Anomaly-Driven Approach for Enhanced Prostate Cancer Segmentation
Journal:
arXiv
Published Date:
Apr 30, 2025
Abstract
Magnetic Resonance Imaging (MRI) plays an important role in identifying
clinically significant prostate cancer (csPCa), yet automated methods face
challenges such as data imbalance, variable tumor sizes, and a lack of
annotated data. This study introduces Anomaly-Driven U-Net (adU-Net), which
incorporates anomaly maps derived from biparametric MRI sequences into a deep
learning-based segmentation framework to improve csPCa identification. We
conduct a comparative analysis of anomaly detection methods and evaluate the
integration of anomaly maps into the segmentation pipeline. Anomaly maps,
generated using Fixed-Point GAN reconstruction, highlight deviations from
normal prostate tissue, guiding the segmentation model to potential cancerous
regions. We compare the performance by using the average score, computed as the
mean of the AUROC and Average Precision (AP). On the external test set, adU-Net
achieves the best average score of 0.618, outperforming the baseline nnU-Net
model (0.605). The results demonstrate that incorporating anomaly detection
into segmentation improves generalization and performance, particularly with
ADC-based anomaly maps, offering a promising direction for automated csPCa
identification.
