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.

翻译：肺栓塞（PE）是导致心血管死亡的主要原因之一。尽管计算机断层肺动脉造影（CTPA）作为医学影像学手段是诊断PE的金标准，但仍易出现误诊或显著诊断延迟，这对危重病例可能致命。尽管深度学习近年来已被证明能为广泛医学影像任务带来显著的性能提升，但关于肺栓塞自动检测的已发表研究仍十分有限。本文提出一种基于深度学习的方法，该方法高效结合计算机视觉与深度神经网络，用于CTPA中的肺栓塞检测。我们的方法在三个正交维度上实现了创新改进：1）解剖结构的自动检测；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结果。