Weakly supervised volumetric prostate registration for MRI-TRUS image driven by signed distance map

Image registration is a fundamental step for MRI-TRUS fusion targeted biopsy. Due to the inherent representational differences between these two image modalities, though, intensity-based similarity losses for registration tend to result in poor performance. To mitigate this, comparison of organ segmentations, functioning as a weak proxy measure of image similarity, has been proposed. Segmentations, though, are limited in their information encoding capabilities. Signed distance maps (SDMs), on the other hand, encode these segmentations into a higher dimensional space where shape and boundary information are implicitly captured, and which, in addition, yield high gradients even for slight mismatches, thus preventing vanishing gradients during deep-network training. Based on these advantages, this study proposes a weakly-supervised deep learning volumetric registration approach driven by a mixed loss that operates both on segmentations and their corresponding SDMs, and which is not only robust to outliers, but also encourages optimal global alignment. Our experimental results, performed on a public prostate MRI-TRUS biopsy dataset, demonstrate that our method outperforms other weaklysupervised registration approaches with a dice similarity coefficient (DSC), Hausdorff distance (HD) and mean surface distance (MSD) of 87.3 & PLUSMN; 11.3, 4.56 & PLUSMN; 1.95 mm, and 0.053 & PLUSMN; 0.026 mm, respectively. We also show that the proposed method effectively preserves the prostate gland's internal structure.

Automated summary

This study proposes a weakly-supervised deep learning volumetric registration approach that combines segmentations and signed distance maps (SDMs) as a mixed loss function. The method is robust to outliers and encourages optimal global alignment. Experimental results on a public prostate MRI-TRUS biopsy dataset demonstrate that the proposed method outperforms other weakly-supervised registration approaches.