# Small Organ Segmentation in Whole-body MRI using a Two-stage FCN and Weighting Schemes

arXiv: https://arxiv.org/abs/1807.11368 · HTML: https://mlsanity.com/paper/1807.11368/

## Key facts

- Cited by 2 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 30 Jul 2018.

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

- [Analyzing Overfitting under Class Imbalance in Neural Networks for Image Segmentation](https://mlsanity.com/paper/2102.10365/) (2021)
- [Context Label Learning: Improving Background Class Representations in Semantic Segmentation](https://mlsanity.com/paper/2212.08423/) (2022)

## Abstract (arXiv)

Accurate and robust segmentation of small organs in whole-body MRI is difficult due to anatomical variation and class imbalance. Recent deep network based approaches have demonstrated promising performance on abdominal multi-organ segmentations. However, the performance on small organs is still suboptimal as these occupy only small regions of the whole-body volumes with unclear boundaries and variable shapes. A coarse-to-fine, hierarchical strategy is a common approach to alleviate this problem, however, this might miss useful contextual information. We propose a two-stage approach with weighting schemes based on auto-context and spatial atlas priors. Our experiments show that the proposed approach can boost the segmentation accuracy of multiple small organs in whole-body MRI scans.