Cover Image

Segment Anything Model for Brain Tumor Segmentation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Peng, Wang, Yaping
Format:	Preprint
Published:	2023
Subjects:	Image and Video Processing Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Mask-Enhanced Segment Anything Model for Tumor Lesion Semantic Segmentation
by: Shi, Hairong, et al.
Published: (2024)

ASPS: Augmented Segment Anything Model for Polyp Segmentation
by: Li, Huiqian, et al.
Published: (2024)

Degradation-Aware Blur-Segmentation of Brain Tumor
by: Wang, Yuchun, et al.
Published: (2026)

Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions
by: Zhang, Yichi, et al.
Published: (2024)

Segment Anything for Histopathology
by: Griebel, Titus, et al.
Published: (2025)

Autoadaptive Medical Segment Anything Model
by: Ward, Tyler, et al.
Published: (2025)

Segment Anything in Medical Images
by: Ma, Jun, et al.
Published: (2023)

DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation
by: Gao, Yifan, et al.
Published: (2023)

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)
by: Li, Hongwei Bran, et al.
Published: (2023)

Deshadow-Anything: When Segment Anything Model Meets Zero-shot shadow removal
by: Zhang, Xiao Feng, et al.
Published: (2023)

Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging
by: Capellán-Martín, Daniel, et al.
Published: (2024)

An Ensemble Approach for Brain Tumor Segmentation and Synthesis
by: Rivera, Juampablo E. Heras, et al.
Published: (2024)

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective
by: Shao, Minye, et al.
Published: (2025)

Zero-Shot Surgical Tool Segmentation in Monocular Video Using Segment Anything Model 2
by: Lou, Ange, et al.
Published: (2024)

Adapting Segment Anything Model 3 for Concept-Driven Lesion Segmentation in Medical Images: An Experimental Study
by: Xu, Guoping, et al.
Published: (2026)

BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification
by: Fateh, Amirreza, et al.
Published: (2025)

Unsupervised Domain Adaptation for Pediatric Brain Tumor Segmentation
by: Fu, Jingru, et al.
Published: (2024)

A New Logic For Pediatric Brain Tumor Segmentation
by: Bengtsson, Max, et al.
Published: (2024)

Exploiting the Segment Anything Model (SAM) for Lung Segmentation in Chest X-ray Images
by: de Carvalho, Gabriel Bellon, et al.
Published: (2024)

Fed-MUnet: Multi-modal Federated Unet for Brain Tumor Segmentation
by: Zhou, Ruojun, et al.
Published: (2024)

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models
by: Cui, Qifei, et al.
Published: (2025)

nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance
by: Li, Yunxiang, et al.
Published: (2023)

SEDNet: Shallow Encoder-Decoder Network for Brain Tumor Segmentation
by: Olisah, Chollette C., et al.
Published: (2024)

Application of Segment Anything Model for Civil Infrastructure Defect Assessment
by: Ahmadi, Mohsen, et al.
Published: (2023)

Anatomical Consistency Distillation and Inconsistency Synthesis for Brain Tumor Segmentation with Missing Modalities
by: Zhang, Zheyu, et al.
Published: (2024)

Segment Anything in Medical Images and Videos: Benchmark and Deployment
by: Ma, Jun, et al.
Published: (2024)

Specialized Change Detection using Segment Anything
by: Ahmad, Tahir, et al.
Published: (2024)

AMGFormer: Adaptive Multi-Granular Transformer for Brain Tumor Segmentation with Missing Modalities
by: Guo, Chengxiang, et al.
Published: (2026)

Task-oriented Uncertainty Collaborative Learning for Label-Efficient Brain Tumor Segmentation
by: Zhang, Zhenxuan, et al.
Published: (2025)

Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings
by: Fadugba, Jeremiah, et al.
Published: (2025)

Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learning
by: Behzadpour, Majid, et al.
Published: (2025)

SAM3D: Segment Anything Model in Volumetric Medical Images
by: Bui, Nhat-Tan, et al.
Published: (2023)

Selective Complementary Feature Fusion and Modal Feature Compression Interaction for Brain Tumor Segmentation
by: Chen, Dong, et al.
Published: (2025)

MCICSAM: Monte Carlo-guided Interpolation Consistency Segment Anything Model for Semi-Supervised Prostate Zone Segmentation
by: Huang, Guantian, et al.
Published: (2024)

Graph-Based Multi-Modal Light-weight Network for Adaptive Brain Tumor Segmentation
by: Huo, Guohao, et al.
Published: (2025)

Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor Segmentation
by: Jiang, Zhifan, et al.
Published: (2024)

Frequency-Aware Ensemble Learning for BraTS 2025 Pediatric Brain Tumor Segmentation
by: Yi, Yuxiao, et al.
Published: (2025)

Efficient MedSAMs: Segment Anything in Medical Images on Laptop
by: Ma, Jun, et al.
Published: (2024)

SAM3D: Zero-Shot Semi-Automatic Segmentation in 3D Medical Images with the Segment Anything Model
by: Chan, Trevor J., et al.
Published: (2024)

Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal Embedding
by: Huynh, Tuan-Luc, et al.
Published: (2025)