# RAP-Net: Coarse-to-Fine Multi-Organ Segmentation with Single Random Anatomical Prior

arXiv: https://arxiv.org/abs/2012.12425 · HTML: https://mlsanity.com/paper/2012.12425/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2209.15076/) (2022)
- [DeformUX-Net: Exploring a 3D Foundation Backbone for Medical Image Segmentation with Depthwise Deformable Convolution](https://mlsanity.com/paper/2310.00199/) (2023)
- [Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation](https://mlsanity.com/paper/2106.01596/) (2021)
- [Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation](https://mlsanity.com/paper/2303.00232/) (2023)
- [Adaptive Contrastive Learning with Dynamic Correlation for Multi-Phase Organ Segmentation](https://mlsanity.com/paper/2210.08652/) (2022)
- [Multi-Contrast Computed Tomography Atlas of Healthy Pancreas](https://mlsanity.com/paper/2306.01853/) (2023)
- [Pseudo-Label Guided Multi-Contrast Generalization for Non-Contrast Organ-Aware Segmentation](https://mlsanity.com/paper/2205.05898/) (2022)
- [Spatially-Adaptive Gradient Re-parameterization for 3D Large Kernel Optimization](https://mlsanity.com/paper/2505.19603/) (2025)

## Abstract (arXiv)

Performing coarse-to-fine abdominal multi-organ segmentation facilitates to extract high-resolution segmentation minimizing the lost of spatial contextual information. However, current coarse-to-refine approaches require a significant number of models to perform single organ refine segmentation corresponding to the extracted organ region of interest (ROI). We propose a coarse-to-fine pipeline, which starts from the extraction of the global prior context of multiple organs from 3D volumes using a low-resolution coarse network, followed by a fine phase that uses a single refined model to segment all abdominal organs instead of multiple organ corresponding models. We combine the anatomical prior with corresponding extracted patches to preserve the anatomical locations and boundary information for performing high-resolution segmentation across all organs in a single model. To train and evaluate our method, a clinical research cohort consisting of 100 patient volumes with 13 organs well-annotated is used. We tested our algorithms with 4-fold cross-validation and computed the Dice score for evaluating the segmentation performance of the 13 organs. Our proposed method using single auto-context outperforms the state-of-the-art on 13 models with an average Dice score 84.58% versus 81.69% (p<0.0001).