Volumetric Medical Image Segmentation
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Benchmarks
PROMISE 2012
Most implemented
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task
nnFormer: Interleaved Transformer for Volumetric Segmentation
Papers
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 Segmentation