mlsanityData to 7 Oct 2026

Neuro-TransUNet: Segmentation of stroke lesion in MRI using transformers

arXiv 2406.06017 ↗First posted 10 Jun 2024eess.IV cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Accurate segmentation of the stroke lesions using magnetic resonance imaging (MRI) is associated with difficulties due to the complicated anatomy of the brain and the different properties of the lesions. This study introduces the Neuro-TransUNet framework, which synergizes the U-Net's spatial feature extraction with SwinUNETR's global contextual processing ability, further enhanced by advanced feature fusion and segmentation synthesis techniques. The comprehensive data pre-processing pipeline improves the framework's efficiency, which involves resampling, bias correction, and data standardization, enhancing data quality and consistency. Ablation studies confirm the significant impact of the advanced integration of U-Net with SwinUNETR and data pre-processing pipelines on performance and demonstrate the model's effectiveness. The proposed Neuro-TransUNet model, trained with the ATLAS v2.0 training dataset, outperforms existing deep learning algorithms and establishes a new benchmark in stroke lesion segmentation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 43 references that are in the map. First posted on arXiv 10 Jun 2024.

Builds on

16 of 43 references.

References · 16

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  3. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  4. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
  5. 2021Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysiscited by 418
  6. 2017Deep Learning for Medical Image Analysiscited by 399
  7. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  8. 2020Deep Learning Based Brain Tumor Segmentation: A Surveycited by 30
  9. 2022MAPPING: Model Average with Post-processing for Stroke Lesion Segmentationcited by 9
  10. 2019Automatic Post-Stroke Lesion Segmentation on MR Images using 3D Residual Convolutional Neural Networkcited by 7
  11. 2023Large-kernel Attention for Efficient and Robust Brain Lesion Segmentationcited by 5
  12. 2023Improving Segmentation of Objects with Varying Sizes in Biomedical Images using Instance-wise and Center-of-Instance Segmentation Loss Functioncited by 4
4 more
  1. 2023Meta-Analysis of Transfer Learning for Segmentation of Brain Lesionscited by 3
  2. 2023ARHNet: Adaptive Region Harmonization for Lesion-aware Augmentation to Improve Segmentation Performancecited by 1
  3. 2023BeSt-LeS: Benchmarking Stroke Lesion Segmentation using Deep Supervisioncited by 1
  4. 2023Fuzzy Information Seeded Region Growing for Automated Lesions After Stroke Segmentation in MR Brain Imagescited by 1

Built on by

Not cited yet.