TLF:从不可靠的标记 CTA 扫描中分割颅内动脉瘤的三重学习框架

IF 5.4 2区 医学 Q1 ENGINEERING, BIOMEDICAL Computerized Medical Imaging and Graphics Pub Date : 2024-07-26 DOI:10.1016/j.compmedimag.2024.102421
Lei Chai , Shuangqian Xue , Daodao Tang , Jixin Liu , Ning Sun , Xiujuan Liu
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引用次数: 0

摘要

颅内动脉瘤(IA)是一种对人类健康构成重大威胁的常见疾病。使用计算机断层扫描血管造影术(CTA)作为动脉瘤的诊断工具仍然耗时且具有挑战性。深度神经网络(DNN)在医学图像分割领域取得了重大进展。然而,大规模 DNNs 的训练需要大量高质量的标记数据,因此对大量脑部 CTA 扫描进行标注是一项极具挑战性的工作。为了应对这些挑战,并从大量未标注的训练数据中有效地开发出稳健的 IAs 分割模型,我们提出了一个三重学习框架(TLF)。该框架主要包括三种学习范式:伪监督学习、对比学习和自信学习。本文引入了增强的平均教师模型和体素选择策略,在不可靠的标注训练数据上进行伪监督学习。同时,我们在高级语义特征空间中构建了正负训练对,通过对比学习提高了 TLF 的整体学习效率。此外,我们还提出了一种多尺度自信学习方法来纠正不可靠标签,从而获取更广泛的局部结构信息,而不是依赖单个体素。为了评估我们方法的有效性,我们在一个自建的数据库中进行了大量实验,该数据库包含数百例带有 IAs 的脑 CTA 扫描。实验结果表明,我们的方法能利用不可靠的标记数据有效地学习基于CTA扫描的IAs分割模型,在分割准确率方面优于最先进的方法。代码发布于 https://github.com/XueShuangqian/TLF。
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TLF: Triple learning framework for intracranial aneurysms segmentation from unreliable labeled CTA scans

Intracranial aneurysm (IA) is a prevalent disease that poses a significant threat to human health. The use of computed tomography angiography (CTA) as a diagnostic tool for IAs remains time-consuming and challenging. Deep neural networks (DNNs) have made significant advancements in the field of medical image segmentation. Nevertheless, training large-scale DNNs demands substantial quantities of high-quality labeled data, making the annotation of numerous brain CTA scans a challenging endeavor. To address these challenges and effectively develop a robust IAs segmentation model from a large amount of unlabeled training data, we propose a triple learning framework (TLF). The framework primarily consists of three learning paradigms: pseudo-supervised learning, contrastive learning, and confident learning. This paper introduces an enhanced mean teacher model and voxel-selective strategy to conduct pseudo-supervised learning on unreliable labeled training data. Concurrently, we construct the positive and negative training pairs within the high-level semantic feature space to improve the overall learning efficiency of the TLF through contrastive learning. In addition, a multi-scale confident learning is proposed to correct unreliable labels, which enables the acquisition of broader local structural information instead of relying on individual voxels. To evaluate the effectiveness of our method, we conducted extensive experiments on a self-built database of hundreds of cases of brain CTA scans with IAs. Experimental results demonstrate that our method can effectively learn a robust CTA scan-based IAs segmentation model using unreliable labeled data, outperforming state-of-the-art methods in terms of segmentation accuracy. Codes are released at https://github.com/XueShuangqian/TLF.

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来源期刊
CiteScore
10.70
自引率
3.50%
发文量
71
审稿时长
26 days
期刊介绍: The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.
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