Atlas-aware ConvNetfor Accurate yet Robust Anatomical Segmentation

TL;DR

This paper introduces a novel atlas-aware convolutional network framework that enhances anatomical segmentation accuracy and robustness by integrating probabilistic atlas priors through a Constraint Adoption Module, benefiting clinical imaging applications.

Contribution

It proposes a flexible, compact CAM module that incorporates atlas priors into ConvNets, improving accuracy and robustness in anatomical segmentation tasks.

Findings

Achieves state-of-the-art accuracy on brain parcellation datasets.

Significantly reduces structural abnormalities in segmentation outputs.

Enhances robustness to synthetic pathologies and different scanning setups.

Abstract

Convolutional networks (ConvNets) have achieved promising accuracy for various anatomical segmentation tasks. Despite the success, these methods can be sensitive to data appearance variations. Considering the large variability of scans caused by artifacts, pathologies, and scanning setups, robust ConvNets are vital for clinical applications, while have not been fully explored. In this paper, we propose to mitigate the challenge by enabling ConvNets' awareness of the underlying anatomical invariances among imaging scans. Specifically, we introduce a fully convolutional Constraint Adoption Module (CAM) that incorporates probabilistic atlas priors as explicit constraints for predictions over a locally connected Conditional Random Field (CFR), which effectively reinforces the anatomical consistency of the labeling outputs. We design the CAM to be flexible for boosting various ConvNet, and compact for co-optimizing with ConvNets for fusion parameters that leads to the optimal performance. We show the advantage of such atlas priors fusion is two-fold with two brain parcellation tasks. First, our models achieve state-of-the-art accuracy among ConvNet-based methods on both datasets, by significantly reducing structural abnormalities of predictions. Second, we can largely boost the robustness of existing ConvNets, proved by: (i) testing on scans with synthetic pathologies, and (ii) training and evaluation on scans of different scanning setups across datasets. Our method is proposing to be easily adopted to existing ConvNets by fine-tuning with CAM plugged in for accuracy and robustness boosts.