Papers Volumetric Medical Image Segmentation
“Volumetric Medical Image Segmentation” 태그가 달린 논문 67편 · 필터 해제
Towards Voxel Spacing Consistency for Medical Image Segmentation
Volumetric medical image segmentation is essential for both preoperative diagnosis and intraoperative guidance. While recent years have witnessed rapid progress in segmentation architectures, comparatively little attenti…
Volumetric Medical Image SegmentationMAE-Based Self-Supervised Pretraining for Data-Efficient Medical Image Segmentation Using nnFormer
Transformer architectures, including nnFormer,have demonstrated promising results in volumetric medical image segmentation by being able to capture long-range spatial interactions. Although they have high performance, th…
Volumetric Medical Image SegmentationSelf-Supervised LearningAutomatic Segmentation of 3D CT scans with SAM2 using a zero-shot approach
Foundation models for image segmentation have shown strong generalization in natural images, yet their applicability to 3D medical imaging remains limited. In this work, we study the zero-shot use of Segment Anything Mod…
Volumetric Medical Image SegmentationCoordinative Learning with Ordinal and Relational Priors for Volumetric Medical Image Segmentation
Volumetric medical image segmentation presents unique challenges due to the inherent anatomical structure and limited availability of annotations. While recent methods have shown promise by contrasting spatial relationsh…
Volumetric Medical Image SegmentationVoxTell: Free-Text Promptable Universal 3D Medical Image Segmentation
We introduce VoxTell, a vision-language model for text-prompted volumetric medical image segmentation. It maps free-form descriptions, from single words to full clinical sentences, to 3D masks. Trained on 62K+ CT, MRI, a…
Volumetric Medical Image SegmentationBARL: Bilateral Alignment in Representation and Label Spaces for Semi-Supervised Volumetric Medical Image Segmentation
Semi-supervised medical image segmentation (SSMIS) seeks to match fully supervised performance while sharply reducing annotation cost. Mainstream SSMIS methods rely on \emph{label-space consistency}, yet they overlook th…
Semi-supervised Medical Image SegmentationVolumetric Medical Image 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 SegmentationAdapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning
Medical vision foundation models remain limited in downstream tasks, particularly volumetric medical image segmentation. While fine-tuning on labeled target-domain data improves performance, existing approaches typically…
Volumetric Medical Image SegmentationActive LearningUniversal Wavelet Units in 3D Retinal Layer Segmentation
This paper presents the first study to apply tunable wavelet units (UwUs) for 3D retinal layer segmentation from Optical Coherence Tomography (OCT) volumes. To overcome the limitations of conventional max-pooling, we int…
Volumetric Medical Image SegmentationSafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus
Foundation models for volumetric medical image segmentation have emerged as powerful tools in clinical workflows, enabling radiologists to delineate regions of interest through intuitive clicks. While these models demons…
Image SegmentationInteractive SegmentationMedical Image SegmentationSegmentation+2TextBraTS: Text-Guided Volumetric Brain Tumor Segmentation with Innovative Dataset Development and Fusion Module Exploration
Deep learning has demonstrated remarkable success in medical image segmentation and computer-aided diagnosis. In particular, numerous advanced methods have achieved state-of-the-art performance in brain tumor segmentatio…
Brain Tumor SegmentationImage SegmentationMedical Image SegmentationSegmentation+3SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation
Recent advances in medical imaging have established deep learning-based segmentation as the predominant approach, though it typically requires large amounts of manually annotated data. However, obtaining annotations for …
Image SegmentationMedical Image SegmentationSegmentationSemantic Segmentation+1Zero-Shot Gaze-based Volumetric Medical Image Segmentation
Accurate segmentation of anatomical structures in volumetric medical images is crucial for clinical applications, including disease monitoring and cancer treatment planning. Contemporary interactive segmentation models, …
Image SegmentationInteractive SegmentationMedical Image SegmentationSegmentation+2RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2
Segment Anything Model 2 (SAM 2), a prompt-driven foundation model extending SAM to both image and video domains, has shown superior zero-shot performance compared to its predecessor. Building on SAM's success in medical…
Image SegmentationMedical Image SegmentationSemantic SegmentationVolumetric Medical Image SegmentationMemorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer
Segment Anything Models (SAMs) have gained increasing attention in medical image analysis due to their zero-shot generalization capability in segmenting objects of unseen classes and domains when provided with appropriat…
Image SegmentationMedical Image AnalysisMedical Image SegmentationSemantic Segmentation+2HResFormer: Hybrid Residual Transformer for Volumetric Medical Image Segmentation
Vision Transformer shows great superiority in medical image segmentation due to the ability in learning long-range dependency. For medical image segmentation from 3D data, such as computed tomography (CT), existing metho…
AnatomyComputed Tomography (CT)Image SegmentationMedical Image Segmentation+3SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image Segmentation
Computed Tomography (CT) is one of the most popular modalities for medical imaging. By far, CT images have contributed to the largest publicly available datasets for volumetric medical segmentation tasks, covering full-b…
Computed Tomography (CT)Image SegmentationLesion DetectionMedical Image Segmentation+3Improving 3D Medical Image Segmentation at Boundary Regions using Local Self-attention and Global Volume Mixing
Volumetric medical image segmentation is a fundamental problem in medical image analysis where the objective is to accurately classify a given 3D volumetric medical image with voxel-level precision. In this work, we prop…
Image SegmentationInstance SegmentationMedical Image AnalysisMedical Image Segmentation+4Y-CA-Net: A Convolutional Attention Based Network for Volumetric Medical Image Segmentation
Recent attention-based volumetric segmentation (VS) methods have achieved remarkable performance in the medical domain which focuses on modeling long-range dependencies. However, for voxel-wise prediction tasks, discrimi…
Image SegmentationMedical Image SegmentationOrgan SegmentationSegmentation+2FastSAM-3DSlicer: A 3D-Slicer Extension for 3D Volumetric Segment Anything Model with Uncertainty Quantification
Accurate segmentation of anatomical structures and pathological regions in medical images is crucial for diagnosis, treatment planning, and disease monitoring. While the Segment Anything Model (SAM) and its variants have…
CPUDomain AdaptationGPUImage Segmentation+7