A Deep Architecture for Surgical Workflow Recognition with Edge Information Realtime surgery workflow automatic detection as computerassisted surgery systems has become an emerging trend due to improving patient safety during surgery. Currently, the convolutional neural networks can show the best performance for contentbased video analysis of surgical workflow. In this paper, a novel solution of surgery workflow detection during the procedure was presented, the edge information of original phases from video frames was extracted and then employed to train together with original phases by using a ResNet. Finally, the methods were evaluated on cataract101 dataset, a publicly available dataset for surgical phase analysis, on which a maximum accuracy of 90.1% was reached. Additionally, the accuracy of 3% improvement was achieved when compared with the method of no processing the data by edge detection. It is shown that using the edge information of original images could improve the performance of surgical phase recognition, because it can be complementary information for original images to recognize the surgical workflow. This paper shows valuable potential to develop modern medical diagnosis and treatment in automating workflow recognition, and the edge processing of original phases for recognition images can also produce new features to assist the network to recognize the original images. Furthermore, the technology studied in this paper can also be used in other video analysis tasks, or classification of image tasks.

EndtoEnd Boundary Aware Networks for Medical Image Segmentation. Fully convolutional neural networks (CNNs) have proven to be effective at representing and classifying textural information, thus transforming image intensity into output class masks that achieve semantic image segmentation. In medical image analysis, however, expert manual segmentation often relies on the boundaries of anatomical structures of interest. We propose boundary aware CNNs for medical image segmentation. Our networks are designed to account for organ boundary information, both by providing a special network edge branch and edgeaware loss terms, and they are trainable endtoend. We validate their effectiveness on the task of brain tumor segmentation using the BraTS 2018 dataset. Our experiments reveal that our approach yields more accurate segmentation results, which makes it promising for more extensive application to medical image segmentation.