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 implementations

Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation

qdaiing/fedmema 18 Mar 2024

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.

1
18 Mar 2024

Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor Segmentation

yaziciz/GLIMS 15 Mar 2024

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).

1
15 Mar 2024

SEDNet: Shallow Encoder-Decoder Network for Brain Tumor Segmentation

chollette/SEDNet_Shallow-Encoder-Decoder-Network-for-Brain-Tumor-Segmentation 24 Jan 2024

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.

0
24 Jan 2024

Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment

nibabel/RLK-Unet Frontiers in Oncology 2024

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.

1
14 Jan 2024

Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty Prediction

wetothemoon/braintumorsegmentation 31 Dec 2023

Brain tumors are one of the deadliest forms of cancer with a mortality rate of over 80%.

0
31 Dec 2023

E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation

boqian333/e2enet-medical 7 Dec 2023

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.

9
07 Dec 2023

Hybrid-Fusion Transformer for Multisequence MRI

zinic95/HFTrans 2 Nov 2023

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.

0
02 Nov 2023

DSFNet: Dual-GCN and Location-fused Self-attention with Weighted Fast Normalized Fusion for Polyps Segmentation

juntongkuki/pytorch-dsfnet 15 Aug 2023

Polyps segmentation poses a significant challenge in medical imaging due to the flat surface of polyps and their texture similarity to surrounding tissues.

6
15 Aug 2023

Prototype-Driven and Multi-Expert Integrated Multi-Modal MR Brain Tumor Image Segmentation

linzy0227/pdminet 22 Jul 2023

To this end, a multi-modal MR brain tumor segmentation method with tumor prototype-driven and multi-expert integration is proposed.

4
22 Jul 2023

AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain Tumor

windstormer/ame-cam 26 Jun 2023

Magnetic resonance imaging (MRI) is commonly used for brain tumor segmentation, which is critical for patient evaluation and treatment planning.

17
26 Jun 2023