Uncertainty-aware transformer model for anatomical landmark detection in paraspinal muscle MRIs

Abstract

Anatomical landmark identification is crucial in the registration of medical images in a wide range of clinical applications. Various machine and deep learning (DL) techniques have been proposed to annotate anatomical landmarks automatically. However, very few have taken advantage of the more recent Transformer models that do not suffer from inductive bias of convolutional neural networks as well as incorporating uncertainty assessments. This paper proposes a novel technique based on the Swin Transformer V2 (Swin-V2) with an uncertainty quantification module for robust anatomical landmark detection in paraspinal muscle MRIs to facilitate muscle morphometric analysis for low back pain (LBP). Specifically, we employ statistical measurements from stochastic sampling using Monte Carlo (MC) dropouts as uncertainty metrics for automatic landmark detection. The proposed method was trained and validated using 350 axial MRI slices of paraspinal muscles at the mid-disc spinal levels commonly associated with LBP. We demonstrate that the selected uncertainty metrics are correlated with automatic landmark detection errors and, in addition, can be used as inputs to grade the quality of the identified landmarks with a lightweight random forest algorithm for a more straightforward interpretation.