclear and fuzzy boundaries.Four key innovations are introduced to enhance the segmentation process’s accuracy and efficiency.First,an automatic deep fusion training network serves as a coarse segmentation strategy.Second,we propose an explainable mathematical mapping formula to better represent the kidney contour.Third,by utilizing the characteristics of the principal curve,a neural network automatically refines curve shapes,thus reducing model errors.Finally,we employ an intelligent searching polyline segment method for automatic kidney contour segmentation.The results show that our method achieves high accuracy and stability in segmenting kidney ultrasound images.This work’s contributions include the deep fusion training network,intelligent searching polyline segment method,and explainable mathematical mapping formula,which are applicable to other medical image segmentation tasks.Additionally,this approach uses a mean-shift clustering model,supplanting standard projection and vertex optimization steps.展开更多
文摘Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. Accurate segmentation is required for volume determination, 3D rendering, radiation therapy, and surgery planning. In medical images, segmentation has traditionally been done by human experts. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore, automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. Many methods have been proposed to detect and segment 2D shapes, most of which involve template matching. Advanced segmentation techniques called Snakes or active contours have been used, considering deformable models or templates. The main purpose of this work is to apply segmentation techniques for the definition of 3D organs (anatomical structures) when big data information has been stored and must be organized by the doctors for medical diagnosis. The processes would be implemented in the CT images from patients with COVID-19.
基金supported by the Changsha Municipal Natural Science Foundation(grant no.kq2014160)in part by the National Natural Science Foundation in China(grant no.61703441)+2 种基金in part by the Key Projects of the Department of Education,Hunan Province(grant no.19A511)in part by the Hunan Key Laboratory of Intelligent Logistics Technology(grant no.2019TP1015)in part by the National Natural Science Foundation of China(grant no.61902436).
文摘Rice leaf diseases have an important impact on modern farming,threatening crop health and yield.Accurate semantic segmentation techniques are crucial for segmenting diseased leaf parts and assisting farmers in disease identification.However,the diversity of rice growing environments and the complexity of leaf diseases pose challenges.To address these issues,this study introduces an innovative semantic segmentation algorithm for rice leaf pests and diseases based on the Transformer architecture AISOA-SSformer.First,it features the sparse global-update perceptron for real-time parameter updating,enhancing model stability and accuracy in learning irregular leaf features.Second,the salient feature attention mechanism is introduced to separate and reorganize features using the spatial reconstruction module(SRM)and channel reconstruction module(CRM),focusing on salient feature extraction and reducing background interference.Additionally,the annealing-integrated sparrow optimization algorithm fine-tunes the sparrow algorithm,gradually reducing the stochastic search amplitude to minimize loss.This enhances the model's adaptability and robustness,particularly against fuzzy edge features.The experimental results show that AISOA-SSformer achieves an 83.1%MIoU,an 80.3%Dice coefficient,and a 76.5%recall on a homemade dataset,with a model size of only 14.71 million parameters.Compared with other popular algorithms,it demonstrates greater accuracy in rice leaf disease segmentation.This method effectively improves segmentation,providing valuable insights for modern plantation management.The data and code used in this study will be open sourced at .
文摘【正】INTRODUCTION Anterior ciliary arteries provide 70%of the vascular supply of the anterior segment.A significant interruption of the vascular flow of these arteries increases the risk for anterior ischemia.Although the frequency of this special condition is low after strabismus surgery(1:13 000)[1],its effects may involve substantial visual problems[2].We report the successful outcome of a new surgical approach for strabismus management in a case of high risk for anterior ischemia.Specifically,we show the correction of the horizontal ocular deviation by means of an adjustable muscle
基金supported by the China Postdoctoral Science Foundation(No.2023M742568)the China Social Development Plan of Taizhou(No.TN202110)。
文摘We present an automatic kidney segmentation method using ultrasound images.This method employs a coarse-to-fine approach to tackle the challenge of unclear and fuzzy boundaries.Four key innovations are introduced to enhance the segmentation process’s accuracy and efficiency.First,an automatic deep fusion training network serves as a coarse segmentation strategy.Second,we propose an explainable mathematical mapping formula to better represent the kidney contour.Third,by utilizing the characteristics of the principal curve,a neural network automatically refines curve shapes,thus reducing model errors.Finally,we employ an intelligent searching polyline segment method for automatic kidney contour segmentation.The results show that our method achieves high accuracy and stability in segmenting kidney ultrasound images.This work’s contributions include the deep fusion training network,intelligent searching polyline segment method,and explainable mathematical mapping formula,which are applicable to other medical image segmentation tasks.Additionally,this approach uses a mean-shift clustering model,supplanting standard projection and vertex optimization steps.
