{"ID":2890289,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.18895","arxiv_id":"2507.18895","title":"Dealing with Segmentation Errors in Needle Reconstruction for MRI-Guided Brachytherapy","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.","short_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...","url_abs":"https://arxiv.org/abs/2507.18895","url_pdf":"https://arxiv.org/pdf/2507.18895v1","authors":"[\"Vangelis Kostoulas\",\"Arthur Guijt\",\"Ellen M. Kerkhof\",\"Bradley R. Pieters\",\"Peter A. N. Bosman\",\"Tanja Alderliesten\"]","published":"2025-07-25T02:35:04Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\"]","methods":"[]","has_code":false}