TY  - EJOU
AU  - Haribabu, Kaku 
AU  - R, Prasath 
AU  - IR, Praveen Joe 

TI  - MLRT-UNet: An Efficient Multi-Level Relation Transformer Based U-Net for Thyroid Nodule Segmentation
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 143
IS  - 1
SN  - 1526-1506

AB  - Thyroid nodules, a common disorder in the endocrine system, require accurate segmentation in ultrasound images for effective diagnosis and treatment. However, achieving precise segmentation remains a challenge due to various factors, including scattering noise, low contrast, and limited resolution in ultrasound images. Although existing segmentation models have made progress, they still suffer from several limitations, such as high error rates, low generalizability, overfitting, limited feature learning capability, etc. To address these challenges, this paper proposes a Multi-level Relation Transformer-based U-Net (MLRT-UNet) to improve thyroid nodule segmentation. The MLRT-UNet leverages a novel Relation Transformer, which processes images at multiple scales, overcoming the limitations of traditional encoding methods. This transformer integrates both local and global features effectively through self-attention and cross-attention units, capturing intricate relationships within the data. The approach also introduces a Co-operative Transformer Fusion (CTF) module to combine multi-scale features from different encoding layers, enhancing the model’s ability to capture complex patterns in the data. Furthermore, the Relation Transformer block enhances long-distance dependencies during the decoding process, improving segmentation accuracy. Experimental results show that the MLRT-UNet achieves high segmentation accuracy, reaching 98.2% on the Digital Database Thyroid Image (DDT) dataset, 97.8% on the Thyroid Nodule 3493 (TG3K) dataset, and 98.2% on the Thyroid Nodule3K (TN3K) dataset. These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation, addressing the limitations of existing models.
KW  - Thyroid nodules; endocrine system; multi-level relation transformer; U-Net; self-attention; external attention; co-operative transformer fusion; thyroid nodules segmentation

DO  - 10.32604/cmes.2025.059406