# Cross-Domain Echocardiography Segmentation with Multi-Space Joint Adaptation
## 1 Introduction
The F-LSeSim [18] captures the spatial relationships of two domains and preserves the scene structure consistency via a new self-supervised learning method. The CUT [19] is a contrastive learning method for patch-based image-to-image translation. The F-LSeSim and CUT only learn the translation in one direction. Figure 1: Echocardiography samples from different medical centers and vendors (**a**) CAMUS dataset: GE Vivid E95 ultrasound scanners with a GE M5 S probe and (**b**) self-acquired dataset: Vinno G86 ultrasound scanners with a S1–6 PX probe. The above-mentioned domain adaptation methods perform well in natural images, but are not fully functional in medical images; this is because most medical images have low color contrast. It is difficult to maintain the key structure by the domain adaptation in the image space [20], and a reasonable approach is the domain adaptation in other spaces. Recently, several approaches have been tried in this way on optical medical images. The p-OSAL [21] is a patch-based output space adversarial learning network for segmenting optical medical images from different datasets. The BEAL [22] network uses two discriminators driven by the boundary and entropy in the output space. The IOSUDA [23], based on the BEAL, aligns different datasets in both the input and output space. There is also some related research on grey-scale medical images. [PERSON] et al. [24] propose a multi-output adapter for cross-vendor left ventricle segmentation on cine MRI sequences. The SIFA [25] accomplishes synergistic image and feature alignment across CT and MR images. However, there has been no research on domain adaptation for echocardiography segmentation. In this study, we present a multi-space adaptation-segmentation-joint network for extracting the LV\({}_{\text{endo}}\) and LV\({}_{\text{epi}}\) in echocardiography. The method is named the MACS. It adopts a GAN architecture, integrating the segmentation part (generator) and domain adaptation part (multi-space discriminators) to handle the cross-domain echocardiography analysis. The segmentation part utilizes an attention mechanism to extract the multi-scale features. Then, a dual-branch prediction module decodes the features into segmentation masks, with an emphasis on both the target region and boundary. The domain adaptation part aligns the samples from different domains in both the feature space and output space, enforcing generators to refine the segmentation results. Our main contributions lie in the following. The MACS achieves the segmentation of the LV\({}_{\text{endo}}\) and LV\({}_{\text{epi}}\) in multi-domain echocardiography datasets using labels of only one domain for training. Specifically, it adapts the source domain and the target domain on both the feature space and output space, which avoids the side-effect of direct translation on the original ultrasound images [23] and improves the segmentation accuracy. The joint adaptation on the feature space and output space promotes the model adaptation. Furthermore, detailed experiments were performed on two datasets from different medical centers and devices. The ablation studies illustrate the contributions of each module of the MACS. The comparisons with the state-of-the-art methods confirm that our method achieves accurate segmentation on the unseen domain dataset. To the best of the authors' knowledge, it is the first model [1] for cross-domain echocardiography segmentation. The paper is organized as follows. Section 2 details the proposed method. Section 3 describes the experiment details. The experimental results are reported in Section 4 to demonstrate the effectiveness of our method. In Section 5, we provide the extended discussion. The conclusion is given in Section 6.