用于CT肺动脉造影中肺栓塞检测的解剖感知双跳学习
Anatomically aware dual-hop learning for pulmonary embolism detection in CT pulmonary angiograms
AI总结：
针对CT肺动脉造影中肺栓塞检测易误诊、相关深度学习研究少的问题，提出融合计算机视觉与深度神经网络的解剖感知双跳学习方法，在RSNA数据集上取得最优结果。
摘要：
肺栓塞（PE）是心血管死亡的主要原因之一。尽管通过CT肺动脉造影（CTPA）进行的医学成像是PE诊断的金标准，但它仍容易出现误诊或明显的诊断延迟，这对重症患者可能是致命的。尽管近期研究表明深度学习在广泛的医学影像任务中能显著提升性能，但已发表的关于自动肺栓塞检测的研究仍然很少。本文提出了一种基于深度学习的方法，该方法将计算机视觉与深度神经网络有效结合，用于CTPA中的肺栓塞检测。我们的方法在三个正交维度上具有创新性改进：1）解剖结构自动检测；2）解剖感知预训练；3）用于PE检测的双跳深度神经网络。我们在公开的多中心大规模RSNA数据集上取得了当前最优的结果。
英文摘要：
Pulmonary Embolisms (PE) represent a leading cause of cardiovascular death. While medical imaging, through computed tomographic pulmonary angiography (CTPA), represents the gold standard for PE diagnosis, it is still susceptible to misdiagnosis or significant diagnosis delays, which may be fatal for critical cases. Despite the recently demonstrated power of deep learning to bring a significant boost in performance in a wide range of medical imaging tasks, there are still very few published researches on automatic pulmonary embolism detection. Herein we introduce a deep learning based approach, which efficiently combines computer vision and deep neural networks for pulmonary embolism detection in CTPA. Our method features novel improvements along three orthogonal axes: 1) automatic detection of anatomical structures; 2) anatomical aware pretraining, and 3) a dual-hop deep neural net for PE detection. We obtain state-of-the-art results on the publicly available multicenter large-scale RSNA dataset.
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