Automated Multisequence Cardiac MRI Segmentation Using Supervised Domain Adaptation Left ventricle segmentation and morphological assessment are essential for improving diagnosis and our understanding of cardiomyopathy, which in turn is imperative for reducing risk of myocardial infarctions in patients. Convolutional neural network (CNN) based methods for cardiac magnetic resonance (CMR) image segmentation rely on supervision with pixellevel annotations, and may not generalize well to images from a different domain. These methods are typically sensitive to variations in imaging protocols and data acquisition. Since annotating multisequence CMR images is tedious and subject to inter and intraobserver variations, developing methods that can automatically adapt from one domain to the target domain is of great interest. In this paper, we propose an approach for domain adaptation in multisequence CMR segmentation task using transfer learning that combines multisource image information. We first train an encoderdecoder CNN on T2weighted and balancedSteady State Free Precession (bSSFP) MR images with pixellevel annotation and finetune the same network with a limited number of Late Gadolinium EnhancedMR (LGEMR) subjects, to adapt the domain features. The domainadapted network was trained with just four LGEMR training samples and obtained an average Dice score of (sim )85.0% on the test set comprises of 40 LGEMR subjects. The proposed method significantly outperformed a network without adaptation trained from scratch on the same set of LGEMR training data.

Multisequence Cardiac MR Segmentation with Adversarial Domain Adaptation Network Automatic and accurate segmentation of the ventricles and myocardium from multisequence 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 T2weighted. Our segmentation network is attention UNet 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 groupwise feature recalibration module to enforce the finegrained semanticlevel feature alignment that matching features from different networks but with the same class label. We evaluate our method on the multisequence 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.