TY - GEN
T1 - Fine-Grained Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation
AU - Han, Luyi
AU - Tan, Tao
AU - Mann, Ritse
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - The domain adaptation approach has gained significant acceptance in transferring styles across various vendors and centers, along with filling the gaps in modalities. However, multi-center application faces the challenge of the difficulty of domain adaptation due to their intra-domain differences. We focus on introducing a fine-grained unsupervised framework for domain adaptation to facilitate cross-modality segmentation of vestibular schwannoma (VS) and cochlea. We propose to use a vector to control the generator to synthesize a fake image with given features. And then, we can apply various augmentations to the dataset by searching the feature dictionary. The diversity augmentation can increase the performance and robustness of the segmentation model. On the CrossMoDA2023 test phase leaderboard, our method received a mean Dice score of 0.773 and 0.843 on VS and cochlea, respectively.
AB - The domain adaptation approach has gained significant acceptance in transferring styles across various vendors and centers, along with filling the gaps in modalities. However, multi-center application faces the challenge of the difficulty of domain adaptation due to their intra-domain differences. We focus on introducing a fine-grained unsupervised framework for domain adaptation to facilitate cross-modality segmentation of vestibular schwannoma (VS) and cochlea. We propose to use a vector to control the generator to synthesize a fake image with given features. And then, we can apply various augmentations to the dataset by searching the feature dictionary. The diversity augmentation can increase the performance and robustness of the segmentation model. On the CrossMoDA2023 test phase leaderboard, our method received a mean Dice score of 0.773 and 0.843 on VS and cochlea, respectively.
KW - Domain Adaptation
KW - Segmentation
KW - Vestibular Schwannoma
UR - https://www.scopus.com/pages/publications/85219209650
U2 - 10.1007/978-3-031-76163-8_33
DO - 10.1007/978-3-031-76163-8_33
M3 - Conference contribution
AN - SCOPUS:85219209650
SN - 9783031761621
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 364
EP - 371
BT - Brain Tumor Segmentation, and Cross-Modality Domain Adaptation for Medical Image Segmentation - MICCAI Challenges, BraTS 2023 and CrossMoDA 2023, Held in Conjunction with MICCAI 2023, Proceedings
A2 - Baid, Ujjwal
A2 - Malec, Sylwia
A2 - Bakas, Spyridon
A2 - Dorent, Reuben
A2 - Pytlarz, Monika
A2 - Crimi, Alessandro
A2 - Su, Ruisheng
A2 - Wijethilake, Navodini
PB - Springer Science and Business Media Deutschland GmbH
T2 - Challenge on Brain Tumor Segmentation, BraTS 2023, International Challenge on Cross-Modality Domain Adaptation for Medical Image Segmentation, CrossMoDA 2023, held in conjunction with the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2023
Y2 - 8 October 2023 through 12 October 2023
ER -