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AutoEncoder-Driven Multimodal Collaborative Learning for Medical Image Synthesis

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Abstract

Multimodal medical images have been widely applied in various clinical diagnoses and treatments. Due to the practical restrictions, certain modalities may be hard to acquire, resulting in incomplete data. Existing methods attempt to generate the missing data with multiple available modalities. However, the modality differences in tissue contrast and lesion appearance become an obstacle to making a precise estimation. To address this issue, we propose an autoencoder-driven multimodal collaborative learning framework for medical image synthesis. The proposed approach takes an autoencoder to comprehensively supervise the synthesis network using the self-representation of target modality, which provides target-modality-specific prior to guide multimodal image fusion. Furthermore, we endow the autoencoder with adversarial learning capabilities by converting its encoder into a pixel-sensitive discriminator capable of both reconstruction and discrimination. To this end, the generative model is completely supervised by the autoencoder. Considering the efficiency of multimodal generation, we also introduce a modality mask vector as the target modality label to guide the synthesis direction, empowering our method to estimate any missing modality with a single model. Extensive experiments on multiple medical image datasets demonstrate the significant generalization capability as well as the superior synthetic quality of the proposed method, compared with other competing methods. The source code will be available: https://github.com/bcaosudo/AE-GAN.

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Data Availability

The data generated during the current study are available on reasonable request.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 62106171, 61925602, U22A2096, 62036007, and 62131015; in part by the Science and Technology Commission of Shanghai Municipality (STCSM) under Grant 21010502600; in part by the Technology Innovation Leading Program of Shaanxi under Grant 2022QFY01-15; in part by Open Research Projects of Zhejiang Lab under Grant 2021KG0AB01; in part by the Fundamental Research Funds for the Central Universities under Grant QTZX23042.

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Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, China

    Bing Cao, Zhiwei Bi & Qinghua Hu

  2. State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China

    Bing Cao & Nannan Wang

  3. School of Biomedical Engineering, ShanghaiTech University, Shanghai, China

    Han Zhang & Dinggang Shen

  4. Chongqing University of Posts and Telecommunications, Chongqing, China

    Xinbo Gao

  5. Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China

    Dinggang Shen

  6. Shanghai Clinical Research and Trial Center, Shanghai, China

    Dinggang Shen

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  1. Bing Cao
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  3. Qinghua Hu
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Correspondence to Qinghua Hu.

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Appendices

Appendix A: Discussions on Discriminator

1.1 A.1: Comparison with Multi-Scale Discriminator

Table 8 The comparisons of multi-scale discriminators and our AE-GAN on T1 modality of BraTS2020 dataset
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Some previous methods (Wang et al., 2018a; Park et al., 2019) introduce multiple discriminators to discriminate images in different resolutions to enhance the discriminant capability and further improve the image synthetic quality. Here, we compare our model with Pix2PixHD (Wang et al., 2018a), which utilizes multi-scale generators and multi-scale discriminators. The results are shown in Table 8. Compared to the non-multi-scale version of Pix2Pix, Pix2PixHD synthesizes images with more precise details and higher quantitative scores. But there is still a considerable gap in comparison with our AE-GAN, which demonstrates the effectiveness of our model.

1.2 A.2: Comparison with Residual Discriminator

Recently, some previous works (Han et al., 2022; Kim & Myung, 2018; Xu et al., 2019) also adapt autoencoder in the generative tasks. Han et al. (2022) proposed a novel algorithm (AE-StyleGAN) to train a style-based autoencoder along with the generator by using a residual discriminator. Kim and Myung (2018) combined the autoencoder with the GAN model to synthesize the encoded vectors and used the discriminator to discriminate the vectors instead of images. Xu et al. (2019) also adapted an autoencoder to drive the generator to learn the latent codes, which used two discriminators to discriminate the latent codes and the reconstruction results. To further verify the effectiveness of our discriminator’s architecture, we have conducted experiments to replace our discriminator with the most recent residual discriminator in AE-StyleGAN (Han et al., 2022). As shown in Table 9, the performance degrades after replacing the discriminator of AE-StyleGAN. Compared with the residual discriminator, our autoencoder-based discriminator shows significant superiority, which demonstrates our effectiveness.

Table 9 The comparisons of the residual discriminator of AE-StyleGAN and the autoencoder-based discriminator of our AE-GAN on BraTS2020 dataset
Full size table
Table 10 The hyperparameter analysis of our model and all the competing methods on the BraTS2020 dataset
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Appendix B: Hyperparameter Analysis

Since some of the competing methods are not specifically designed for medical image synthesis, their performance might be decreased by using the default parameters. For fairer comparisons, we tune the key hyperparameters of all the competing methods and choose the best results for the presentation. For Pix2Pix, CycleGAN, StarGAN, Hi-Net, CollaGAN, and our Auto-GAN and AE-GAN, they both contain the pixel-level reconstruction loss constraints on the generators, the weights of which are important hyperparameters. As most methods set this hyperparameter to 10 or 100 by default, we tune this parameter in the following five values: 1, 10, 50, 100, and 200. For CUT, we tune the hyperparameter of PatchNCE loss weight \(\lambda _X\) and \(\lambda _Y\). For TSIT, we tune the hyperparameter of perceptual loss \(\lambda _p\) and feature matching loss \(\lambda _{FM}\). The default weight of these hyperparameters is 1, so we tune these parameters in 0.1, 0.5, 1, 5, 10. Besides, for our AE-GAN, we also tune the parameter \(\lambda \) of self-representation loss. The results are shown in Table 10. According to the experimental results, the hyperparameter of Pix2Pix remains default as 100, CycleGAN, StarGAN, and Hi-Net are tuned to 50, CollaGAN and Auto-GAN are tuned to 200, CUT and TSIT are tuned to 10. The two hyperparameters \(\lambda \) and \(\gamma \) of our AE-GAN are set to 100 and 0.1, separately.

Fig. 8
figure 8

The qualitative comparisons of different imaging orientations on T2 modality of ISLES2015 dataset

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Appendix C: Discussion on Imaging Orientations

For the ISLES2015 dataset, T2 modality is acquired in sagittal orientation, which results in relatively limited in-plane resolution in axial orientation compared to other modalities that are acquired in axial orientation. We conduct experiments to translate images that are acquired from one orientation to those that are acquired from another orientation, as shown in Fig. 8. Due to the in-plane resolution difference, the synthetic results show more blurry and inaccurate details. We have also provided quantitative evaluation in Table 11, the performance of translating the high-resolution image to the low-resolution is significantly better than the opposite experiment. This might be because the high-resolution plane provides more accurate information than the low-resolution plane, which improves the synthetic quality.

Table 11 The quantitative comparisons of different imaging orientations on T2 modality of ISLES2015 dataset
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Cao, B., Bi, Z., Hu, Q. et al. AutoEncoder-Driven Multimodal Collaborative Learning for Medical Image Synthesis. Int J Comput Vis 131, 1995–2014 (2023). https://doi.org/10.1007/s11263-023-01791-0

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