Mr. Srinjoy Bhuiya Profile

Srinjoy Bhuiya

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Proceedings Article | 29 March 2024 Poster + Paper

Using attention-based convolutional auto-encoders for catheter path reconstruction in ultrasound images

Shreyasi Mandal, Srinjoy Bhuiya, Elodie Lugez

Proceedings Volume 12928, 129281N (2024) https://doi.org/10.1117/12.3003897

KEYWORDS: Image segmentation, Reconstruction algorithms, Ultrasonography, 3D modeling, Education and training, Prostate, High dynamic range imaging, 3D image processing, Visual process modeling, Medical image reconstruction, Deep learning

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Purpose: In high-dose-rate prostate brachytherapy, multiple catheters are inserted into the prostate gland. Conventional catheter detection in ultrasound images is hindered by images of a low signal-to-noise ratio, making it difficult to identify the catheters manually. To address this issue, this paper presents an innovative automated deep-learning approach for catheter path reconstruction. Method: A lightweight spatial attention-based autoencoder convolutional neural network is developed to accurately and rapidly segment catheters in real-time volumetric transrectal ultrasound images. To overcome the challenges of noisy and limited annotated data, an auto-augmentation technique is employed, which leverages a controller network to learn the optimal augmentation policy. A 3D random sample consensus-based catheter path reconstruction algorithm is also proposed to transform the catheter segmentations into smooth curves representing their paths. Results: By integrating automated data augmentation with an optimized autoencoder network, structured dropout, and batch normalization techniques, the proposed algorithm successfully detected 98% of the catheter paths tested. The mean tip errors were recorded as 0.18±0.12 mm, while the mean shaft errors were recorded as 0.39±0.28 mm, varying depending on the complexity of the catheter curve path. Notably, the proposed algorithm outperformed existing methods by exhibiting faster inference times, with an average inference time of 0.0029 seconds. Conclusion: The proposed methodology offers a comprehensive approach to enhance the accuracy of catheter path reconstruction in prostate brachytherapy. This lightweight neural network also has the potential to significantly improve the prostate brachytherapy workflow by making the catheter reconstruction process timeefficient.

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