Background Coronary artery angiography is an indispensable assistive technique for cardiac interventional surgery. Segmentation and extraction of blood vessels from coronary angiographic images or videos are very essential prerequisites for physicians to locate, assess and diagnose the plaques and stenosis in blood vessels. Methods This article proposes a novel coronary artery segmentation framework that combines a three–dimensional (3D) convolutional input layer and a two–dimensional (2D) convolutional network. Instead of a single input image in the previous medical image segmentation applications, our framework accepts a sequence of coronary angiographic images as input, and outputs the clearest mask of segmentation result. The 3D input layer leverages the temporal information in the image sequence, and fuses the multiple images into more comprehensive 2D feature maps. The 2D convolutional network implements down–sampling encoders, up–sampling decoders, bottle–neck modules, and skip connections to accomplish the segmentation task. Results The spatial–temporal model of this article obtains good segmentation results despite the poor quality of coronary angiographic video sequences, and outperforms the state–of–the–art techniques. Conclusions The results justify that making full use of the spatial and temporal information in the image sequences will promote the analysis and understanding of the images in videos.

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In this work, experimental isobaric vapor–liquid equilibrium data were measured for the ethyl acetate (EAC) + 2-ethylhexanoic acid (EA) and propyl acetate (PAC) + EA systems at atmospheric pressure. The experimental data were in good agreement with the NRTL-HOC, UNIQUAC-HOC, and Wilson-HOC models. The maximum absolute deviations and mean absolute deviations of temperature were less than 0.94 and 0.80, respectively. The maximum absolute deviations and mean absolute deviations of the mole fraction of the vapor phase were less than 0.0094 and 0.0067, respectively. The Van Ness method was used to check the thermodynamic consistency of the experimental data. In addition, the binary interaction parameters for the EAC + EA and PAC + EA systems were obtained.

We have been developing a paradigm that we call learning-from-observation for a robot to automatically acquire a robot program to conduct a series of operations, or for a robot to understand what to do, through observing humans performing the same operations. Since a simple mimicking method to repeat exact joint angles or exact end-effector trajectories does not work well because of the kinematic and dynamic differences between a human and a robot, the proposed method employs intermediate symbolic representations, tasks, for conceptually representing what-to-do through observation. These tasks are subsequently mapped to appropriate robot operations depending on the robot hardware. In the present work, task models for upper-body operations of humanoid robots are presented, which are designed on the basis of Labanotation. Given a series of human operations, we first analyze the upper-body motions and extract certain fixed poses from key frames. These key poses are translated into tasks represented by Labanotation symbols. Then, a robot performs the operations corresponding to those task models. Because tasks based on Labanotation are independent of robot hardware, different robots can share the same observation module, and only different task-mapping modules specific to robot hardware are required. The system was implemented and demonstrated that three different robots can automatically mimic human upper-body operations with a satisfactory level of resemblance.

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