Automated 3D segmentation of the prostate gland in CT images - a first step towards objective measurements of prostate uptake in PET and SPECT images

Journal article, 2017

Objectives: Uptake of PSMA-targeted tracers and choline in the prostate gland may serve as guidance for management of patients with prostate cancer. Our aim was to develop objectively measured PET/CT and SPECT/CT imaging biomarkers reflecting such uptake. In this study we took the first step by introducing and validating a completely automated algorithm for 3D-segmentation of the prostate gland in CT images.
Methods: A group of 100 patients who had undergone 18F-FDG PET/CT scanning was used as training set. A single radiologist performed manual segmentations of the prostate gland in all 100 CT scans using a custom software tool. A multi-atlas-based method was used applied for automated segmentation of the prostate gland. Each of a subset of the training images was registered separately to the test image. By applying the resulting transformations to the manual delineations a rough segmentation of the test image was obtained. This segmentation was refined using a random-forest classifier and the final segmentation was obtained with graph cuts. A separate validation group comprised 46 patients (aged 53-94 years) with biopsy-proven prostate cancer, who had undergone both 18F-fluoromethylcholine PET/CT and 18F-sodiumfluoride PET/CT within a time frame of 3 weeks as part of a previous research project. A diagnostic contrast-enhanced CT scan (64-slice helical, 120 kV, ’smart mA’ maximum 400 mA) was obtained with a CT slice thickness of 3.75 mm. We speculated that the volume of the prostate gland and in particular the fraction of the gland that had abnormally high tracer accumulation, might be useful biomarkers helping to improve management and prognostication in cancer patients. The reproducibility of automated measurements of the prostate gland volume was therefore studied using the two CT scans from each patient in the validation set.
Results: The automatically measured prostate gland volumes in the validation set ranged between 13 ml and 90 ml with a mean of 48 ml. The mean difference between the two volume measurements in each patient was 2.4 ml with an SD of 6.6 ml. The difference was less than 10 ml in 41 of the 46 cases.
Conclusion: We have demonstrated a reproducible and automated algorithm for 3D-segmentation of the prostate gland in CT images. This is a first step towards objective measurements of prostate gland tracer uptake in PET and SPECT examinations, because PET and SPECT images alone do not allow for accurate segmentation of the prostate gland, which instead depends on proper segmentation based on the corresponding CT scans.