Abstract
The automatic segmentation of prostate MRI often produces inconsistent performance because certain image slices are more difficult to segment than others. In this paper, we show that consistency can be improved using Conditional Random Fields (CRFs), which refine the segmentation results by considering pixel relationships pairwise. In practice, however, conventional CRFs are susceptible to noise and MRI intensity shifts due to their use of simple binary potentials involving spatial distance and intensity difference. Such heuristic potential functions are hardly expressive, limiting the network from extracting more relevant information and having more stable potential calculations. We propose a novel end-to-end Neural CRF (NCRF) model that utilizes learnable binary potential functions based on deep image features. Experiments show that our NCRF is a better model for prostate zonal segmentation than state-of-the-art CRF models. The NCRF improves segmentation accuracy in both the prostate transition zone and peripheral zone such that segmentation results are consistent across all the prostate slices, which can improve the performance of downstream tasks such as prostate cancer detection and segmentation. Our code is available at https://github.com/aL3x-O-o-Hung/NCRF.