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Medical Image Segmentation

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하위 태스크: 31
평가표: 49
보존된 평가 기록: 332

Medical Image Segmentation is a computer vision task that involves dividing an medical image into multiple segments, where each segment represents a different object or structure of interest in the image. The goal of medical image segmentation is to provide a precise and accurate representation of the objects of interest within the image, typically for the purpose of diagnosis, treatment planning, and quantitative analysis.

<span style="color:grey; opacity: 0.6">( Image credit: IVD-Net )</span>

코드

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

https://github.com/clguo/SA-UNet

공식 코드

SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation

https://github.com/clinical-and-translational-imaging-lab/brats-e1d3

공식 코드

E1D3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 Challenge

https://github.com/clinicaldatascience/tucker-cnn

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Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition

https://github.com/cmzuo11/dcca

공식 코드

Deep Learning in Single-Cell Analysis

https://github.com/cocofeat/devis

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Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty

https://github.com/cocofeat/tbrats

공식 코드

TBraTS: Trusted Brain Tumor Segmentation

https://github.com/cocofeat/umis

공식 코드

Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty

https://github.com/computational-cell-analytics/medico-sam

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MedicoSAM: Towards foundation models for medical image segmentation

https://github.com/conscienceli/IterNet

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IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks

https://github.com/cosmoiknoslab/nulite

공식 코드

NuLite -- Lightweight and Fast Model for Nuclei Instance Segmentation and Classification

https://github.com/cpystan/psm

공식 코드

Exploring Unsupervised Cell Recognition with Prior Self-activation Maps

https://github.com/crazorback/aadg

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AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation

https://github.com/cscyqj/miccai23-protocontra-sfda

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Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning

https://github.com/cshwhale/Med3D

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Med3D: Transfer Learning for 3D Medical Image Analysis

https://github.com/csyfjiang/mlla-unet

공식 코드

MLLA-UNet: Mamba-like Linear Attention in an Efficient U-Shape Model for Medical Image Segmentation

https://github.com/cucl-2/dc-seg

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DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing Modalities

https://github.com/cuhk-aim-group/diffrect

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DiffRect: Latent Diffusion Label Rectification for Semi-supervised Medical Image Segmentation

https://github.com/curli-quan/slide-sam

공식 코드

Slide-SAM: Medical SAM Meets Sliding Window

https://github.com/cuthbert-huang/cpcanet

공식 코드

Channel prior convolutional attention for medical image segmentation

https://github.com/cuttle-fish-my/gm-guided-dg

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Gradient-Map-Guided Adaptive Domain Generalization for Cross Modality MRI Segmentation

https://github.com/cvblab/anomaly_localization_vae_gcams

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Looking at the whole picture: constrained unsupervised anomaly segmentation

https://github.com/cviu-csu/hrdecoder

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HRDecoder: High-Resolution Decoder Network for Fundus Image Lesion Segmentation

https://github.com/cviu-csu/m2mrf-lesion-segmentation

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M2MRF: Many-to-Many Reassembly of Features for Tiny Lesion Segmentation in Fundus Images

https://github.com/cviviers/prob_3d_segmentation

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Probabilistic 3D segmentation for aleatoric uncertainty quantification in full 3D medical data

https://github.com/cvlab-dresden/DSAC

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DSAC - Differentiable RANSAC for Camera Localization

https://github.com/cvlab-stonybrook/SelfMedMAE

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Self Pre-training with Masked Autoencoders for Medical Image Classification and Segmentation

https://github.com/cvlab-stonybrook/tokensparse-for-medseg

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Token Sparsification for Faster Medical Image Segmentation

https://github.com/cwwang1979/CW-nnU-Net-for-PET-CT

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Dual channel CW nnU-Net for 3D PET-CT Lesion Segmentation in 2024 autoPET III Challenge

https://github.com/cxh-research/smaformer

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SMAFormer: Synergistic Multi-Attention Transformer for Medical Image Segmentation

https://github.com/cyb08/mambaclinix-pytorch

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MambaClinix: Hierarchical Gated Convolution and Mamba-Based U-Net for Enhanced 3D Medical Image Segmentation

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

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

https://github.com/cys1102/fba-net

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FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation

https://github.com/dafne-imaging/dafne

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Deep Anatomical Federated Network (Dafne): An open client-server framework for the continuous, collaborative improvement of deep learning-based medical image segmentation

https://github.com/dair-iitd/guided-prompting-sam

공식 코드

Guided Prompting in SAM for Weakly Supervised Cell Segmentation in Histopathological Images

https://github.com/daniyanaj/dwinformer

공식 코드

Medical Image Segmentation Using Directional Window Attention

https://github.com/david-stojanovski/echo_from_noise

공식 코드

Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation

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