# Extending SEEDS to a Supervoxel Algorithm for Medical Image Analysis

arXiv: https://arxiv.org/abs/2502.02409 · HTML: https://mlsanity.com/paper/2502.02409/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 4 Feb 2025.

## Builds on (most cited first, 5 of 5)

- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation](https://mlsanity.com/paper/2007.09886/) (2020)
- [SEEDS: Superpixels Extracted via Energy-Driven Sampling](https://mlsanity.com/paper/1309.3848/) (2013)
- [Learning Hierarchical Image Segmentation For Recognition and By Recognition](https://mlsanity.com/paper/2210.00314/) (2022)
- [Boundary-aware Supervoxel-level Iteratively Refined Interactive 3D Image Segmentation with Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2303.10692/) (2023)

## Built on by (most cited first, 2 of 2)

- [Decoder-Free Supervoxel GNN for Accurate Brain-Tumor Localization in Multi-Modal MRI](https://mlsanity.com/paper/2601.14055/) (2026)
- [Efficient KernelSHAP Explanations for Patch-based 3D Medical Image Segmentation](https://mlsanity.com/paper/2604.11775/) (2026)

## 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