Multi-needle Localization with Attention U-Net in US-guided HDR Prostate Brachytherapy.

Medical Physics
Yupei ZhangXiaofeng Yang

Abstract

Ultrasound (US)-guided high dose rate (HDR) prostate brachytherapy requests the clinicians to place HDR needles (catheters) into the prostate gland under transrectal US (TRUS) guidance in the operating room. The quality of the subsequent radiation treatment plan is largely dictated by the needle placements, which varies upon the experience level of the clinicians and the procedure protocols. Real-time plan dose distribution, if available, could be a vital tool to provide more subjective assessment of the needle placements, hence potentially improving the radiation plan quality and the treatment outcome. However, due to low signal-to-noise ratio (SNR) in US imaging, real-time multi-needle segmentation in 3D TRUS, which is the major obstacle for real-time dose mapping, has not been realized to date. In this study, we propose a deep learning-based method that enables accurate and real-time digitization of the multiple needles in the 3D TRUS images of HDR prostate brachytherapy. A deep learning model based on the U-Net architecture was developed to segment multiple needles in the 3D TRUS images. Attention gates were considered in our model to improve the prediction on the small needle points. Furthermore, the spatial continuity of ...Continue Reading

References

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Citations

Aug 18, 2020·Radiotherapy and Oncology : Journal of the European Society for Therapeutic Radiology and Oncology·Joel PoderDean Cutajar
Oct 5, 2020·Medical Physics·Christoffer AndersénJakob H Lagerlöf
May 17, 2021·Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)·Yabo FuXiaofeng Yang
Jun 25, 2021·Journal of Applied Clinical Medical Physics·Mingquan LinXiaofeng Yang

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