Extending SEEDS to a Supervoxel Algorithm for Medical Image Analysis
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Abstract (arXiv)
In this work, we extend the SEEDS superpixel algorithm from 2D images to 3D volumes, resulting in 3D SEEDS, a faster, better, and open-source supervoxel algorithm for medical image analysis. We compare 3D SEEDS with the widely used supervoxel algorithm SLIC on 13 segmentation tasks across 10 organs. 3D SEEDS accelerates supervoxel generation by a factor of 10, improves the achievable Dice score by +6.5%, and reduces the under-segmentation error by -0.16%. The code is available at https://github.com/Zch0414/3d_seeds
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 5 of its 15 references that are in the map. First posted on arXiv 4 Feb 2025.
Builds on
5 of 15 references.
References · 5
- 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 507
- 2020Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotationcited by 128
- 2013SEEDS: Superpixels Extracted via Energy-Driven Samplingcited by 75
- 2022Learning Hierarchical Image Segmentation For Recognition and By Recognitioncited by 16
- 2023Boundary-aware Supervoxel-level Iteratively Refined Interactive 3D Image Segmentation with Multi-agent Reinforcement Learningcited by 13
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Citing papers per year: 2026: 2.
2 papers.