Using a deep learning approach for implanted seed detection on fluoroscopy images in prostate brachytherapy

Andy Yuan; Tarun Podder; Jiankui Yuan; Yiran Zheng

doi:10.5114/jcb.2023.125512

eISSN: 2081-2841
ISSN: 1689-832X

Journal of Contemporary Brachytherapy

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1/2023
vol. 15

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abstract:

Original paper

Using a deep learning approach for implanted seed detection on fluoroscopy images in prostate brachytherapy

Andy Yuan

¹

,

Tarun Podder

²

,

Jiankui Yuan

²

,

Yiran Zheng

²

Youngstown State University, Youngstown, United States
University Hospitals, Cleveland Medical Center, Cleveland, United States

J Contemp Brachytherapy 2023; 15, 1: 69–74

DOI: https://doi.org/10.5114/jcb.2023.125512

Online publish date: 2023/02/27

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Purpose:
To apply a deep learning approach to automatically detect implanted seeds on a fluoroscopy image in prostate brachytherapy.

Material and methods:
Forty-eight fluoroscopy images of patients, who underwent permanent seed implant (PSI) were used for this study after our Institutional Review Boards approval. Pre-processing procedures that were used to prepare for the training data, included encapsulating each seed in a bounding box, re-normalizing seed dimension, cropping to a region of prostate, and converting fluoroscopy image to PNG format. We employed a pre-trained faster region convolutional neural network (R-CNN) from PyTorch library for automatic seed detection, and leave-one-out cross-validation (LOOCV) procedure was applied to evaluate the performance of the model.

Results:
Almost all cases had mean average precision (mAP) greater than 0.91, with most cases (83.3%) having a mean average recall (mAR) above 0.9. All cases achieved F1-scores exceeding 0.91. The averaged results for all the cases were 0.979, 0.937, and 0.957 for mAP, mAR, and F1-score, respectively.

Conclusions:
Although there are limitations shown in interpreting overlapping seeds, our model is reasonably accurate and shows potential for further applications.

keywords:

Using a deep learning approach for implanted seed detection on fluoroscopy images in prostate brachytherapy

Andy Yuan 1 , Tarun Podder 2 , Jiankui Yuan 2 , Yiran Zheng 2

deep learning, prostate seed implant, brachytherapy, automatic seed identification

Andy Yuan

¹

,

Tarun Podder

²

,

Jiankui Yuan

²

,

Yiran Zheng

²