Anatomical landmark detection on PBA-Unet++

Yang Zhang; Tianbiao Luo; Jiahao Wang; Honglin Xiang

doi:10.1117/12.3040514

10 September 2024 Anatomical landmark detection on PBA-Unet++

Yang Zhang, Tianbiao Luo, Jiahao Wang, Honglin Xiang

Author Affiliations +

Proceedings Volume 13257, International Conference on Advanced Image Processing Technology (AIPT 2024); 1325718 (2024) https://doi.org/10.1117/12.3040514
Event: International Conference on Advanced Image Processing Technology (AIPT 2024), 2024, Chongqing, China

Abstract

The detection of anatomical landmarks in medical images is a widely studied topic, with a plethora of novel deep neural networks emerging in the field of medical imaging. However, due to an excessive focus on training with a single dataset, most deep neural networks fall short in terms of generalization capability. U-Net has become a popular backbone for image segmentation tasks, but it may have limited generalization when dealing with complex medical images. In this work, to overcome some of the shortcomings of U-Net, we propose an improved deep neural network model, called Position Based Attention-Unet⁺⁺ (PBA-Unet⁺⁺). We have integrated learnable positional encodings based on CBAM into Unet⁺⁺ to better capture image features at different scales. Our new model successfully addresses the issue of insufficient generalization capability in existing methods and accurately detects target landmarks in medical images. We conducted extensive experiments and comparisons on a dataset containing head X-ray images of ISBI 2015. The experimental results demonstrate that our model performs excellently.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Yang Zhang, Tianbiao Luo, Jiahao Wang, and Honglin Xiang "Anatomical landmark detection on PBA-Unet++", Proc. SPIE 13257, International Conference on Advanced Image Processing Technology (AIPT 2024), 1325718 (10 September 2024); https://doi.org/10.1117/12.3040514

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KEYWORDS

Anatomy

Medical imaging

Performance modeling

Data modeling

Neural networks

Education and training

Image segmentation

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