# An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation

arXiv: https://arxiv.org/abs/2208.07417 · HTML: https://mlsanity.com/paper/2208.07417/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 18 references that are in the map.
- First posted on arXiv 15 Aug 2022.

## Builds on (most cited first, 14 of 14)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [Deep learning for cardiac image segmentation: A review](https://mlsanity.com/paper/1911.03723/) (2019)
- [Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation](https://mlsanity.com/paper/2111.01236/) (2021)
- [MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation](https://mlsanity.com/paper/2105.07451/) (2021)
- [Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation](https://mlsanity.com/paper/2005.09120/) (2020)
- [OpenKBP: The open-access knowledge-based planning grand challenge](https://mlsanity.com/paper/2011.14076/) (2020)
- [GMSRF-Net: An improved generalizability with global multi-scale residual fusion network for polyp segmentation](https://mlsanity.com/paper/2111.10614/) (2021)
- [Enhancing Organ at Risk Segmentation with Improved Deep Neural Networks](https://mlsanity.com/paper/2202.01866/) (2022)

## Built on by (most cited first, 4 of 4)

- [Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation](https://mlsanity.com/paper/2303.00232/) (2023)
- [Leveraging Unlabeled Data for 3D Medical Image Segmentation through Self-Supervised Contrastive Learning](https://mlsanity.com/paper/2311.12617/) (2023)
- [Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation](https://mlsanity.com/paper/2210.16704/) (2022)
- [Selecting the Best Optimizers for Deep Learning based Medical Image Segmentation](https://mlsanity.com/paper/2302.02289/) (2023)

## Abstract (arXiv)

Accurate segmentation of organs-at-risks (OARs) is a precursor for optimizing radiation therapy planning. Existing deep learning-based multi-scale fusion architectures have demonstrated a tremendous capacity for 2D medical image segmentation. The key to their success is aggregating global context and maintaining high resolution representations. However, when translated into 3D segmentation problems, existing multi-scale fusion architectures might underperform due to their heavy computation overhead and substantial data diet. To address this issue, we propose a new OAR segmentation framework, called OARFocalFuseNet, which fuses multi-scale features and employs focal modulation for capturing global-local context across multiple scales. Each resolution stream is enriched with features from different resolution scales, and multi-scale information is aggregated to model diverse contextual ranges. As a result, feature representations are further boosted. The comprehensive comparisons in our experimental setup with OAR segmentation as well as multi-organ segmentation show that our proposed OARFocalFuseNet outperforms the recent state-of-the-art methods on publicly available OpenKBP datasets and Synapse multi-organ segmentation. Both of the proposed methods (3D-MSF and OARFocalFuseNet) showed promising performance in terms of standard evaluation metrics. Our best performing method (OARFocalFuseNet) obtained a dice coefficient of 0.7995 and hausdorff distance of 5.1435 on OpenKBP datasets and dice coefficient of 0.8137 on Synapse multi-organ segmentation dataset.