Mono2D: A Trainable Monogenic Layer for Robust Knee Cartilage Segmentation on Out-of-Distribution 2D Ultrasound Data
This addresses domain generalization for medical imaging segmentation, particularly for knee osteoarthritis management using point-of-care ultrasound, but it is incremental as it builds on existing single-source domain generalization methods.
The paper tackled the problem of domain shifts in automated knee cartilage segmentation from 2D ultrasound data by proposing Mono2D, a trainable monogenic layer that improved generalization to out-of-distribution domains, outperforming other methods in Dice score and mean average surface distance on a multi-domain dataset and a multi-site prostate MRI dataset.
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.