Recent Advances in Medical Imaging Segmentation: A Survey

arXiv:2505.09274 · cs.CV · Submitted 2025-05-14 · Read on arXiv

cs.CV

Submitted: 2025-05-14

Updated: 2026-09-26

Terminology

• Medical Image Segmentation — 47× in this paper
• Foundation Model — 30× in this paper · explained in 3 episodes
• Foundation Models — 26× in this paper · explained in 7 episodes
• Adaptation — 21× in this paper · explained in 1 episode
• Generative Model — 17× in this paper · explained in 2 episodes
• Generative Models — 17× in this paper · explained in 2 episodes
• Transformer — 17× in this paper · explained in 2 episodes
• Embeddings — 14× in this paper · explained in 2 episodes
• Fine-Tuning — 14× in this paper · explained in 5 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Deep Learning — 13× in this paper
• Consistency — 11× in this paper · explained in 3 episodes
• Diffusion Model — 11× in this paper · explained in 8 episodes
• Generalization — 10× in this paper · explained in 13 episodes
• Machine Learning — 10× in this paper
• Diffusion Models — 9× in this paper · explained in 6 episodes
• Segmentation Masks — 9× in this paper · explained in 2 episodes
• Few-Shot Learning — 8× in this paper · explained in 4 episodes
• Loss Function — 8× in this paper · explained in 2 episodes
• Domain Shift — 6× in this paper · explained in 4 episodes
• Pseudo-labels — 6× in this paper · explained in 2 episodes
• Semi-supervised Learning — 6× in this paper · explained in 4 episodes
• Data Augmentation — 5× in this paper · explained in 7 episodes
• Generative AI — 5× in this paper · explained in 2 episodes
• Self-Supervised Learning — 5× in this paper · explained in 7 episodes
• Generalizability — 4× in this paper · explained in 6 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Adaptability — 3× in this paper · explained in 1 episode
• Adversarial Training — 3× in this paper · explained in 1 episode
• Attention Mechanism — 3× in this paper · explained in 2 episodes
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Domain Adaptation — 3× in this paper · explained in 5 episodes
• Fractal Synthetic Module — 3× in this paper
• In-Context Learning — 3× in this paper · explained in 4 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Standardized Benchmarks — 3× in this paper · explained in 2 episodes
• Adversarial Model — 2× in this paper · explained in 1 episode
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Few-shot Semantic Segmentation — 2× in this paper
• Hypernetwork — 2× in this paper · explained in 2 episodes
• Normalizing Flow — 2× in this paper · explained in 3 episodes
• Normalizing Flows — 2× in this paper · explained in 4 episodes
• Objective Function — 2× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning — 2× in this paper · explained in 2 episodes
• Transferability — 2× in this paper · explained in 4 episodes
• U-net architecture — 2× in this paper · explained in 3 episodes
• Active Learning — 1× in this paper · explained in 6 episodes
• Adaptive Local Prototype Pooling — 1× in this paper
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Federated Learning — 1× in this paper · explained in 14 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Hybrid Models — 1× in this paper · explained in 2 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Mask Generation — 1× in this paper · explained in 2 episodes
• Masked Autoencoder (MAE) — 1× in this paper · explained in 5 episodes
• Parameter-Efficient Fine-Tuning (PEFT) — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Segment Anything Model (SAM) — 1× in this paper · explained in 2 episodes
• Text Affine Layer — 1× in this paper
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Using Masked Average Pooling — 1× in this paper
• Variational Bayes — 1× in this paper · explained in 2 episodes
• nnU-Net — 1× in this paper · explained in 2 episodes

Sources

• Score-Based Generative Modeling through Stochastic Differential Equations
• REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening
• The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification
• Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
• GPT-4 Technical Report
• LLaMA: Open and Efficient Foundation Language Models
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Customized Segment Anything Model for Medical Image Segmentation
• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
• SAM-Med2D
• 3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation
• Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
• 2018 Robotic Scene Segmentation Challenge
• 2017 Robotic Instrument Segmentation Challenge
• Can SAM Segment Polyps?
• SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation
• AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder

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