Copertina

Segment anything model (SAM) for brain extraction in fMRI studies

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Chenna, Dwith, Bhogawar, Suyash
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Image and Video Processing Computer Vision and Pattern Recognition
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

4D iterative reconstruction of brain fMRI in the moving fetus
di: Taymourtash, Athena, et al.
Pubblicazione: (2021)

Whole-brain Transferable Representations from Large-Scale fMRI Data Improve Task-Evoked Brain Activity Decoding
di: Peng, Yueh-Po, et al.
Pubblicazione: (2025)

NT-ViT: Neural Transcoding Vision Transformers for EEG-to-fMRI Synthesis
di: Lanzino, Romeo, et al.
Pubblicazione: (2024)

TV-based Deep 3D Self Super-Resolution for fMRI
di: Pérez-Bueno, Fernando, et al.
Pubblicazione: (2024)

Generative AI Enables Structural Brain Network Construction from fMRI via Symmetric Diffusion Learning
di: Zuo, Qiankun, et al.
Pubblicazione: (2023)

On generalisability of segment anything model for nuclear instance segmentation in histology images
di: Xu, Kesi, et al.
Pubblicazione: (2024)

Segmentation of the cortical plate in fetal brain MRI with a topological loss
di: de Dumast, Priscille, et al.
Pubblicazione: (2020)

Is SAM 2 Better than SAM in Medical Image Segmentation?
di: Sengupta, Sourya, et al.
Pubblicazione: (2024)

PA-SAM: Prompt Adapter SAM for High-Quality Image Segmentation
di: Xie, Zhaozhi, et al.
Pubblicazione: (2024)

Brain-Grasp: Graph-based Saliency Priors for Improved fMRI-based Visual Brain Decoding
di: Moradi, Mohammad, et al.
Pubblicazione: (2026)

NeuroClips: Towards High-fidelity and Smooth fMRI-to-Video Reconstruction
di: Gong, Zixuan, et al.
Pubblicazione: (2024)

GBT-SAM: A Parameter-Efficient Depth-Aware Model for Generalizable Brain tumour Segmentation on mp-MRI
di: Diana-Albelda, Cecilia, et al.
Pubblicazione: (2025)

Ultrasound SAM Adapter: Adapting SAM for Breast Lesion Segmentation in Ultrasound Images
di: Tu, Zhengzheng, et al.
Pubblicazione: (2024)

De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation
di: Xu, Qing, et al.
Pubblicazione: (2024)

PathSeqSAM: Sequential Modeling for Pathology Image Segmentation with SAM2
di: Zhu, Mingyang, et al.
Pubblicazione: (2025)

Multi-Stage Graph Learning for fMRI Analysis to Diagnose Neuro-Developmental Disorders
di: Gao, Wenjing, et al.
Pubblicazione: (2024)

SynBrain: Enhancing Visual-to-fMRI Synthesis via Probabilistic Representation Learning
di: Mai, Weijian, et al.
Pubblicazione: (2025)

Efficient Multi Subject Visual Reconstruction from fMRI Using Aligned Representations
di: Zangos, Christos, et al.
Pubblicazione: (2025)

Comparing SAM 2 and SAM 3 for Zero-Shot Segmentation of 3D Medical Data
di: Chakrabarty, Satrajit, et al.
Pubblicazione: (2025)

SAM2-3dMed: Empowering SAM2 for 3D Medical Image Segmentation
di: Yang, Yeqing, et al.
Pubblicazione: (2025)

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

CRC-SAM: SAM-Based Multi-Modal Segmentation and Quantification of Colorectal Cancer in CT, Colonoscopy, and Histology Images
di: Lao, Daniel
Pubblicazione: (2026)

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

SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection
di: Kollias, Dimitrios, et al.
Pubblicazione: (2024)

Mitigating analytical variability in fMRI results with style transfer
di: Germani, Elodie, et al.
Pubblicazione: (2024)

FreqSelect: Frequency-Aware fMRI-to-Image Reconstruction
di: Ye, Junliang, et al.
Pubblicazione: (2025)

DB-SAM: Delving into High Quality Universal Medical Image Segmentation
di: Qin, Chao, et al.
Pubblicazione: (2024)

UN-SAM: Universal Prompt-Free Segmentation for Generalized Nuclei Images
di: Chen, Zhen, et al.
Pubblicazione: (2024)

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

Spec2VolCAMU-Net: A Spectrogram-to-Volume Model for EEG-to-fMRI Reconstruction based on Multi-directional Time-Frequency Convolutional Attention Encoder and Vision-Mamba U-Net
di: He, Dongyi, et al.
Pubblicazione: (2025)

TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI
di: D'Antonoli, Tugba Akinci, et al.
Pubblicazione: (2024)

Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation
di: Na, Saiyang, et al.
Pubblicazione: (2024)

SAMRI: Segment Any MRI
di: Wang, Zhao, et al.
Pubblicazione: (2025)

UniUltra: Interactive Parameter-Efficient SAM2 for Universal Ultrasound Segmentation
di: Li, Yue, et al.
Pubblicazione: (2025)

Multi-modal brain MRI synthesis based on SwinUNETR
di: Pang, Haowen, et al.
Pubblicazione: (2025)

Assessing Foundational Medical 'Segment Anything' (Med-SAM1, Med-SAM2) Deep Learning Models for Left Atrial Segmentation in 3D LGE MRI
di: Mehrnia, Mehri, et al.
Pubblicazione: (2024)

GCAN: Generative Counterfactual Attention-guided Network for Explainable Cognitive Decline Diagnostics based on fMRI Functional Connectivity
di: Shen, Xiongri, et al.
Pubblicazione: (2024)

3D Wasserstein generative adversarial network with dense U-Net based discriminator for preclinical fMRI denoising
di: Soltanpour, Sima, et al.
Pubblicazione: (2024)

HA-HI: Synergising fMRI and DTI through Hierarchical Alignments and Hierarchical Interactions for Mild Cognitive Impairment Diagnosis
di: Shen, Xiongri, et al.
Pubblicazione: (2024)

An automated framework for brain vessel centerline extraction from CTA images
di: Liu, Sijie, et al.
Pubblicazione: (2024)