Cardiac coronary angiography is a major technique that assists physicians during interventional heart surgery.Under X-ray irradiation,the physician injects a contrast agent through a catheter and determines the corona...Cardiac coronary angiography is a major technique that assists physicians during interventional heart surgery.Under X-ray irradiation,the physician injects a contrast agent through a catheter and determines the coronary arteries’state in real time.However,to obtain a more accurate state of the coronary arteries,physicians need to increase the fre-quency and intensity of X-ray exposure,which will inevitably increase the potential for harm to both the patient and the surgeon.In the work reported here,we use advanced deep learning algorithms to fi nd a method of frame interpola-tion for coronary angiography videos that reduces the frequency of X-ray exposure by reducing the frame rate of the coronary angiography video,thereby reducing X-ray-induced damage to physicians.We established a new coronary angiography image group dataset containing 95,039 groups of images extracted from 31 videos.Each group includes three consecutive images,which are used to train the video interpolation network model.We apply six popular frame interpolation methods to this dataset to confi rm that the video frame interpolation technology can reduce the video frame rate and reduce exposure of physicians to X-rays.展开更多
A popular and challenging task in video research,frame interpolation aims to increase the frame rate of video.Most existing methods employ a fixed motion model,e.g.,linear,quadratic,or cubic,to estimate the intermedia...A popular and challenging task in video research,frame interpolation aims to increase the frame rate of video.Most existing methods employ a fixed motion model,e.g.,linear,quadratic,or cubic,to estimate the intermediate warping field.However,such fixed motion models cannot well represent the complicated non-linear motions in the real world or rendered animations.Instead,we present an adaptive flow prediction module to better approximate the complex motions in video.Furthermore,interpolating just one intermediate frame between consecutive input frames may be insufficient for complicated non-linear motions.To enable multi-frame interpolation,we introduce the time as a control variable when interpolating frames between original ones in our generic adaptive flow prediction module.Qualitative and quantitative experimental results show that our method can produce high-quality results and outperforms the existing stateof-the-art methods on popular public datasets.展开更多
文摘Cardiac coronary angiography is a major technique that assists physicians during interventional heart surgery.Under X-ray irradiation,the physician injects a contrast agent through a catheter and determines the coronary arteries’state in real time.However,to obtain a more accurate state of the coronary arteries,physicians need to increase the fre-quency and intensity of X-ray exposure,which will inevitably increase the potential for harm to both the patient and the surgeon.In the work reported here,we use advanced deep learning algorithms to fi nd a method of frame interpola-tion for coronary angiography videos that reduces the frequency of X-ray exposure by reducing the frame rate of the coronary angiography video,thereby reducing X-ray-induced damage to physicians.We established a new coronary angiography image group dataset containing 95,039 groups of images extracted from 31 videos.Each group includes three consecutive images,which are used to train the video interpolation network model.We apply six popular frame interpolation methods to this dataset to confi rm that the video frame interpolation technology can reduce the video frame rate and reduce exposure of physicians to X-rays.
基金supported by the Research Grants Council of the Hong Kong Special Administrative Region,under RGC General Research Fund(Project No.CUHK 14201017)Shenzhen Science and Technology Program(No.JCYJ20180507182410327)the Science and Technology Plan Project of Guangzhou(No.201704020141)。
文摘A popular and challenging task in video research,frame interpolation aims to increase the frame rate of video.Most existing methods employ a fixed motion model,e.g.,linear,quadratic,or cubic,to estimate the intermediate warping field.However,such fixed motion models cannot well represent the complicated non-linear motions in the real world or rendered animations.Instead,we present an adaptive flow prediction module to better approximate the complex motions in video.Furthermore,interpolating just one intermediate frame between consecutive input frames may be insufficient for complicated non-linear motions.To enable multi-frame interpolation,we introduce the time as a control variable when interpolating frames between original ones in our generic adaptive flow prediction module.Qualitative and quantitative experimental results show that our method can produce high-quality results and outperforms the existing stateof-the-art methods on popular public datasets.
