Exploring Test Time Adaptation for Subcortical Segmentation of the Fetal Brain in 3D Ultrasound

TL;DR

This paper investigates the use of test time adaptation (TTA) with a normative atlas to improve deep learning-based subcortical segmentation of the fetal brain in 3D ultrasound images, especially under domain shifts.

Contribution

It introduces a novel TTA method incorporating a normative atlas as a prior, and benchmarks its effectiveness against other TTA approaches under various domain shifts.

Findings

TTA improves segmentation performance under domain shifts.

The proposed atlas-based TTA outperforms other TTA methods.

Code availability facilitates future research and application.

Abstract

Monitoring the growth of subcortical regions of the fetal brain in ultrasound (US) images can help identify the presence of abnormal development. Manually segmenting these regions is a challenging task, but recent work has shown that it can be automated using deep learning. However, applying pretrained models to unseen freehand US volumes often leads to a degradation of performance due to the vast differences in acquisition and alignment. In this work, we first demonstrate that test time adaptation (TTA) can be used to improve model performance in the presence of both real and simulated domain shifts. We further propose a novel TTA method by incorporating a normative atlas as a prior for anatomy. In the presence of various types of domain shifts, we benchmark the performance of different TTA methods and demonstrate the improvements brought by our proposed approach, which may further facilitate automated monitoring of fetal brain development. Our code is available at https://github.com/joshuaomolegan/TTA-for-3D-Fetal-Subcortical-Segmentation.