Automatic seed localization of iodine brachytherapy implants from CT images

Radioactive seed implantation has emerged as an effective treatment modality where small radioactive seeds are implanted into the target organ to eradicate the cancer by emitting radiation. Precise seeds localization can indicate whether those seeds deliver sufficient doses of radiation. However, it is challenging and laborious to identify all seeds manually in a short time. Therefore, our purpose in this study was to develop an automatic technique for identifying implanted seeds on any parts of body. The algorithm relies on a 3D adaptive median filter to remove bone structure; white top-hat transform to extract seeds-like objects and further seed classification analysis based on size, shape and their connection etc. Preliminary results on ten patients and seven simulated data show that this approach to be effective and accurate. It resulted in a 96.9 % detection rate with a corresponding 4.7 % false-positive rate for clinical data; a 98.5 % detection rate with a corresponding 4.1 % false-positive rate for the simulated data; and sub-millimeter accuracy for both data sets. This method can achieve robust and accurate seed segmentation through the proposed workflow.