Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction based on Semi-Supervised Contrastive Learning

TL;DR

This paper introduces an unsupervised domain adaptation framework that leverages semi-supervised contrastive learning to improve cross-modality segmentation and Koos grade prediction of vestibular schwannoma, achieving competitive results.

Contribution

It proposes a novel unsupervised domain adaptation method combining shared representation learning, modality recovery, and contrastive pre-training for improved medical image analysis.

Findings

Ranked 4th in task1 with Dice score 0.8394

Ranked 2nd in task2 with MSE 0.3941

Effective cross-modality segmentation and Koos grade prediction

Abstract

Domain adaptation has been widely adopted to transfer styles across multi-vendors and multi-centers, as well as to complement the missing modalities. In this challenge, we proposed an unsupervised domain adaptation framework for cross-modality vestibular schwannoma (VS) and cochlea segmentation and Koos grade prediction. We learn the shared representation from both ceT1 and hrT2 images and recover another modality from the latent representation, and we also utilize proxy tasks of VS segmentation and brain parcellation to restrict the consistency of image structures in domain adaptation. After generating missing modalities, the nnU-Net model is utilized for VS and cochlea segmentation, while a semi-supervised contrastive learning pre-train approach is employed to improve the model performance for Koos grade prediction. On CrossMoDA validation phase Leaderboard, our method received rank 4 in task1 with a mean Dice score of 0.8394 and rank 2 in task2 with Macro-Average Mean Square Error of 0.3941. Our code is available at https://github.com/fiy2W/cmda2022.superpolymerization.