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

TL;DR

This paper introduces adaptations to post-processing techniques for automatic needle reconstruction in MRI-guided brachytherapy, effectively handling segmentation errors and improving localization accuracy in prostate cancer imaging.

Contribution

It proposes novel adaptations to existing post-processing methods specifically designed to robustly address segmentation errors in needle reconstruction.

Findings

Median needle-tip localization error of 1.07 mm

Median shaft error of 0.75 mm

Zero false positives and negatives in test set

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.