mlsanityData to 7 Oct 2026

Ultrasound Image Segmentation of Thyroid Nodule via Latent Semantic Feature Co-Registration

arXiv 2310.09221 ↗First posted 13 Oct 2023eess.IV cs.CVcited by 0

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Abstract (arXiv)

Segmentation of nodules in thyroid ultrasound imaging plays a crucial role in the detection and treatment of thyroid cancer. However, owing to the diversity of scanner vendors and imaging protocols in different hospitals, the automatic segmentation model, which has already demonstrated expert-level accuracy in the field of medical image segmentation, finds its accuracy reduced as the result of its weak generalization performance when being applied in clinically realistic environments. To address this issue, the present paper proposes ASTN, a framework for thyroid nodule segmentation achieved through a new type co-registration network. By extracting latent semantic information from the atlas and target images and utilizing in-depth features to accomplish the co-registration of nodules in thyroid ultrasound images, this framework can ensure the integrity of anatomical structure and reduce the impact on segmentation as the result of overall differences in image caused by different devices. In addition, this paper also provides an atlas selection algorithm to mitigate the difficulty of co-registration. As shown by the evaluation results collected from the datasets of different devices, thanks to the method we proposed, the model generalization has been greatly improved while maintaining a high level of segmentation accuracy.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 41 references that are in the map. First posted on arXiv 13 Oct 2023.

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References · 18

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
  3. 2015Spatial Transformer Networkscited by 1,423
  4. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 762
  5. 2018VoxelMorph: A Learning Framework for Deformable Medical Image Registrationcited by 657
  6. 2015Learning Deconvolution Network for Semantic Segmentationcited by 365
  7. 2019FFA-Net: Feature Fusion Attention Network for Single Image Dehazingcited by 307
  8. 2020Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentationcited by 134
  9. 2018Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registrationcited by 90
  10. 2018Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptationcited by 45
  11. 2022Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image Segmentationcited by 44
  12. 2022TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentationcited by 43
6 more
  1. 2020BANet: Bidirectional Aggregation Network with Occlusion Handling for Panoptic Segmentationcited by 25
  2. 2020Atlas-ISTN: Joint Segmentation, Registration and Atlas Construction with Image-and-Spatial Transformer Networkscited by 25
  3. 2022Aladdin: Joint Atlas Building and Diffeomorphic Registration Learning with Pairwise Alignmentcited by 18
  4. 2022Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusioncited by 8
  5. 2023Boosting multiple sclerosis lesion segmentation through attention mechanismcited by 8
  6. 2020Cross-Modality Multi-Atlas Segmentation Using Deep Neural Networkscited by 6

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