Background: Excess prostate tissue is trimmed near the prostate capsula boundary during
transurethral plasma kinetic enucleation of prostate (PKEP) and transurethral bipolar plasmakinetic
resection of prostate (PKRP) surgeries. If a large portion of the tissue is removed, a prostate
capsula perforation can potentially occur. As such, real-time accurate prostate capsula (PC) detection
is critical for the prevention of these perforations.
Objective: This study investigated the potential for using image denoising, image dimension reduction
and feature fusion to improve real-time prostate capsula detection with two objectives. First,
this paper mainly studied feature selection and input dimension reduction. Secondly, image denoising
was evaluated, as it is of paramount importance to transient stability assessment based on neural
Methods: Two new feature fusion techniques, maxpooling bilinear interpolation single-shot multibox
detector (PBSSD) and bilinear interpolation single shot multibox detector (BSSD) were proposed.
Before original images were sent to the neural network, they were processed by principal
component analysis (PCA) and adaptive median filter (AMF) for dimension reduction and image
Results: The results showed that the application of PCA and AMF with PBSSD increased the
mean average precision (mAP) for prostate capsula images by 8.55% and reached 80.15%, compared
with single shot multibox detector (SSD) alone. Application of PCA with BSSD increased
the mAP for prostate capsula images by 4.6% compared with SSD alone.
Conclusion: Compared with other methods, ours were proven to be more accurate for real-time
prostate capsula detection. The improved mAP results suggest that the proposed approaches are
powerful tools for improving SSD networks.