Automatic detection of pulmonary embolism on computed tomography pulmonary angiogram scan using a three-dimensional convolutional neural network.

Objective: To propose a convolutional neural network (EmbNet) for automatic pulmonary embolism detection on computed tomography pulmonary angiogram (CTPA) scans and to assess its diagnostic performance.

Methods: 305 consecutive CTPA scans between January 2019 and December 2021 were enrolled in this study (142 for training, 163 for internal validation), and 250 CTPA scans from a public dataset were used for external validation. The framework comprised a preprocessing step to segment the pulmonary vessels and the EmbNet to detect emboli.

Co-learning of appearance and shape for precise ejection fraction estimation from echocardiographic sequences.

Accurate estimation of ejection fraction (EF) from echocardiography is of great importance for evaluation of cardiac function. It is usually obtained by the Simpson's bi-plane method based on the segmentation of the left ventricle (LV) in two keyframes. However, obtaining accurate EF estimation from echocardiography is challenging due to (1) noisy appearance in ultrasound images, (2) temporal dynamic movement of myocardium, (3) sparse annotation of the full sequence, and (4) potential quality degradation during scanning.