Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to vari...Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.展开更多
Globally,liver cancer ranks as the sixth most frequent malignancy cancer.The importance of early detection is undeniable,as liver cancer is the fifth most common disease in men and the ninth most common cancer in wome...Globally,liver cancer ranks as the sixth most frequent malignancy cancer.The importance of early detection is undeniable,as liver cancer is the fifth most common disease in men and the ninth most common cancer in women.Recent advances in imaging,biomarker discovery,and genetic profiling have greatly enhanced the ability to diagnose liver cancer.Early identification is vital since liver cancer is often asymptomatic,making diagnosis difficult.Imaging techniques such as Magnetic Resonance Imaging(MRI),Computed Tomography(CT),and ultrasonography can be used to identify liver cancer once a sample of liver tissue is taken.In recent research,reliable detection of liver cancer with minimal computing computational complexity and time has remained a serious difficulty.This paper employs the DenseNet model to enhance the detection of liver nodules with tumors by segmenting them using UNet and VGG using Fastai(UVF)in CT images.Its dense interconnections distinguish the DenseNet between layers.These dense connections facilitate the propagation of gradients and the flow of information throughout the network,thereby enhancing the efficacy and performance of training.DenseNet’s architecture combines dense blocks,bottleneck layers,and transition layers,allowing it to achieve a compromise between expressiveness and computing efficiency.Finally,the 3D liver nodular models were created using a raycasting volume rendering approach.Compared to other state-of-the-art deep neural networks,it is suitable for clinical applications to assist doctors in diagnosing liver cancer.The proposed approach was tested on a 3Dircadb dataset.According to experiments,UVF segmentation on the 3Dircadb dataset is 97.9%accurate.According to the study,the DenseNet and UVF segment liver cancer better than prior methods.The system proposes automated 3D liver cancer tumor visualization.展开更多
文摘Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.
文摘Globally,liver cancer ranks as the sixth most frequent malignancy cancer.The importance of early detection is undeniable,as liver cancer is the fifth most common disease in men and the ninth most common cancer in women.Recent advances in imaging,biomarker discovery,and genetic profiling have greatly enhanced the ability to diagnose liver cancer.Early identification is vital since liver cancer is often asymptomatic,making diagnosis difficult.Imaging techniques such as Magnetic Resonance Imaging(MRI),Computed Tomography(CT),and ultrasonography can be used to identify liver cancer once a sample of liver tissue is taken.In recent research,reliable detection of liver cancer with minimal computing computational complexity and time has remained a serious difficulty.This paper employs the DenseNet model to enhance the detection of liver nodules with tumors by segmenting them using UNet and VGG using Fastai(UVF)in CT images.Its dense interconnections distinguish the DenseNet between layers.These dense connections facilitate the propagation of gradients and the flow of information throughout the network,thereby enhancing the efficacy and performance of training.DenseNet’s architecture combines dense blocks,bottleneck layers,and transition layers,allowing it to achieve a compromise between expressiveness and computing efficiency.Finally,the 3D liver nodular models were created using a raycasting volume rendering approach.Compared to other state-of-the-art deep neural networks,it is suitable for clinical applications to assist doctors in diagnosing liver cancer.The proposed approach was tested on a 3Dircadb dataset.According to experiments,UVF segmentation on the 3Dircadb dataset is 97.9%accurate.According to the study,the DenseNet and UVF segment liver cancer better than prior methods.The system proposes automated 3D liver cancer tumor visualization.
