  The purpose of this study is to differentiate malignant and benign
mediastinal lesions by using the three-dimensional convolutional neural network
through the endobronchial ultrasound (EBUS) image. Compared with previous
study, our proposed model is robust to noise and able to fuse various imaging
features and spatiotemporal features of EBUS videos. Endobronchial
ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a diagnostic
tool for intrathoracic lymph nodes. Physician can observe the characteristics
of the lesion using grayscale mode, doppler mode, and elastography during the
procedure. To process the EBUS data in the form of a video and appropriately
integrate the features of multiple imaging modes, we used a time-series
three-dimensional convolutional neural network (3D CNN) to learn the
spatiotemporal features and design a variety of architectures to fuse each
imaging mode. Our model (Res3D_UDE) took grayscale mode, Doppler mode, and
elastography as training data and achieved an accuracy of 82.00% and area under
the curve (AUC) of 0.83 on the validation set. Compared with previous study, we
directly used videos recorded during procedure as training and validation data,
without additional manual selection, which might be easier for clinical
application. In addition, model designed with 3D CNN can also effectively learn
spatiotemporal features and improve accuracy. In the future, our model may be
used to guide physicians to quickly and correctly find the target lesions for
slice sampling during the inspection process, reduce the number of slices of
benign lesions, and shorten the inspection time.
