Dealing with Segmentation Errors in Needle Reconstruction for MRI-Guided Brachytherapy
This work addresses a critical bottleneck for medical professionals in brachytherapy planning by improving reconstruction accuracy, though it is incremental as it adapts existing techniques.
The paper tackled the problem of segmentation errors in automatic needle reconstruction for MRI-guided brachytherapy, proposing adaptations to existing post-processing techniques that achieved median localization errors as low as 0.43 mm and 0 false positives/negatives on a test set of 261 needles.
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.