Brain Tumor Segmentation
122 papers with code • 9 benchmarks • 4 datasets
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 )
Libraries
Use these libraries to find Brain Tumor Segmentation models and implementationsLatest papers
Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation
In practice, it is not uncommon that some FL participants only possess a subset of the complete imaging modalities, posing inter-modal heterogeneity as a challenge to effectively training a global model on all participants' data.
Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor Segmentation
In our approach, we utilized a multi-scale, attention-guided and hybrid U-Net-shaped model -- GLIMS -- to perform 3D brain tumor segmentation in three regions: Enhancing Tumor (ET), Tumor Core (TC), and Whole Tumor (WT).
SEDNet: Shallow Encoder-Decoder Network for Brain Tumor Segmentation
Despite the advancement in computational modeling towards brain tumor segmentation, of which several models have been developed, it is evident from the computational complexity of existing models which are still at an all-time high, that performance and efficiency under clinical application scenarios are limited.
Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment
Methods and materials: A total of 128 patients with 1339 BMs, who underwent BM magnetic resonance imaging using the contrast-enhanced 3D T1 weighted (T1WI) turbo spin-echo black blood sequence, were included in the development of the DL algorithm.
Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty Prediction
Brain tumors are one of the deadliest forms of cancer with a mortality rate of over 80%.
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation
E2ENet achieves comparable accuracy on the large-scale challenge AMOS-CT, while saving over 68\% parameter count and 29\% FLOPs in the inference phase, compared with the previous best-performing method.
Hybrid-Fusion Transformer for Multisequence MRI
Medical segmentation has grown exponentially through the advent of a fully convolutional network (FCN), and we have now reached a turning point through the success of Transformer.
DSFNet: Dual-GCN and Location-fused Self-attention with Weighted Fast Normalized Fusion for Polyps Segmentation
Polyps segmentation poses a significant challenge in medical imaging due to the flat surface of polyps and their texture similarity to surrounding tissues.
Prototype-Driven and Multi-Expert Integrated Multi-Modal MR Brain Tumor Image Segmentation
To this end, a multi-modal MR brain tumor segmentation method with tumor prototype-driven and multi-expert integration is proposed.
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain Tumor
Magnetic resonance imaging (MRI) is commonly used for brain tumor segmentation, which is critical for patient evaluation and treatment planning.