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Brain Tumor Segmentation

Brain Tumor Segmentation is a medical image analysis task that involves the separation of brain tumors from normal brain tissue in magnetic resonance imaging (MRI) scans. The goal of brain tumor segmentation is to produce a binary or multi-class segmentation map that accurately reflects the location and extent of the tumor.

( Image credit: Brain Tumor Segmentation with Deep Neural Networks )

Papers

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Showing 401–410 of 436 papers

TitleStatusHype
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation—0
Lesion Focused Super-ResolutionCode1
Deep Recurrent Level Set for Segmenting Brain Tumors—0
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation—0
Glioma Segmentation with Cascaded UnetCode0
Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images—0
Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation—0
Focus, Segment and Erase: An Efficient Network for Multi-Label Brain Tumor Segmentation—0
FR-MRInet: A Deep Convolutional Encoder-Decoder for Brain Tumor Segmentation with Relu-RGB and Sliding-windowCode0
Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival PredictionCode0
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