1. Weakly supervised volumetric prostate registration for MRI-TRUS image driven by signed distance map.
- Author
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Wu M, He X, Li F, Zhu J, Wang S, and Burstein PD
- Subjects
- Male, Humans, Ultrasonography, Biopsy, Magnetic Resonance Imaging methods, Image Processing, Computer-Assisted methods, Prostate diagnostic imaging, Prostatic Neoplasms diagnostic imaging
- Abstract
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 weakly-supervised registration approaches with a dice similarity coefficient (DSC), Hausdorff distance (HD) and mean surface distance (MSD) of 87.3 ± 11.3, 4.56 ± 1.95 mm, and 0.053 ± 0.026 mm, respectively. We also show that the proposed method effectively preserves the prostate gland's internal structure., Competing Interests: Declaration of competing interest None Declared., (Copyright © 2023 Elsevier Ltd. All rights reserved.)
- Published
- 2023
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