Dealing with Segmentation Errors in Needle Reconstruction for MRI-Guided Brachytherapy

• URL: http://arxiv.org/abs/2507.18895v1
• Date: Fri, 25 Jul 2025 02:35:04 GMT
• Title: Dealing with Segmentation Errors in Needle Reconstruction for MRI-Guided Brachytherapy
• Authors: Vangelis Kostoulas, Arthur Guijt, Ellen M. Kerkhof, Bradley R. Pieters, Peter A. N. Bosman, Tanja Alderliesten,
• Abstract summary: We propose adaptations to existing post-processing techniques aimed at dealing with segmentation errors and improving the reconstruction accuracy.<n> Experiments on a prostate cancer dataset, based on MRI scans annotated by medical professionals, demonstrate that our proposed adaptations can help to effectively manage segmentation errors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Brachytherapy involves bringing a radioactive source near tumor tissue using implanted needles. Image-guided brachytherapy planning requires amongst others, the reconstruction of the needles. Manually annotating these needles on patient images can be a challenging and time-consuming task for medical professionals. For automatic needle reconstruction, a two-stage pipeline is commonly adopted, comprising a segmentation stage followed by a post-processing stage. While deep learning models are effective for segmentation, their results often contain errors. No currently existing post-processing technique is robust to all possible segmentation errors. We therefore propose adaptations to existing post-processing techniques mainly aimed at dealing with segmentation errors and thereby improving the reconstruction accuracy. Experiments on a prostate cancer dataset, based on MRI scans annotated by medical professionals, demonstrate that our proposed adaptations can help to effectively manage segmentation errors, with the best adapted post-processing technique achieving median needle-tip and needle-bottom point localization errors of $1.07$ (IQR $\pm 1.04$) mm and $0.43$ (IQR $\pm 0.46$) mm, respectively, and median shaft error of $0.75$ (IQR $\pm 0.69$) mm with 0 false positive and 0 false negative needles on a test set of 261 needles.

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