Improving Automatic Fetal Biometry Measurement with Swoosh Activation Function

TL;DR

This paper introduces a novel Swoosh Activation Function (SAF) that enhances deep learning landmark detection for fetal biometry, significantly improving measurement accuracy of fetal thalamus diameter and head circumference from ultrasound images.

Contribution

The paper proposes the SAF, a new activation function that regularizes heatmaps in landmark detection, improving fetal biometry measurements beyond current state-of-the-art methods.

Findings

SAF improves measurement accuracy of FTD and FHC.

SAF outperforms BiometryNet in ICC and mean difference scores.

SAF is highly generalizable and customizable.

Abstract

The measurement of fetal thalamus diameter (FTD) and fetal head circumference (FHC) are crucial in identifying abnormal fetal thalamus development as it may lead to certain neuropsychiatric disorders in later life. However, manual measurements from 2D-US images are laborious, prone to high inter-observer variability, and complicated by the high signal-to-noise ratio nature of the images. Deep learning-based landmark detection approaches have shown promise in measuring biometrics from US images, but the current state-of-the-art (SOTA) algorithm, BiometryNet, is inadequate for FTD and FHC measurement due to its inability to account for the fuzzy edges of these structures and the complex shape of the FTD structure. To address these inadequacies, we propose a novel Swoosh Activation Function (SAF) designed to enhance the regularization of heatmaps produced by landmark detection algorithms. Our SAF serves as a regularization term to enforce an optimum mean squared error (MSE) level between predicted heatmaps, reducing the dispersiveness of hotspots in predicted heatmaps. Our experimental results demonstrate that SAF significantly improves the measurement performances of FTD and FHC with higher intraclass correlation coefficient scores in FTD and lower mean difference scores in FHC measurement than those of the current SOTA algorithm BiometryNet. Moreover, our proposed SAF is highly generalizable and architecture-agnostic. The SAF's coefficients can be configured for different tasks, making it highly customizable. Our study demonstrates that the SAF activation function is a novel method that can improve measurement accuracy in fetal biometry landmark detection. This improvement has the potential to contribute to better fetal monitoring and improved neonatal outcomes.