背景:多器官分割是医学成像中的一项关键任务,在临床实践和研究中具有广泛的应用。从高分辨率3D医学图像中准确描绘器官,比如CT扫描,对放射治疗计划至关重要,提高治疗效果,并将辐射毒性风险降至最低。此外,它在定量图像分析中起着举足轻重的作用,支持各种医学研究。尽管意义重大,从3D图像中手动分割多个器官是劳动密集型的,并且由于操作者间的高变异性而容易再现性低。深度学习的最新进展导致了几种自动分割方法,然而,许多人严重依赖标记数据和人体解剖学专业知识。
目的:在本研究中,我们的主要目标是解决现有的用于腹部多器官分割的半监督学习(SSL)方法的局限性.我们的目标是引入一种新的SSL方法,利用未标记的数据来增强深度神经网络在分割腹部器官方面的性能。具体来说,我们提出了一种将重绘网络纳入分割过程的方法,以纠正错误并提高准确性。
方法:我们提出的方法包括三个互连的神经网络:用于图像分割的分割网络,一致性正规化的教师网络,和用于重新绘制对象的重新绘制网络。在培训期间,细分网络经历了两轮优化:基础训练和重新调整。我们采用平均教师模型作为我们的基线SSL方法,利用有标签和无标签的数据。然而,仅使用此方法识别腹部多器官分割中的重大错误,我们引入重绘网络来生成基于CT扫描的重绘图像,保留原始解剖信息。我们的方法基于生成过程假设,包括分割,绘图,和组装阶段。正确的分割对于生成准确的图像至关重要。在基础训练阶段,分割网络使用标记和未标记数据进行训练,纳入一致性学习,以确保扰动前后的一致预测。重新调整阶段的重点是通过根据重新绘制的CT图像和原始CT图像之间的差异优化分割网络参数来减少分割误差。
结果:我们使用两个公开可用的数据集评估了我们的方法:颅骨外(BTCV)分割数据集(训练:44,验证:6)和腹部多器官分割(AMOS)挑战2022数据集(训练:138,验证:16)。我们的结果与最先进的SSL方法进行了比较,包括MT和双任务一致性(DTC),使用骰子相似性系数(DSC)作为准确性度量。在这两个数据集上,我们提出的SSL方法始终优于其他方法,包括监督学习,实现各种腹部器官优越的分割性能。这些发现证明了我们方法的有效性,即使使用有限数量的标记数据。
结论:我们新颖的腹部多器官分割半监督学习方法解决了与这项任务相关的挑战。通过集成重绘网络并利用未标记的数据,我们在准确性方面取得了显着提高。与现有的SSL和监督学习方法相比,我们的方法表现出卓越的性能。这种方法在提高医学成像应用中多器官分割的精度和效率方面具有很大的前景。
BACKGROUND: Multi-organ segmentation is a critical task in medical imaging, with wide-ranging applications in both clinical practice and research. Accurate delineation of organs from high-resolution 3D medical images, such as CT scans, is essential for radiation therapy planning, enhancing treatment outcomes, and minimizing radiation toxicity risks. Additionally, it plays a pivotal role in quantitative image analysis, supporting various medical research studies. Despite its significance, manual segmentation of multiple organs from 3D images is labor-intensive and prone to low reproducibility due to high interoperator variability. Recent advancements in deep learning have led to several automated segmentation methods, yet many rely heavily on labeled data and human anatomy expertise.
OBJECTIVE: In this study, our primary objective is to address the limitations of existing semi-supervised learning (SSL) methods for abdominal multi-organ segmentation. We aim to introduce a novel SSL approach that leverages unlabeled data to enhance the performance of deep neural networks in segmenting abdominal organs. Specifically, we propose a method that incorporates a redrawing network into the segmentation process to correct errors and improve accuracy.
METHODS: Our proposed method comprises three interconnected neural networks: a segmentation network for image segmentation, a teacher network for consistency regularization, and a redrawing network for object redrawing. During training, the segmentation network undergoes two rounds of optimization: basic training and readjustment. We adopt the Mean-Teacher model as our baseline SSL approach, utilizing labeled and unlabeled data. However, recognizing significant errors in abdominal multi-organ segmentation using this method alone, we introduce the redrawing network to generate redrawn images based on CT scans, preserving original anatomical information. Our approach is grounded in the generative process hypothesis, encompassing segmentation, drawing, and assembling stages. Correct segmentation is crucial for generating accurate images. In the basic training phase, the segmentation network is trained using both labeled and unlabeled data, incorporating consistency learning to ensure consistent predictions before and after perturbations. The readjustment phase focuses on reducing segmentation errors by optimizing the segmentation network parameters based on the differences between redrawn and original CT images.
RESULTS: We evaluated our method using two publicly available datasets: the beyond the cranial vault (BTCV) segmentation dataset (training: 44, validation: 6) and the abdominal multi-organ segmentation (AMOS) challenge 2022 dataset (training:138, validation:16). Our results were compared with state-of-the-art SSL methods, including MT and dual-task consistency (DTC), using the Dice similarity coefficient (DSC) as an accuracy metric. On both datasets, our proposed SSL method consistently outperformed other methods, including supervised learning, achieving superior segmentation performance for various abdominal organs. These findings demonstrate the effectiveness of our approach, even with a limited number of labeled data.
CONCLUSIONS: Our novel semi-supervised learning approach for abdominal multi-organ segmentation addresses the challenges associated with this task. By integrating a redrawing network and leveraging unlabeled data, we achieve remarkable improvements in accuracy. Our method demonstrates superior performance compared to existing SSL and supervised learning methods. This approach holds great promise in enhancing the precision and efficiency of multi-organ segmentation in medical imaging applications.