Zhang Xuzhe, Angelini Elsa D, Hoffman Eric A, Watson Karol E, Smith Benjamin M, Barr R Graham, Laine Andrew F
Department of Biomedical Engineering, Columbia University, New York, NY, USA.
LTCI, Télécom Paris, Institut Polytechnique de Paris, Paris, France.
Proc IEEE Int Symp Biomed Imaging. 2024 May;2024. doi: 10.1109/isbi56570.2024.10635299. Epub 2024 Aug 22.
Robust quantification of pulmonary emphysema on computed tomography (CT) remains challenging for large-scale research studies that involve scans from different scanner types and for translation to clinical scans. Although the domain shifts in different CT scanners are subtle compared to shifts existing in other modalities (e.g., MRI) or cross-modality, emphysema is highly sensitive to it. Such subtle difference limits the application of general domain adaptation methods, such as image translation-based methods, as the contrast difference is too subtle to be distinguished. Existing studies have explored several directions to tackle this challenge, including density correction, noise filtering, regression, hidden Markov measure field (HMMF) model-based segmentation, and volume-adjusted lung density. Despite some promising results, previous studies either required a tedious workflow or eliminated opportunities for downstream emphysema subtyping, limiting efficient adaptation on a large-scale study. To alleviate this dilemma, we developed an end-to-end deep learning framework based on an existing HMMF segmentation framework. We first demonstrate that a regular UNet cannot replicate the existing HMMF results because of the lack of scanner priors. We then design a novel domain attention block, a simple yet efficient cross-modal block to fuse image visual features with quantitative scanner priors (a sequence), which significantly improves the results.
对于涉及不同扫描仪类型扫描的大规模研究以及向临床扫描的转化而言，在计算机断层扫描（CT）上对肺气肿进行稳健的定量分析仍然具有挑战性。尽管与其他模态（如MRI）或跨模态中存在的差异相比，不同CT扫描仪中的域偏移较为细微，但肺气肿对其高度敏感。这种细微差异限制了一般域适应方法的应用，如图像翻译方法，因为对比度差异过于细微难以区分。现有研究探索了几个方向来应对这一挑战，包括密度校正、噪声过滤、回归、基于隐马尔可夫测度场（HMMF）模型的分割以及体积调整后的肺密度。尽管取得了一些有前景的结果，但先前的研究要么需要繁琐的工作流程，要么消除了下游肺气肿亚型分类的机会，限制了大规模研究中的有效适应。为缓解这一困境，我们基于现有的HMMF分割框架开发了一个端到端的深度学习框架。我们首先证明，由于缺乏扫描仪先验知识，常规的UNet无法复制现有的HMMF结果。然后，我们设计了一种新颖的域注意力模块，这是一个简单而有效的跨模态模块，用于将图像视觉特征与定量扫描仪先验知识（一个序列）融合，从而显著改善结果。
