Self super-resolution of anisotropic volumes in prostate MRI with normalized edge priors
Résumé
Due to involuntary organ motion, prostate magnetic resonance images are often acquired in 2D with thick through plane spacings and reconstructed into anisotropic voxelized volumes. In prostate cancer (PCa) radiotherapy, annotations such as zonal or lesion segmentation are then performed on the few axial slices in which the prostate appears, leading to coarse and often inconsistent delineations across slices, which are nevertheless used as ground truth for deep segmentation model training and performance reports. Recently, deep self-supervised super resolution (SSR) was proposed in brain MRI to remove the need for calibrated training data, by learning the low-resolution to high-resolution mapping from the anisotropic image itself. In this work, we propose a new training objective (loss function) for SSR based on the alignment of normalized image gradient vectors, a technique normally used for image registration. Preliminary experiments on the public Prostate158 dataset suggest that automatic PCa detection could be improved using SSR with the proposed training objective. However, the low quality of lesion annotations obtained on a few slices is a major bottleneck to confirm these findings quantitatively.
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