Dealing with segmentation errors in needle reconstruction for MRI-guided brachytherapy

SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling, pp. 75

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 ±1.04) mm and 0.43 (IQR ±0.46) mm, respectively, and median shaft error of 0.75 (IQR ±0.69) mm with 0 false positive and 0 false negative needles on a test set of 261 needles.

Authors 6

1. Vangelis Kostoulas Aachen Leiden Univ. Medical Ctr

   Leiden University Medical Center

   Affiliation as printed

   Leiden Univ. Medical Ctr. (Netherlands)

2. Arthur Guijt

   Centrum Wiskunde & Informatica

   Affiliation as printed

   Ctr. Wiskunde & Informatica (Netherlands)

3. Ellen M. Kerkhof Aachen Leiden Univ. Medical Ctr

   Leiden University Medical Center

   Affiliation as printed

   Leiden Univ. Medical Ctr. (Netherlands)

4. Bradley Rumwell Pieters

   Cancer Center Amsterdam · University of Amsterdam

   Affiliation as printed

   Cancer Ctr. Amsterdam (Netherlands)

   Univ. of Amsterdam (Netherlands)

5. Peter A. N. Bosman

   Centrum Wiskunde & Informatica

   Affiliation as printed

   Ctr. Wiskunde & Informatica (Netherlands)

6. Tanja Alderliesten Aachen Leiden Univ. Medical Ctr

   Leiden University Medical Center

   Affiliation as printed

   Leiden Univ. Medical Ctr. (Netherlands)