TY - GEN
T1 - Parameter-Free Latent Space Transformer for Zero-Shot Bidirectional Cross-modality Liver Segmentation
AU - Li, Yang
AU - Zou, Beiji
AU - Dai, Yulan
AU - Zhu, Chengzhang
AU - Yang, Fan
AU - Li, Xin
AU - Bai, Harrison X.
AU - Jiao, Zhicheng
N1 - Funding Information:
Acknowledgements. This work is supported by the National Key R&D Program of China (2018AAA0102100), International Science and Technology Innovation Joint Base of Machine Vision and Medical Image Processing in Hunan Province (2021CB1013), the Scientific and Technological Innovation Leading Plan of High-tech Industry of Hunan Province (2020GK2021) , the 111 project under grant no. B18059, the Fundamental Research Funds for the Central Universities of Central South University.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - In this paper, we address the domain shift in cross CT-MR liver segmentation task with a latent space investigation. Domain adaptation between modalities is of significant importance in clinical practice, as different diagnostic procedures require different imaging modalities, such as CT and MR. Thus, training a convolutional neural network (CNN) with one modality may not be sufficient for application in another one. Most domain adaptation methods need to use data and ground truths of both source and target domain in the training process. Different from these techniques, we propose a zero-shot bidirectional cross-modality liver segmentation method by investigating a parameter-free latent space through the prior knowledge from CT and MR images. Experiments on the CHAOS, the subset of LiTS and the local TACE datasets demonstrate that our method can well deal with the problem of CNN failure caused by domain shift and yields promising segmentation results.
AB - In this paper, we address the domain shift in cross CT-MR liver segmentation task with a latent space investigation. Domain adaptation between modalities is of significant importance in clinical practice, as different diagnostic procedures require different imaging modalities, such as CT and MR. Thus, training a convolutional neural network (CNN) with one modality may not be sufficient for application in another one. Most domain adaptation methods need to use data and ground truths of both source and target domain in the training process. Different from these techniques, we propose a zero-shot bidirectional cross-modality liver segmentation method by investigating a parameter-free latent space through the prior knowledge from CT and MR images. Experiments on the CHAOS, the subset of LiTS and the local TACE datasets demonstrate that our method can well deal with the problem of CNN failure caused by domain shift and yields promising segmentation results.
KW - Cross-modality liver segmentation
KW - Latent space transformer
KW - Parameter-free
KW - Zero-shot
UR - http://www.scopus.com/inward/record.url?scp=85139031653&partnerID=8YFLogxK
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U2 - 10.1007/978-3-031-16440-8_59
DO - 10.1007/978-3-031-16440-8_59
M3 - Conference contribution
AN - SCOPUS:85139031653
SN - 9783031164392
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 619
EP - 628
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
A2 - Wang, Linwei
A2 - Dou, Qi
A2 - Fletcher, P. Thomas
A2 - Speidel, Stefanie
A2 - Li, Shuo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Y2 - 18 September 2022 through 22 September 2022
ER -