Multi-Input and Dataset-Invariant Adversarial Learning (MDAL) for Left and Right-Ventricular Coverage Estimation in Cardiac MRI

Zhang, Le; Pereañez, Marco; Neubauer, Stefan; Petersen, Steffen E.; Frangi, Alejandro F.

doi:10.1007/978-3-030-00934-2_54

Cited by 17 publications

(9 citation statements)

References 10 publications

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“…For example, Kamnitsas et al [37] made the earliest attempts to align feature distributions with an adversarial loss for unsupervised domain adaptation cross-protocol MRI segmentation and achieved promising adaptation performance. Degel et al [38] and Zhang et al [39] combined regularization with adversarial training and obtain better adaptation results on ultrasound datasets and cardiac MRI segmentation, respectively. Wang et al [40] presented a novel patch-based output space adversarial learning framework to jointly and robustly segment the optic disc and optic cup from different fundus image datasets and achieved effective feature alignment.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Domain Adaptation Network With Category-Centric Prototype Aligner for Biomedical Image Segmentation

Gong

Sun

et al. 2021

IEEE Access

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With the widespread success of deep learning in biomedical image segmentation, domain shift becomes a critical and challenging problem, as the gap between two domains can severely affect model performance when deployed to unseen data with heterogeneous features. To alleviate this problem, we present a novel unsupervised domain adaptation network, for generalizing models learned from the labeled source domain to the unlabeled target domain for cross-modality biomedical image segmentation. Specifically, our approach consists of two key modules, a conditional domain discriminator (CDD) and a category-centric prototype aligner (CCPA). The CDD, extended from conditional domain adversarial networks in classifier tasks, is effective and robust in handling complex cross-modality biomedical images. The CCPA, improved from the graph-induced prototype alignment mechanism in cross-domain object detection, can exploit precise instance-level features through an elaborate prototype representation. In addition, it can address the negative effect of class imbalance via entropy-based loss. Extensive experiments on a public benchmark for the cardiac substructure segmentation task demonstrate that our method significantly improves performance on the target domain. INDEX TERMS Biomedical image segmentation, cross-modality learning, unsupervised domain adaptation, category-centric prototype aligner.

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Section: Related Workmentioning

confidence: 99%

Unsupervised Domain Adaptation Network With Category-Centric Prototype Aligner for Biomedical Image Segmentation

Gong

Sun

et al. 2021

IEEE Access

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“…4,280 sequences correspond to quality score 1 for both ventricles, which indicates full coverage of the heart from base to apex, and form the source datasets to construct the ground-truth distance label for our experiments. Note that having full coverage should not be confused with having the top/bottom slices corresponding exactly to base/apex [13].…”

Section: Experiments and Analysismentioning

confidence: 99%

Missing Slice Imputation in Population CMR Imaging via Conditional Generative Adversarial Nets

Pereañez

Bowles

et al. 2019

Lecture Notes in Computer Science

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Accurate ventricular volume measurements depend on complete heart coverage in cardiac magnetic resonance (CMR) from where most immediate indicators of normal/abnormal cardiac function are available non-invasively. However, incomplete coverage, especially missing basal or apical slices in CMR sequences is insufficiently addressed in population imaging and current clinical research studies yet has important impact on volume calculation accuracy. In this work, we propose a new deep architecture, coined Missing Slice Imputation Generative Adversarial Network (MSIGAN), to learn key features of cardiac shortaxis (SAX) slices across different positions, and use them as conditional variables to effectively infer missing slices in the query volumes. In MSI-GAN, the slices are first mapped to latent vectors with position features through a regression net. The latent vector corresponding to the desired position is then projected onto the slice manifold conditional on slice intensity through a generator net. The latent vector along with the slice features (i.e., intensity) and desired position control the generation vs. regression. Two adversarial networks are imposed on the regressor and generator, encouraging more realistic slices. Experimental results show that our method outperforms the previous state-of-the-art in missing slice imputation for cardiac MRI.

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“…To address the domain shift problem, unsupervised domain adaptation (UDA) has been an active research topic. Existing UDA methods typically require to simultaneously access the source and target data for distribution alignment [2,10,11,16,20,[23][24][25][26][27]. However, in real-world scenarios, the source data are often inaccessible during model adaptation in the target domain, because medical data are strictly regulated and prohibitive to be shared before taking complex ethical procedures.…”

Section: Introductionmentioning

confidence: 99%

Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling

Chen¹,

Liu²,

Jin³

et al. 2021

Preprint

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Domain adaptation typically requires to access source domain data to utilize their distribution information for domain alignment with the target data. However, in many real-world scenarios, the source data may not be accessible during the model adaptation in the target domain due to privacy issue. This paper studies the practical yet challenging source-free unsupervised domain adaptation problem, in which only an existing source model and the unlabeled target data are available for model adaptation. We present a novel denoised pseudo-labeling method for this problem, which effectively makes use of the source model and unlabeled target data to promote model self-adaptation from pseudo labels. Importantly, considering that the pseudo labels generated from source model are inevitably noisy due to domain shift, we further introduce two complementary pixel-level and class-level denoising schemes with uncertainty estimation and prototype estimation to reduce noisy pseudo labels and select reliable ones to enhance the pseudo-labeling efficacy. Experimental results on cross-domain fundus image segmentation show that without using any source images or altering source training, our approach achieves comparable or even higher performance than stateof-the-art source-dependent unsupervised domain adaptation methods 1 .

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Multi-Input and Dataset-Invariant Adversarial Learning (MDAL) for Left and Right-Ventricular Coverage Estimation in Cardiac MRI

Abstract: This is a repository copy of Multi-input and dataset-invariant adversarial learning (MDAL) for left and right-ventricular coverage estimation in cardiac MRI.

Cited by 17 publications

References 10 publications

Unsupervised Domain Adaptation Network With Category-Centric Prototype Aligner for Biomedical Image Segmentation

Unsupervised Domain Adaptation Network With Category-Centric Prototype Aligner for Biomedical Image Segmentation

Missing Slice Imputation in Population CMR Imaging via Conditional Generative Adversarial Nets

Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling

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