Name
Abstract | ||
|---|---|---|
Automatic and accurate segmentation of the ventricles and myocardium from multi-sequence cardiac MRI (CMR) is crucial for the diagnosis and treatment management for patients suffering from myocardial infarction (MI). However, due to the existence of domain shift among different modalities of datasets, the performance of deep neural networks drops significantly when the training and testing datasets are distinct. In this paper, we propose an unsupervised domain alignment method to explicitly alleviate the domain shifts among different modalities of CMR sequences, e.g., bSSFP, LGE, and T2-weighted. Our segmentation network is attention U-Net with pyramid pooling module, where multilevel feature space and output space adversarial learning are proposed to transfer discriminative domain knowledge across different datasets. Moreover, we further introduce a group-wise feature recalibration module to enforce the fine-grained semantic-level feature alignment that matching features from different networks but with the same class label. We evaluate our method on the multi-sequence cardiac MR Segmentation Challenge 2019 datasets, which contain three different modalities of MRI sequences. Extensive experimental results show that the proposed methods can obtain significant segmentation improvements compared with the baseline models. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-39074-7_27 | STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART: MULTI-SEQUENCE CMR SEGMENTATION, CRT-EPIGGY AND LV FULL QUANTIFICATION CHALLENGES |
Field | DocType | Volume |
Modalities,Feature vector,Domain knowledge,Pattern recognition,Domain adaptation,Segmentation,Computer science,Pooling,Pyramid,Artificial intelligence,Discriminative model | Conference | 12009 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiexiang Wang | 1 | 9 | 1.48 |
| hongyu huang | 2 | 0 | 2.03 |
| Chaoqi Chen | 3 | 9 | 1.76 |
| Wenao Ma | 4 | 6 | 1.79 |
| Yue Huang | 5 | 317 | 29.82 |
| Xinghao Ding | 6 | 591 | 52.95 |