Abstract: The disclosure provides a video processing method, an apparatus, an electronic device, a computer readable storage medium, a computer program product and a computer program. The method includes: obtaining a video edit draft, the video edit draft being used to record an initial multimedia material and indication information of a first video editing operation for the initial multimedia material, the initial multimedia material comprising a video material and/or an image material; extracting the initial multimedia material from the video edit draft; determining, based on the initial multimedia material, a target edit template that matches the initial multimedia material, the target edit template being used to record indication information of a second video editing operation; and processing, based on the target edit template, the initial multimedia material according to the second video editing operation, to obtain a first target video having an editing effect of the second video editing operation.

Abstract: Described herein are systems, methods, and instrumentalities associated with processing medical chest images such as chest X-ray (CXR) images. Segmentation models derived via a deep learning process are used to segment the chest images and obtain a rib segmentation result and a lung segmentation result for each image. The rib segmentation result may include a rib sequence identified in the image while the lung segmentation result may include one or more lung fields identified in the image. The quality of each chest image (e.g., whether the image reflects a breath-holding state of the patient) may then be determined based on whether a sufficient number of ribs in the rib segmentation result overlap with the lung fields in the lung segmentation result. The segmentation results may be obtained in a coarse-to-fine manner, e.g., by first determining a large rib area and then further segmenting the large rib area to identify each individual rib.

Abstract: Described herein are systems, methods, and instrumentalities associated with processing medical chest images such as chest X-ray (CXR) images. Segmentation models derived via a deep learning process are used to segment the chest images and obtain a rib segmentation result and a lung segmentation result for each image. The rib segmentation result may include a rib sequence identified in the image while the lung segmentation result may include one or more lung fields identified in the image. The quality of each chest image (e.g., whether the image reflects a breath-holding state of the patient) may then be determined based on whether a sufficient number of ribs in the rib segmentation result overlap with the lung fields in the lung segmentation result. The segmentation results may be obtained in a coarse-to-fine manner, e.g., by first determining a large rib area and then further segmenting the large rib area to identify each individual rib.