# UESA-Net: U-Shaped Embedded Multidirectional Shrinkage Attention Network for Ultrasound Nodule Segmentation

arXiv: https://arxiv.org/abs/2509.22763 · HTML: https://mlsanity.com/paper/2509.22763/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 9 of 9)

- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation](https://mlsanity.com/paper/1611.06612/) (2016)
- [MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation](https://mlsanity.com/paper/1902.04049/) (2019)
- [3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study](https://mlsanity.com/paper/1612.03925/) (2016)
- [Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation](https://mlsanity.com/paper/2107.04914/) (2021)
- [DeepMRSeg: A convolutional deep neural network for anatomy and abnormality segmentation on MR images](https://mlsanity.com/paper/1907.02110/) (2019)
- [InfiNet: Fully Convolutional Networks for Infant Brain MRI Segmentation](https://mlsanity.com/paper/1810.05735/) (2018)
- [Multi-Texture GAN: Exploring the Multi-Scale Texture Translation for Brain MR Images](https://mlsanity.com/paper/2102.07225/) (2021)
- [DSU-net: Dense SegU-net for automatic head-and-neck tumor segmentation in MR images](https://mlsanity.com/paper/2006.06278/) (2020)

## Abstract (arXiv)

Background: Breast and thyroid cancers pose an increasing public-health burden. Ultrasound imaging is a cost-effective, real-time modality for lesion detection and segmentation, yet suffers from speckle noise, overlapping structures, and weak global-local feature interactions. Existing networks struggle to reconcile high-level semantics with low-level spatial details. We aim to develop a segmentation framework that bridges the semantic gap between global context and local detail in noisy ultrasound images. Methods: We propose UESA-Net, a U-shaped network with multidirectional shrinkage attention. The encoder-decoder architecture captures long-range dependencies and fine-grained structures of lesions. Within each encoding block, attention modules operate along horizontal, vertical, and depth directions to exploit spatial details, while a shrinkage (threshold) strategy integrates prior knowledge and local features. The decoder mirrors the encoder but applies a pairwise shrinkage mechanism, combining prior low-level physical cues with corresponding encoder features to enhance context modeling. Results: On two public datasets - TN3K (3493 images) and BUSI (780 images) - UESA-Net achieved state-of-the-art performance with intersection-over-union (IoU) scores of 0.8487 and 0.6495, respectively. Conclusions: UESA-Net effectively aggregates multidirectional spatial information and prior knowledge to improve robustness and accuracy in breast and thyroid ultrasound segmentation, demonstrating superior performance to existing methods on multiple benchmarks.