Mask Enhanced Deeply Supervised Prostate Cancer Detection on B-mode Micro-Ultrasound
Journal:
arXiv
Published Date:
Dec 14, 2024
Abstract
Prostate cancer is a leading cause of cancer-related deaths among men. The
recent development of high frequency, micro-ultrasound imaging offers improved
resolution compared to conventional ultrasound and potentially a better ability
to differentiate clinically significant cancer from normal tissue. However, the
features of prostate cancer remain subtle, with ambiguous borders with normal
tissue and large variations in appearance, making it challenging for both
machine learning and humans to localize it on micro-ultrasound images.
We propose a novel Mask Enhanced Deeply-supervised Micro-US network, termed
MedMusNet, to automatically and more accurately segment prostate cancer to be
used as potential targets for biopsy procedures. MedMusNet leverages predicted
masks of prostate cancer to enforce the learned features layer-wisely within
the network, reducing the influence of noise and improving overall consistency
across frames.
MedMusNet successfully detected 76% of clinically significant cancer with a
Dice Similarity Coefficient of 0.365, significantly outperforming the baseline
Swin-M2F in specificity and accuracy (Wilcoxon test, Bonferroni correction,
p-value<0.05). While the lesion-level and patient-level analyses showed
improved performance compared to human experts and different baseline, the
improvements did not reach statistical significance, likely on account of the
small cohort.
We have presented a novel approach to automatically detect and segment
clinically significant prostate cancer on B-mode micro-ultrasound images. Our
MedMusNet model outperformed other models, surpassing even human experts. These
preliminary results suggest the potential for aiding urologists in prostate
cancer diagnosis via biopsy and treatment decision-making.
