CNN-based Prostate Zonal Segmentation on T2-weighted MR Images: A Cross-dataset Study
This work addresses the challenge of automating prostate zonal segmentation for improved cancer diagnosis, but it is incremental as it compares existing methods on new data.
The study tackled prostate zonal segmentation on T2-weighted MR images by comparing CNN architectures like SegNet, U-Net, and pix2pix across two multi-centric datasets, finding that U-Net generally outperformed others, especially when trained and tested on multiple datasets.
Prostate cancer is the most common cancer among US men. However, prostate imaging is still challenging despite the advances in multi-parametric Magnetic Resonance Imaging (MRI), which provides both morphologic and functional information pertaining to the pathological regions. Along with whole prostate gland segmentation, distinguishing between the Central Gland (CG) and Peripheral Zone (PZ) can guide towards differential diagnosis, since the frequency and severity of tumors differ in these regions; however, their boundary is often weak and fuzzy. This work presents a preliminary study on Deep Learning to automatically delineate the CG and PZ, aiming at evaluating the generalization ability of Convolutional Neural Networks (CNNs) on two multi-centric MRI prostate datasets. Especially, we compared three CNN-based architectures: SegNet, U-Net, and pix2pix. In such a context, the segmentation performances achieved with/without pre-training were compared in 4-fold cross-validation. In general, U-Net outperforms the other methods, especially when training and testing are performed on multiple datasets.