Papers with Code 아카이브

Brain Segmentation

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하위 태스크: 0
평가표: 1
보존된 평가 기록: 2

<span style="color:grey; opacity: 0.6">( Image credit: 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://bitbucket.org/bchradiology/u-net

공식 코드

Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning

https://github.com/AfifaKhaled/Two-Independent-Teachers-are-Better-Role-Model

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Two Independent Teachers are Better Role Model

https://github.com/BBillot/SynthSeg

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A Learning Strategy for Contrast-agnostic MRI Segmentation

https://github.com/DLTK/models

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NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines

https://github.com/Deep-MI/FastSurfer

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FastSurfer -- A fast and accurate deep learning based neuroimaging pipeline

https://github.com/HiLab-git/UGIR

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Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices

https://github.com/JianghaoWu/FPL-UDA

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CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation

https://github.com/MASILab/SLANTbrainSeg

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Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data

https://github.com/NIC-VICOROB/tissue_segmentation_comparison

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Quantitative analysis of patch-based fully convolutional neural networks for tissue segmentation on brain magnetic resonance imaging

https://github.com/NYUMedML/DARTS

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DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation

https://github.com/WCHN/CA_MTV-preproc

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MRI Super-Resolution using Multi-Channel Total Variation

https://github.com/WCHN/Label-Training

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Factorisation-based Image Labelling

https://github.com/aaroncao06/MedSegMamba

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MedSegMamba: 3D CNN-Mamba Hybrid Architecture for Brain Segmentation

https://github.com/abhi4ssj/QuickNATv2

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QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy

https://github.com/ai-med/squeeze_and_excitation

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`Project & Excite' Modules for Segmentation of Volumetric Medical Scans

https://github.com/arnab39/FewShot_GAN-Unet3D

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Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

https://github.com/bchimagine/real_time_fetal_brain_segmentation

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Deep Learning Framework for Real-time Fetal Brain Segmentation in MRI

https://github.com/brudfors/spm_superres

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MRI Super-Resolution using Multi-Channel Total Variation

https://github.com/cyndwith/fMRI-SAM

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

https://github.com/erashed/SubForkNet

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End-to-end semantic segmentation of personalized deep brain structures for non-invasive brain stimulation

https://github.com/freesurfer/freesurfer/tree/dev/samseg

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An Open-Source Tool for Longitudinal Whole-Brain and White Matter Lesion Segmentation

https://github.com/gift-surg/MONAIfbs

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MONAIfbs: MONAI-based fetal brain MRI deep learning segmentation

https://github.com/hilab-git/upl-sfda

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UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation

https://github.com/himashi92/lofihippseg

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Bilateral Hippocampi Segmentation in Low Field MRIs Using Mutual Feature Learning via Dual-Views

https://github.com/imran-maia/ibsr_18_bratseg_deep_learning

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Comparative Study of Probabilistic Atlas and Deep Learning Approaches for Automatic Brain Tissue Segmentation from MRI Using N4 Bias Field Correction and Anisotropic Diffusion Pre-processing Techniques

https://github.com/jeremy-chopin/apacosi

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Model-based inexact graph matching on top of CNNs for semantic scene understanding

https://github.com/josedolz/3D-F-CNN-BrainStruct

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3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study

https://github.com/josedolz/HyperDenseNet

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HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation

https://github.com/josedolz/LiviaNET

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3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study

https://github.com/kaiseem/dar-unet

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CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation

https://github.com/liamchalcroft/contrast-squared

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Unified 3D MRI Representations via Sequence-Invariant Contrastive Learning

https://github.com/lkshrsch/multiaxial_brain_segmenter

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Full-Head Segmentation of MRI with Abnormal Brain Anatomy: Model and Data Release

https://github.com/looooongChen/MRBrainS-Brain-Segmentation

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MixNet: Multi-modality Mix Network for Brain Segmentation

https://github.com/lucasfidon/feta-inference

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Partial supervision for the FeTA challenge 2021

https://github.com/luxneuroimage/dbsegment

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DBSegment: Fast and robust segmentation of deep brain structures -- Evaluation of transportability across acquisition domains

https://github.com/mafortin/GOUHFI

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GOUHFI: a novel contrast- and resolution-agnostic segmentation tool for Ultra-High Field MRI

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