Papers Interactive Segmentation
“Interactive Segmentation” 태그가 달린 논문 306편 · 필터 해제
ActiveFreq: Integrating Active Learning and Frequency Domain Analysis for Interactive Segmentation
Interactive segmentation is commonly used in medical image analysis to obtain precise, pixel-level labeling, typically involving iterative user input to correct mislabeled regions. However, existing approaches often fail…
Interactive SegmentationActive LearningBALD-SAM: Disagreement-based Active Prompting in Interactive Segmentation
The Segment Anything Model (SAM) has revolutionized interactive segmentation through spatial prompting. While existing work primarily focuses on automating prompts in various settings, real-world annotation workflows inv…
Interactive SegmentationActive LearningCLoPA: Continual Low Parameter Adaptation of Interactive Segmentation for Medical Image Annotation
Interactive segmentation enables clinicians to guide annotation, but existing zero-shot models like nnInteractive fail to consistently reach expert-level performance across diverse medical imaging tasks. Because annotati…
Interactive SegmentationPrefer-DAS: Learning from Local Preferences and Sparse Prompts for Domain Adaptive Segmentation of Electron Microscopy
Domain adaptive segmentation (DAS) is a promising paradigm for delineating intracellular structures from various large-scale electron microscopy (EM) without incurring extensive annotated data in each domain. However, th…
Unsupervised Domain AdaptationInteractive SegmentationContrastive LearningSegMoTE: Token-Level Mixture of Experts for Medical Image Segmentation
Medical image segmentation is vital for clinical diagnosis and quantitative analysis, yet remains challenging due to the heterogeneity of imaging modalities and the high cost of pixel-level annotations. Although general …
Medical Image SegmentationZero-shot GeneralizationInteractive SegmentationMedSAM-Agent: Empowering Interactive Medical Image Segmentation with Multi-turn Agentic Reinforcement Learning
Medical image segmentation is evolving from task-specific models toward generalizable frameworks. Recent research leverages Multi-modal Large Language Models (MLLMs) as autonomous agents, employing reinforcement learning…
Medical Image SegmentationInteractive SegmentationReinforcement LearningSAMTok: Representing Any Mask with Two Words
Pixel-wise capabilities are essential for building interactive intelligent systems. However, pixel-wise multi-modal LLMs (MLLMs) remain difficult to scale due to complex region-level encoders, specialized segmentation de…
Interactive SegmentationReinforcement LearningPSSI-MaxST: An Efficient Pixel-Segment Similarity Index Using Intensity and Smoothness Features for Maximum Spanning Tree Based Segmentation
Interactive graph-based segmentation methods partition an image into foreground and background regions with the aid of user inputs. However, existing approaches often suffer from high computational costs, sensitivity to …
Interactive SegmentationMedVL-SAM2: A unified 3D medical vision-language model for multimodal reasoning and prompt-driven segmentation
Recent progress in medical vision-language models (VLMs) has achieved strong performance on image-level text-centric tasks such as report generation and visual question answering (VQA). However, achieving fine-grained vi…
Visual Question AnsweringInteractive SegmentationMultimodal ReasoningSpatial ReasoningAtlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation
Accurate segmentation of anatomical structures in medical images is essential for diagnosis and treatment planning. While recent interactive segmentation foundation models enhance generalization through large-scale multi…
Medical Image SegmentationInteractive SegmentationVFM-ISRefiner: Towards Better Adapting Vision Foundation Models for Interactive Segmentation of Remote Sensing Images
Interactive image segmentation(IIS) plays a critical role in generating precise annotations for remote sensing imagery, where objects often exhibit scale variations, irregular boundaries and complex backgrounds. However,…
Interactive SegmentationInstance SegmentationImage SegmentationReSAM: Refine, Requery, and Reinforce: Self-Prompting Point-Supervised Segmentation for Remote Sensing Images
Interactive segmentation models such as the Segment Anything Model (SAM) have demonstrated remarkable generalization on natural images, but they perform suboptimally on remote sensing imagery (RSI) due to severe domain s…
Interactive SegmentationBackSplit: The Importance of Sub-dividing the Background in Biomedical Lesion Segmentation
Segmenting small lesions in medical images remains notoriously difficult. Most prior work tackles this challenge by either designing better architectures, loss functions, or data augmentation schemes; and collecting more…
Interactive SegmentationLesion SegmentationData AugmentationAdvancing Annotat3D with Harpia: A CUDA-Accelerated Library For Large-Scale Volumetric Data Segmentation
High-resolution volumetric imaging techniques, such as X-ray tomography and advanced microscopy, generate increasingly large datasets that challenge existing tools for efficient processing, segmentation, and interactive …
Interactive SegmentationMIQ-SAM3D: From Single-Point Prompt to Multi-Instance Segmentation via Competitive Query Refinement
Accurate segmentation of medical images is fundamental to tumor diagnosis and treatment planning. SAM-based interactive segmentation has gained attention for its strong generalization, but most methods follow a single-po…
Interactive SegmentationInstance SegmentationLesion SegmentationA methodology for clinically driven interactive segmentation evaluation
Interactive segmentation is a promising strategy for building robust, generalisable algorithms for volumetric medical image segmentation. However, inconsistent and clinically unrealistic evaluation hinders fair compariso…
Volumetric Medical Image SegmentationInteractive SegmentationDomain-Specialized Interactive Segmentation Framework for Meningioma Radiotherapy Planning
Precise delineation of meningiomas is crucial for effective radiotherapy (RT) planning, directly influencing treatment efficacy and preservation of adjacent healthy tissues. While automated deep learning approaches have …
Medical Image SegmentationInteractive SegmentationPinPoint3D: Fine-Grained 3D Part Segmentation from a Few Clicks
Fine-grained 3D part segmentation is crucial for enabling embodied AI systems to perform complex manipulation tasks, such as interacting with specific functional components of an object. However, existing interactive seg…
Interactive Segmentation3D Part SegmentationPoint CloudsK-Prism: A Knowledge-Guided and Prompt Integrated Universal Medical Image Segmentation Model
Medical image segmentation is fundamental to clinical decision-making, yet existing models remain fragmented. They are usually trained on single knowledge sources and specific to individual tasks, modalities, or organs. …
Medical Image SegmentationInteractive SegmentationPrompt-DAS: Annotation-Efficient Prompt Learning for Domain Adaptive Semantic Segmentation of Electron Microscopy Images
Domain adaptive segmentation (DAS) of numerous organelle instances from large-scale electron microscopy (EM) is a promising way to enable annotation-efficient learning. Inspired by SAM, we propose a promptable multitask …
Unsupervised Domain AdaptationInteractive SegmentationSemantic SegmentationContrastive Learning