Unsupervised Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation via Semi-supervised Learning and Label Fusion

TL;DR

This paper introduces an unsupervised domain adaptation approach combining domain alignment, semi-supervised learning, and label fusion to improve MRI segmentation of vestibular schwannoma and cochlea, achieving state-of-the-art results.

Contribution

It presents a novel semi-supervised domain adaptation method with label fusion for improved MRI segmentation of VS and cochlea without requiring full annotations.

Findings

Achieved mean dice scores of 79.9% for VS and 82.5% for cochlea.

Outperformed all competing methods in cochlea ASSD.

Demonstrated effective unsupervised adaptation in medical image segmentation.

Abstract

Automatic methods to segment the vestibular schwannoma (VS) tumors and the cochlea from magnetic resonance imaging (MRI) are critical to VS treatment planning. Although supervised methods have achieved satisfactory performance in VS segmentation, they require full annotations by experts, which is laborious and time-consuming. In this work, we aim to tackle the VS and cochlea segmentation problem in an unsupervised domain adaptation setting. Our proposed method leverages both the image-level domain alignment to minimize the domain divergence and semi-supervised training to further boost the performance. Furthermore, we propose to fuse the labels predicted from multiple models via noisy label correction. In the MICCAI 2021 crossMoDA challenge, our results on the final evaluation leaderboard showed that our proposed method has achieved promising segmentation performance with mean dice score of 79.9% and 82.5% and ASSD of 1.29 mm and 0.18 mm for VS tumor and cochlea, respectively. The cochlea ASSD achieved by our method has outperformed all other competing methods as well as the supervised nnU-Net.