Detecting landmarks in anatomical medical images using transformer-based networks

Akhil Kasturi; Ali Vosoughi; Nathan Hadjiyski; Larry Stockmaster; William J. Sehnert; Axel Wismüller

doi:10.1117/12.2678538

28 September 2023 Detecting landmarks in anatomical medical images using transformer-based networks

Akhil Kasturi, Ali Vosoughi, Nathan Hadjiyski, Larry Stockmaster, William J. Sehnert, Axel Wismüller

Proceedings Volume 12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; 1265508 (2023) https://doi.org/10.1117/12.2678538
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States

Abstract

Landmark detection is critical in medical imaging for accurate diagnosis and treatment of diseases. While there are many automated methods for landmark detection, the potential of transformers in this area has not been fully explored. This work proposes a transformer-based network for accurate anatomical landmark detection in chest x-ray images. By leveraging the combined power of transformers and U-Net, our method effectively captures global context and spatial dependencies, leading to robust landmark localization. The proposed method outperforms state-of-the-art methods on Chest x-ray datasets, reducing mean radial error from 5.57 to 4.68 pixels. The experiments show that the transformer-based method can effectively learn complex spatial patterns in medical images. The results of this method show the potential to improve the precision and efficiency of tasks such as surgical planning and detecting abnormalities in medical images.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Akhil Kasturi, Ali Vosoughi, Nathan Hadjiyski, Larry Stockmaster, William J. Sehnert, and Axel Wismüller "Detecting landmarks in anatomical medical images using transformer-based networks", Proc. SPIE 12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, 1265508 (28 September 2023); https://doi.org/10.1117/12.2678538

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