Tissue Segmentation in Nasopharyngeal CT Images Using Two-Stage Learning

Tissue segmentation is a fundamental and important task in nasopharyngeal images analysis.However,it is a challenging task to accurately and quickly segment various tissues in the nasopharynx region due to the small difference in gray value between tissues in the nasopharyngeal image and the complexity of the tissue structure.In this paper,we propose a novel tissue segmentation approach based on a two-stage learning framework and U-Net.In the proposed methodology,the network consists of two segmentation modules.The first module performs rough segmentation and the second module performs accurate segmentation.Considering the training time and the limitation of computing resources,the structure of the second module is simpler and the number of network layers is less.In addition,our segmentation module is based on U-Net and incorporates a skip structure,which can make full use of the original features of the data and avoid feature loss.We evaluated our proposed method on the nasopharyngeal dataset provided by West China Hospital of Sichuan University.The experimental results show that the proposed method is superior to many standard segmentation structures and the recently proposed nasopharyngeal tissue segmentation method,and can be easily generalized across different tissue types in various organs.

ICA-Unet:An improved U-net network for brown adipose tissue segmentation

Brown adipose tissue(BAT)is a kind of adipose tissue engaging in thermoregulatory thermogenesis,metaboloregulatory thermogenesis,and secretory.Current studies have revealed that BAT activity is negatively correlated with adult body weight and is considered a target tissue for the treatment of obesity and other metabolic-related diseases.Additionally,the activity of BAT presents certain differences between different ages and genders.Clinically,BAT segmentation based on PET/CT data is a reliable method for brown fat research.However,most of the current BAT segmentation methods rely on the experience of doctors.In this paper,an improved U-net network,ICA-Unet,is proposed to achieve automatic and precise segmentation of BAT.First,the traditional 2D convolution layer in the encoder is replaced with a depth-wise overparameterized convolutional(Do-Conv)layer.Second,the channel attention block is introduced between the double-layer convolution.Finally,the image information entropy(IIE)block is added in the skip connections to strengthen the edge features.Furthermore,the performance of this method is evaluated on the dataset of PET/CT images from 368 patients.The results demonstrate a strong agreement between the automatic segmentation of BAT and manual annotation by experts.The average DICE coeffcient(DSC)is 0.9057,and the average Hausdorff distance is 7.2810.Experimental results suggest that the method proposed in this paper can achieve effcient and accurate automatic BAT segmentation and satisfy the clinical requirements of BAT.

Improved neurological diagnoses and treatment strategies via automated human brain tissue segmentation from clinical magnetic resonance imaging

Objective Segmentation of medical images is a crucial process in various image analysis applications.Automated segmentation methods excel in accuracy when compared to manual segmentation in the context of medical image analysis.One of the essential phases in the quantitative analysis of the brain is automated brain tissue segmentation using clinically obtained magnetic resonance imaging(MRI)data.It allows for precise quantitative examination of the brain,which aids in diagnosis,identification,and classification of disorders.Consequently,the efficacy of the segmentation approach is crucial to disease diagnosis and treatment planning.Methods This study presented a hybrid optimization method for segmenting brain tissue in clinical MRI scans us-ing a fractional Henry horse herd gas optimization-based Shepard convolutional neural network(FrHHGO-based ShCNN).To segment the clinical brain MRI images into white matter(WM),grey matter(GM),and cerebrospinal fluid(CSF)tissues,the proposed framework was evaluated on the Lifespan Human Connectome Projects(HCP)database.The hybrid optimization algorithm,FrHHGO,integrates the fractional Henry gas optimization(FHGO)and horse herd optimization(HHO)algorithms.Training required 30 min,whereas testing and segmentation of brain tissues from an unseen image required an average of 12 s.Results Compared to the results obtained with no refinements,the Skull stripping refinement showed significant improvement.As the method included a preprocessing stage,it was flexible enough to enhance image quality,allowing for better results even with low-resolution input.Maximum precision of 93.2%,recall of 91.5%,Dice score of 91.1%,and F1-score of 90.5% were achieved using the proposed FrHHGO-based ShCNN,which was superior to all other approaches.