Mono2D: A Trainable Monogenic Layer for Robust Knee Cartilage Segmentation on Out-of-Distribution 2D Ultrasound Data
Journal:
arXiv
Published Date:
Mar 12, 2025
Abstract
Automated knee cartilage segmentation using point-of-care ultrasound devices
and deep-learning networks has the potential to enhance the management of knee
osteoarthritis. However, segmentation algorithms often struggle with domain
shifts caused by variations in ultrasound devices and acquisition parameters,
limiting their generalizability. In this paper, we propose Mono2D, a monogenic
layer that extracts multi-scale, contrast- and intensity-invariant local phase
features using trainable bandpass quadrature filters. This layer mitigates
domain shifts, improving generalization to out-of-distribution domains. Mono2D
is integrated before the first layer of a segmentation network, and its
parameters jointly trained alongside the network's parameters. We evaluated
Mono2D on a multi-domain 2D ultrasound knee cartilage dataset for single-source
domain generalization (SSDG). Our results demonstrate that Mono2D outperforms
other SSDG methods in terms of Dice score and mean average surface distance. To
further assess its generalizability, we evaluate Mono2D on a multi-site
prostate MRI dataset, where it continues to outperform other SSDG methods,
highlighting its potential to improve domain generalization in medical imaging.
Nevertheless, further evaluation on diverse datasets is still necessary to
assess its clinical utility.
