Cahan Noa, Marom Edith M, Soffer Shelly, Barash Yiftach, Konen Eli, Klang Eyal, Greenspan Hayit
Faculty of Engineering, Tel-Aviv University, Tel-Aviv, Israel.
Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Israel affiliated with the Tel Aviv University, Tel Aviv, Israel.
Comput Methods Programs Biomed. 2022 Jun;220:106815. doi: 10.1016/j.cmpb.2022.106815. Epub 2022 Apr 13.
Evaluation of the right ventricle (RV) is a key component of the clinical assessment of many cardiovascular and pulmonary disorders. In this work, we focus on RV strain classification from patients who were diagnosed with pulmonary embolism (PE) in computed tomography pulmonary angiography (CTPA) scans. PE is a life-threatening condition, often without warning signs or symptoms. Early diagnosis and accurate risk stratification are critical for decreasing mortality rates. High-risk PE relies on the presence of RV dysfunction resulting from acute pressure overload. PE severity classification and specifically, high-risk PE diagnosis are crucial for appropriate therapy. CTPA is the golden standard in the diagnostic workup of suspected PE. Therefore, it can link between diagnosis and risk stratification strategies.
We retrieved data of consecutive patients who underwent CTPA and were diagnosed with PE and extracted a single binary label of "RV strain biomarker" from the CTPA scan report. This label was used as a weak label for classification. Our solution applies a 3D DenseNet network architecture, further improved by integrating residual attention blocks into the network's layers.
This model achieved an area under the receiver operating characteristic curve (AUC) of 0.88 for classifying RV strain. For Youden's index, the model showed a sensitivity of 87% and specificity of 83.7%. Our solution outperforms state-of-the-art 3D CNN networks. The proposed design allows for a fully automated network that can be trained easily in an end-to-end manner without requiring computationally intensive and time-consuming preprocessing or strenuous labeling of the data.
This current solution demonstrates that a small dataset of readily available unmarked CTPAs can be used for effective RV strain classification. To our knowledge, this is the first work that attempts to solve the problem of RV strain classification from CTPA scans and this is the first work where medical images are used in such an architecture. Our generalized self-attention blocks can be incorporated into various existing classification architectures making this a general methodology that can be applied to 3D medical datasets.
右心室（RV）评估是许多心血管和肺部疾病临床评估的关键组成部分。在本研究中，我们专注于从计算机断层扫描肺动脉造影（CTPA）扫描中被诊断为肺栓塞（PE）的患者中进行右心室应变分类。肺栓塞是一种危及生命的疾病，通常没有警示体征或症状。早期诊断和准确的风险分层对于降低死亡率至关重要。高危肺栓塞依赖于急性压力过载导致的右心室功能障碍。肺栓塞严重程度分类，特别是高危肺栓塞诊断对于适当的治疗至关重要。CTPA是疑似肺栓塞诊断检查的金标准。因此，它可以将诊断与风险分层策略联系起来。
我们检索了接受CTPA并被诊断为肺栓塞的连续患者的数据，并从CTPA扫描报告中提取了一个单一的二元标签“右心室应变生物标志物”。这个标签被用作分类的弱标签。我们的解决方案应用了3D密集连接网络（DenseNet）架构，并通过将残差注意力块集成到网络层中进一步改进。
该模型在分类右心室应变时，受试者操作特征曲线（AUC）下的面积达到了0.88。对于约登指数，该模型的灵敏度为87%，特异性为83.7%。我们的解决方案优于当前最先进的3D卷积神经网络（CNN）。所提出的设计允许构建一个完全自动化的网络，该网络可以以端到端的方式轻松训练，而无需进行计算量大且耗时的预处理或对数据进行费力的标注。
当前的解决方案表明，一小批现成的未标记CTPA数据集可用于有效的右心室应变分类。据我们所知，这是第一项尝试从CTPA扫描中解决右心室应变分类问题的工作，也是第一项在这种架构中使用医学图像的工作。我们的广义自注意力块可以纳入各种现有的分类架构中，使其成为一种可应用于3D医学数据集的通用方法。
