mlsanityData to 14 Oct 2026

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

arXiv 2210.03189 ↗First posted 6 Oct 2022eess.IV cs.CVcited by 9

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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.

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

17 of 27 references.

References · 17

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  3. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  4. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
  5. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
  6. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  7. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  8. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,222
  9. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 959
  10. 2021Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysiscited by 422
  11. 2022AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentationcited by 351
  12. 2021Medical Transformer: Gated Axial-Attention for Medical Image Segmentationcited by 320
5 more
  1. 2021Focal Self-attention for Local-Global Interactions in Vision Transformerscited by 288
  2. 2021TransFuse: Fusing Transformers and CNNs for Medical Image Segmentationcited by 258
  3. 2019Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentationcited by 30
  4. 2021BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Imagescited by 17
  5. 2020HF-UNet: Learning Hierarchically Inter-Task Relevance in Multi-Task U-Net for Accurate Prostate Segmentationcited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 2; 2026: 3.

9 papers.

  1. 2023A Recent Survey of Vision Transformers for Medical Image Segmentationcited by 23
  2. 2023AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentationcited by 19
  3. 2024MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Trainingcited by 6
  4. 2025FluenceFormer: Transformer-Driven Multi-Beam Fluence Map Regression for Radiotherapy Planningcited by 4
  5. 2025BiPVL-Seg: Bidirectional Progressive Vision-Language Fusion with Global-Local Alignment for Medical Image Segmentationcited by 1
  6. 2026A Neighborhood Attention Transformer Network for Enhanced 3D Segmentation of the Left Anterior Descending Arterycited by 1
  7. 2024AI-based Automatic Segmentation of Prostate on Multi-modality Images: A Reviewcited by 0
  8. 2026EX-FIQA: Leveraging Intermediate Early eXit Representations from Vision Transformers for Face Image Quality Assessmentcited by 0
  9. 2026MedPlex: Deep Vision-Language Co-Adaptation for Clinically Grounded Medical Segmentationcited by 0