Objectives

The accurate preoperative segmentation of the uterus and uterine fibroids from magnetic resonance images (MRI) is an essential step for diagnosis and real-time ultrasound guidance during high-intensity focused ultrasound (HIFU) surgery. Conventional supervised methods are effective techniques for image segmentation. Recently, semi-supervised segmentation approaches have been reported in the literature. One popular technique for semi-supervised methods is to use pseudo-labels to artificially annotate unlabeled data. However, many existing pseudo-label generations rely on a fixed threshold used to generate a confidence map, regardless of the proportion of unlabeled and labeled data.

Materials and Methods

To address this issue, we propose a novel semi-supervised framework called Confidence-based Threshold Adaptation Network (CTANet) to improve the quality of pseudo-labels. Specifically, we propose an online pseudo-labels method to automatically adjust the threshold, producing high-confident unlabeled annotations and boosting segmentation accuracy. To further improve the network's generalization to fit the diversity of different patients, we design a novel mixup strategy by regularizing the network on each layer in the decoder part and introducing a consistency regularization loss between the outputs of two sub-networks in CTANet.

Results

We compare our method with several state-of-the-art semi-supervised segmentation methods on the same uterine fibroids dataset containing 297 patients. The performance is evaluated by the Dice similarity coefficient, the precision, and the recall. The results show that our method outperforms other semi-supervised learning methods. Moreover, for the same training set, our method approaches the segmentation performance of a fully supervised U-Net (100% annotated data) but using 4 times less annotated data (25% annotated data, 75% unannotated data).

Conclusion

Experimental results are provided to illustrate the effectiveness of the proposed semi-supervised approach. The proposed method can contribute to multi-class segmentation of uterine regions from MRI for HIFU treatment.

中文翻译：

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CTANet：基于置信度的阈值自适应网络，用于从用于 HIFU 治疗的 MR 图像中半监督分割子宫区域

目标

从磁共振图像 (MRI) 准确术前分割子宫和子宫肌瘤是高强度聚焦超声 (HIFU) 手术期间诊断和实时超声引导的重要步骤。传统的监督方法是图像分割的有效技术。最近，文献中报道了半监督分割方法。半监督方法的一种流行技术是使用伪标签来人工注释未标记的数据。然而，许多现有的伪标签生成依赖于用于生成置信度图的固定阈值，而不管未标记和标记数据的比例如何。

材料和方法

为了解决这个问题，我们提出了一种称为基于置信度的阈值适应网络 (CTANet) 的新型半监督框架，以提高伪标签的质量。具体来说，我们提出了一种在线伪标签方法来自动调整阈值，产生高置信度的未标记注释并提高分割精度。为了进一步提高网络的泛化能力以适应不同患者的多样性，我们设计了一种新的混合策略，方法是在解码器部分对每一层的网络进行正则化，并在 CTANet 的两个子网络的输出之间引入一致性正则化损失。

结果

我们将我们的方法与包含 297 名患者的同一子宫肌瘤数据集上的几种最先进的半监督分割方法进行了比较。性能由 Dice 相似系数、精度和召回率评估。结果表明，我们的方法优于其他半监督学习方法。此外，对于相同的训练集，我们的方法接近完全监督的 U-Net（100% 注释数据）的分割性能，但使用少 4 倍的注释数据（25% 注释数据，75% 未注释数据）。

结论

提供了实验结果来说明所提出的半监督方法的有效性。所提出的方法有助于从 MRI 对子宫区域进行多类分割以进行 HIFU 治疗。