Causal recurrent intervention for cross-modal cardiac image segmentation

IF 5.4 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Qixin Lin , Saidi Guo , Heye Zhang , Zhifan Gao
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引用次数: 0

Abstract

Cross-modal cardiac image segmentation is essential for cardiac disease analysis. In diagnosis, it enables clinicians to obtain more precise information about cardiac structure or function for potential signs by leveraging specific imaging modalities. For instance, cardiovascular pathologies such as myocardial infarction and congenital heart defects require precise cross-modal characterization to guide clinical decisions. The growing adoption of cross-modal segmentation in clinical research underscores its technical value, yet annotating cardiac images with multiple slices is time-consuming and labor-intensive, making it difficult to meet clinical and deep learning demands. To reduce the need for labels, cross-modal approaches could leverage general knowledge from multiple modalities. However, implementing a cross-modal method remains challenging due to cross-domain confounding. This challenge arises from the intricate effects of modality and view alterations between images, including inconsistent high-dimensional features. The confounding complicates the causality between the observation (image) and the prediction (label), thereby weakening the domain-invariant representation. Existing disentanglement methods face difficulties in addressing the confounding due to the insufficient depiction of the relationship between latent factors. This paper proposes the causal recurrent intervention (CRI) method to overcome the above challenge. It establishes a structural causal model that allows individual domains to maintain causal consistency through interventions. The CRI method integrates diverse high-dimensional variations into a singular causal relationship by embedding image slices into a sequence. This approach further distinguishes stable and dynamic factors from the sequence, subsequently separating the stable factor into modal and view factors and establishing causal connections between them. It then learns the dynamic factor and the view factor from the observation to obtain the label. Experimental results on cross-modal cardiac images of 1697 examples show that the CRI method delivers promising and productive cross-modal cardiac image segmentation performance.
交叉模态心脏图像分割的因果复发干预
跨模态心脏图像分割对于心脏疾病分析至关重要。在诊断中,它能让临床医生通过利用特定的成像模式,获得更精确的心脏结构或功能信息,以发现潜在的征兆。例如,心肌梗塞和先天性心脏缺陷等心血管疾病需要精确的跨模态特征描述来指导临床决策。临床研究中越来越多地采用跨模态分割技术,这凸显了它的技术价值,但对多切片心脏图像进行标注耗时耗力,难以满足临床和深度学习的需求。为了减少对标签的需求,跨模态方法可以利用来自多种模态的一般知识。然而,由于跨域混杂,实施跨模态方法仍然具有挑战性。这一挑战源于图像之间模态和视图变化的复杂影响,包括不一致的高维特征。混杂使观察(图像)和预测(标签)之间的因果关系变得复杂,从而削弱了域不变表示。由于对潜在因素之间的关系描述不足,现有的解纠缠方法在解决混杂问题时面临困难。本文提出了因果循环干预(CRI)方法来克服上述难题。它建立了一个结构因果模型,允许单个领域通过干预保持因果一致性。CRI 方法通过将图像切片嵌入序列,将多样化的高维变化整合为单一的因果关系。该方法进一步区分序列中的稳定因素和动态因素,随后将稳定因素分离为模式因素和视图因素,并在它们之间建立因果联系。然后,它从观察结果中学习动态因子和视图因子,从而获得标签。对 1697 个实例的跨模态心脏图像进行的实验结果表明,CRI 方法具有良好的跨模态心脏图像分割性能和成果。
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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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