Surgical Phase Recognition of Short Video Shots Based on Temporal Modeling of Deep Features

Recognizing the phases of a laparoscopic surgery (LS) operation form its video constitutes a fundamental step for efficient content representation, indexing and retrieval in surgical video databases. In the literature, most techniques focus on phase segmentation of the entire LS video using hand-crafted visual features, instrument usage signals, and recently convolutional neural networks (CNNs). In this paper we address the problem of phase recognition of short video shots (10s) of the operation, without utilizing information about the preceding/forthcoming video frames, their phase labels or the instruments used. We investigate four state-of-the-art CNN architectures (Alexnet, VGG19, GoogleNet, and ResNet101), for feature extraction via transfer learning. Visual saliency was employed for selecting the most informative region of the image as input to the CNN. Video shot representation was based on two temporal pooling mechanisms. Most importantly, we investigate the role of 'elapsed time' (from the beginning of the operation), and we show that inclusion of this feature can increase performance dramatically (69% vs. 75% mean accuracy). Finally, a long short-term memory (LSTM) network was trained for video shot classification based on the fusion of CNN features with 'elapsed time', increasing the accuracy to 86%. Our results highlight the prominent role of visual saliency, long-range temporal recursion and 'elapsed time' (a feature so far ignored), for surgical phase recognition.Comment: 6 pages, 4 figures, 6 table

2017 Robotic Instrument Segmentation Challenge

In mainstream computer vision and machine learning, public datasets such as ImageNet, COCO and KITTI have helped drive enormous improvements by enabling researchers to understand the strengths and limitations of different algorithms via performance comparison. However, this type of approach has had limited translation to problems in robotic assisted surgery as this field has never established the same level of common datasets and benchmarking methods. In 2015 a sub-challenge was introduced at the EndoVis workshop where a set of robotic images were provided with automatically generated annotations from robot forward kinematics. However, there were issues with this dataset due to the limited background variation, lack of complex motion and inaccuracies in the annotation. In this work we present the results of the 2017 challenge on robotic instrument segmentation which involved 10 teams participating in binary, parts and type based segmentation of articulated da Vinci robotic instruments

Escape of surgical smoke particles, comparing conventional and valveless trocar systems

Background: During minimal access surgery, surgical smoke is produced which can potentially be inhaled by the surgical team, leading to several health risks. This smoke can escape from the abdominal cavity into the operating room due to trocar leakage. The trocars and insufflator that are used during surgery influence gas leakage. Therefore, this study compares particle escape from a valveless (Conmed AirSeal iFS), and a conventional (Karl Storz Endoflator) system. Materials and methods:Using an in vitro model, a conventional and a valveless trocar system were compared. A protocol that simulated various surgical phases was defined to assess the surgical conditions and particle leakage. Insufflation pressures and instrument diameters were varied as these are known to affect gas leakage. Results: The conventional trocar leaked during two distinct phases. Removal of the obturator caused a sudden release of particles. During instrument insertion, an average of 211 (IQR 111) particles per second escaped when using the 5 mm diameter instrument. With the 10 mm instrument, 50 (IQR 13) particles per second were measured. With the conventional trocar, a higher abdominal pressure increased particle leakage. The valveless trocar demonstrated a continuously high particle release during all phases. After the obturator was removed, particle escape increased sharply. Particle escape decreased to 1276 (IQR 580) particles per second for the 5 mm instrument insertion, and 1084 (IQR 630) particles per second for 10 mm instrument insertion. With the valveless trocar system, a higher insufflation pressure lowered particle escape. Conclusions: This study shows that a valveless trocar system releases more particles into the operating room environment than a conventional trocar. During instrument insertion, the leakage through the valveless system is 6 to 20 times higher than the conventional system. With a valveless trocar, leakage decreases with increasing pressure. With both trocar types leakage depends on instrument diameter.</p

Towards an Accurate Tracking of Liver Tumors for Augmented Reality in Robotic Assisted Surgery

International audienceThis article introduces a method for tracking the internal structures of the liver during robot-assisted procedures. Vascular network, tumors and cut planes, computed from pre-operative data, can be overlaid onto the laparoscopic view for image-guidance, even in the case of large motion or deformation of the organ. Compared to current methods, our method is able to precisely propagate surface motion to the internal structures. This is made possible by relying on a fast yet accurate biomechanical model of the liver combined with a robust visual tracking approach designed to properly constrain the model. Augmentation results are demonstrated on in-vivo sequences of a human liver during robotic surgery, while quantitative validation is performed on an ex-vivo porcine liver experimentation. Validation results show that our approach gives an accurate surface registration with an error of less than 6mm on the position of the tumor