Keywords: Supervoxel segmentation, inter-patient registration, noisy labels
TL;DR: DL-based registration so far used either regression or metric/label-supervision, here we propose deformable supervoxel segmentation for improved learning.
Abstract: Deep learning has the potential to substantially improve inter-subject alignment for shape and atlas analysis. So far most highly accurate supervised approaches require dense manual annotations and complex multi-level architectures but may still be susceptible to label bias. We present a radically different approach for learning to estimate large deformations without expert supervision. Instead of regressing displacements, we train a 3D DeepLab network to predict automatic supervoxel segmentations. To enable consistent supervoxel labels, we use the warping field of a conventional approach and increase the accuracy by sampling multiple complementary over-segmentations. We experimentally demonstrate that 1) our deformable supervoxels are less sensitive to large initial misalignment and can combine linear and nonlinear registration and 2) using this self-supervised classification loss is more robust to noisy ground truth and leads to better convergence than direct regression as supervision.
Paper Type: methodological development
Primary Subject Area: Image Registration
Secondary Subject Area: Learning with Noisy Labels and Limited Data
Paper Status: original work, not submitted yet
Source Code Url: https://github.com/multimodallearning/slic_reg
Data Set Url: https://github.com/multimodallearning/slic_reg
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