# FocalUNETR: A Focal Transformer for Boundary-aware Segmentation of CT Images

arXiv: https://arxiv.org/abs/2210.03189 · HTML: https://mlsanity.com/paper/2210.03189/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 27 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 17 of 17)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [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)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021)
- [AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation](https://mlsanity.com/paper/2206.08023/) (2022)
- [Medical Transformer: Gated Axial-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2102.10662/) (2021)
- [Focal Self-attention for Local-Global Interactions in Vision Transformers](https://mlsanity.com/paper/2107.00641/) (2021)
- [TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation](https://mlsanity.com/paper/2102.08005/) (2021)
- [Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation](https://mlsanity.com/paper/1902.04099/) (2019)
- [BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images](https://mlsanity.com/paper/2107.04823/) (2021)
- [HF-UNet: Learning Hierarchically Inter-Task Relevance in Multi-Task U-Net for Accurate Prostate Segmentation](https://mlsanity.com/paper/2005.10439/) (2020)

## Built on by (most cited first, 9 of 9)

- [A Recent Survey of Vision Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2312.00634/) (2023)
- [AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation](https://mlsanity.com/paper/2308.14936/) (2023)
- [MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training](https://mlsanity.com/paper/2411.15576/) (2024)
- [FluenceFormer: Transformer-Driven Multi-Beam Fluence Map Regression for Radiotherapy Planning](https://mlsanity.com/paper/2512.22425/) (2025)
- [BiPVL-Seg: Bidirectional Progressive Vision-Language Fusion with Global-Local Alignment for Medical Image Segmentation](https://mlsanity.com/paper/2503.23534/) (2025)
- [A Neighborhood Attention Transformer Network for Enhanced 3D Segmentation of the Left Anterior Descending Artery](https://mlsanity.com/paper/2608.12274/) (2026)
- [AI-based Automatic Segmentation of Prostate on Multi-modality Images: A Review](https://mlsanity.com/paper/2407.06612/) (2024)
- [EX-FIQA: Leveraging Intermediate Early eXit Representations from Vision Transformers for Face Image Quality Assessment](https://mlsanity.com/paper/2604.22842/) (2026)
- [MedPlex: Deep Vision-Language Co-Adaptation for Clinically Grounded Medical Segmentation](https://mlsanity.com/paper/2608.13690/) (2026)

## Abstract (arXiv)

Computed Tomography (CT) based precise prostate segmentation for treatment planning is challenging due to (1) the unclear boundary of the prostate derived from CT's poor soft tissue contrast and (2) the limitation of convolutional neural network-based models in capturing long-range global context. Here we propose a novel focal transformer-based image segmentation architecture to effectively and efficiently extract local visual features and global context from CT images. Additionally, we design an auxiliary boundary-induced label regression task coupled with the main prostate segmentation task to address the unclear boundary issue in CT images. We demonstrate that this design significantly improves the quality of the CT-based prostate segmentation task over other competing methods, resulting in substantially improved performance, i.e., higher Dice Similarity Coefficient, lower Hausdorff Distance, and Average Symmetric Surface Distance, on both private and public CT image datasets. Our code is available at this hrefhttps://github.com/ChengyinLee/FocalUNETR.gitlink.