Learning Deep Representations of Cardiac Structures for 4D Cine MRI Image Segmentation through Semi-Supervised Learning
Learning Deep Representations of Cardiac Structures for 4D Cine MRI Image Segmentation through Semi-Supervised Learning
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DOI：
10.3390/app122312163
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
S. Hasan;C. Linte
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文献类型：
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作者：
S. Hasan;C. Linte
学习医学成像任务的良好数据表示，确保保留相关信息并从数据中删除无关的信息，以改善本文的解释性。半监督学习（CQSL） - 展示了分解块，变化自动编码器（VAE），通用对手网络（GAN）的简单组合的功能，以及用于基于调节层的重建器的功能在医学成像中执行两个重要的任务：分割和重建。我们的工作是通过半监督学习的最新进展（SSL）的动机，这表现出了良好的结果，该结果有限地标记了数据和大量的无标记数据块将输入图像分解为域不变的空间因子和域特异性非空间因子。信息共享一个常见的空间空间，但在非空间空间中（强度，对比等）原始图像，我们的基于调节层的重建块重组了从通用模型中采样的随机非空间信息的空间信息。域 - 不变（空间）以及域特异性（非空间）信息，我们进一步应用结构化的L2相似性（SL2SIM）损失以及相互的信息最小化（MIM），以改善受对抗训练的通用模型更好的重建。半监督模型，即使仅使用1％标记的数据，我们的性能精度也达到了83.2％。 ，注释很昂贵，并且将公开提供实验配置。
Learning good data representations for medical imaging tasks ensures the preservation of relevant information and the removal of irrelevant information from the data to improve the interpretability of the learned features. In this paper, we propose a semi-supervised model—namely, combine-all in semi-supervised learning (CqSL)—to demonstrate the power of a simple combination of a disentanglement block, variational autoencoder (VAE), generative adversarial network (GAN), and a conditioning layer-based reconstructor for performing two important tasks in medical imaging: segmentation and reconstruction. Our work is motivated by the recent progress in image segmentation using semi-supervised learning (SSL), which has shown good results with limited labeled data and large amounts of unlabeled data. A disentanglement block decomposes an input image into a domain-invariant spatial factor and a domain-specific non-spatial factor. We assume that medical images acquired using multiple scanners (different domain information) share a common spatial space but differ in non-spatial space (intensities, contrast, etc.). Hence, we utilize our spatial information to generate segmentation masks from unlabeled datasets using a generative adversarial network (GAN). Finally, to reconstruct the original image, our conditioning layer-based reconstruction block recombines spatial information with random non-spatial information sampled from the generative models. Our ablation study demonstrates the benefits of disentanglement in holding domain-invariant (spatial) as well as domain-specific (non-spatial) information with high accuracy. We further apply a structured L2 similarity (SL2SIM) loss along with a mutual information minimizer (MIM) to improve the adversarially trained generative models for better reconstruction. Experimental results achieved on the STACOM 2017 ACDC cine cardiac magnetic resonance (MR) dataset suggest that our proposed (CqSL) model outperforms fully supervised and semi-supervised models, achieving an 83.2% performance accuracy even when using only 1% labeled data. We hypothesize that our proposed model has the potential to become an efficient semantic segmentation tool that may be used for domain adaptation in data-limited medical imaging scenarios, where annotations are expensive. Code, and experimental configurations will be made available publicly.