SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIs
This addresses the problem of accurate 3D segmentation in medical imaging for clinicians and researchers, enabling broader application to MRI tasks where high-resolution data is scarce, though it is incremental as it builds on existing multi-view and registration techniques.
The paper tackles the challenge of generating high-resolution 3D object masks from multiple low-resolution, unaligned MRI scans by proposing a weakly-supervised deep learning method that combines segmentation and registration networks with new regularizations and multi-view fusion, achieving superior results on two datasets without requiring high-resolution training data.
Three-dimensional segmentation in magnetic resonance images (MRI), which reflects the true shape of the objects, is challenging since high-resolution isotropic MRIs are rare and typical MRIs are anisotropic, with the out-of-plane dimension having a much lower resolution. A potential remedy to this issue lies in the fact that often multiple sequences are acquired on different planes. However, in practice, these sequences are not orthogonal to each other, limiting the applicability of many previous solutions to reconstruct higher-resolution images from multiple lower-resolution ones. We propose a weakly-supervised deep learning-based solution to generating high-resolution masks from multiple low-resolution images. Our method combines segmentation and unsupervised registration networks by introducing two new regularizations to make registration and segmentation reinforce each other. Finally, we introduce a multi-view fusion method to generate high-resolution target object masks. The experimental results on two datasets show the superiority of our methods. Importantly, the advantage of not using high-resolution images in the training process makes our method applicable to a wide variety of MRI segmentation tasks.