Biomaterial Database

DCU-Net: Multi-scale U-Net for brain tumor segmentation. 2020

Tiejun Yang, and Yudan Zhou, and Lei Li, and Chunhua Zhu

Key Laboratory of Grain Information Processing and Control (Henan University of Technology), Ministry of Education, China.

Brain tumor segmentation plays an important role in assisting diagnosis of disease, treatment plan planning, and surgical navigation. This study aims to improve the accuracy of tumor boundary segmentation using the multi-scale U-Net network. In this study, a novel U-Net with dilated convolution (DCU-Net) structure is proposed for brain tumor segmentation based on the classic U-Net structure. First, the MR brain tumor images are pre-processed to alleviate the class imbalance problem by reducing the input of the background pixels. Then, the multi-scale spatial pyramid pooling is used to replace the max pooling at the end of the down-sampling path. It can expand the feature receptive field while maintaining image resolution. Finally, a dilated convolution residual block is combined to improve the skip connections in the training networks to improve the network's ability to recognize the tumor details. The proposed model has been evaluated using the Brain Tumor Segmentation (BRATS) 2018 Challenge training dataset and achieved the dice similarity coefficients (DSC) score of 0.91, 0.78 and 0.83 for whole tumor, core tumor and enhancing tumor segmentation, respectively. The experiment results indicate that the proposed model yields a promising performance in automated brain tumor segmentation.

UI	MeSH Term	Description	Entries
D007091	Image Processing, Computer-Assisted	A technique of inputting two-dimensional or three-dimensional images into a computer and then enhancing or analyzing the imagery into a form that is more useful to the human observer.	Biomedical Image Processing,Computer-Assisted Image Processing,Digital Image Processing,Image Analysis, Computer-Assisted,Image Reconstruction,Medical Image Processing,Analysis, Computer-Assisted Image,Computer-Assisted Image Analysis,Computer Assisted Image Analysis,Computer Assisted Image Processing,Computer-Assisted Image Analyses,Image Analyses, Computer-Assisted,Image Analysis, Computer Assisted,Image Processing, Biomedical,Image Processing, Computer Assisted,Image Processing, Digital,Image Processing, Medical,Image Processings, Medical,Image Reconstructions,Medical Image Processings,Processing, Biomedical Image,Processing, Digital Image,Processing, Medical Image,Processings, Digital Image,Processings, Medical Image,Reconstruction, Image,Reconstructions, Image
D001932	Brain Neoplasms	Neoplasms of the intracranial components of the central nervous system, including the cerebral hemispheres, basal ganglia, hypothalamus, thalamus, brain stem, and cerebellum. Brain neoplasms are subdivided into primary (originating from brain tissue) and secondary (i.e., metastatic) forms. Primary neoplasms are subdivided into benign and malignant forms. In general, brain tumors may also be classified by age of onset, histologic type, or presenting location in the brain.	Brain Cancer,Brain Metastases,Brain Tumors,Cancer of Brain,Malignant Primary Brain Tumors,Neoplasms, Intracranial,Benign Neoplasms, Brain,Brain Neoplasm, Primary,Brain Neoplasms, Benign,Brain Neoplasms, Malignant,Brain Neoplasms, Malignant, Primary,Brain Neoplasms, Primary Malignant,Brain Tumor, Primary,Brain Tumor, Recurrent,Cancer of the Brain,Intracranial Neoplasms,Malignant Neoplasms, Brain,Malignant Primary Brain Neoplasms,Neoplasms, Brain,Neoplasms, Brain, Benign,Neoplasms, Brain, Malignant,Neoplasms, Brain, Primary,Primary Brain Neoplasms,Primary Malignant Brain Neoplasms,Primary Malignant Brain Tumors,Benign Brain Neoplasm,Benign Brain Neoplasms,Benign Neoplasm, Brain,Brain Benign Neoplasm,Brain Benign Neoplasms,Brain Cancers,Brain Malignant Neoplasm,Brain Malignant Neoplasms,Brain Metastase,Brain Neoplasm,Brain Neoplasm, Benign,Brain Neoplasm, Malignant,Brain Neoplasms, Primary,Brain Tumor,Brain Tumors, Recurrent,Cancer, Brain,Intracranial Neoplasm,Malignant Brain Neoplasm,Malignant Brain Neoplasms,Malignant Neoplasm, Brain,Neoplasm, Brain,Neoplasm, Intracranial,Primary Brain Neoplasm,Primary Brain Tumor,Primary Brain Tumors,Recurrent Brain Tumor,Recurrent Brain Tumors,Tumor, Brain
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000465	Algorithms	A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.	Algorithm
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron