Automatic phase prediction from low-level surgical activities

Purpose: Analyzing surgical activities has received a growing interest in recent years. Several methods have been proposed to identify surgical activities and surgical phases from data acquired in operating rooms. These context-aware systems have multiple applications including: supporting the surgical team during the intervention, improving the automatic monitoring, designing new teaching paradigms. Methods: In this paper, we use low-level recordings of the activities that are performed by a surgeon to automatically predict the current (high-level) phase of the surgery. We augment a decision tree algorithm with the ability to consider the local context of the surgical activities and a hierarchical clustering algorithm. Results: Experiments were performed on 22 surgeries of lumbar disk herniation. We obtained an overall precision of 0.843 in detecting phases of 51,489 single activities. We also assess the robustness of the method with regard to noise. Conclusion: We show that using the local context allows us to improve the results compared with methods only considering single activity. Experiments show that the use of the local context makes our method very robust to noise and that clustering the input data first improves the predictions

Parsing human skeletons in an operating room

Multiple human pose estimation is an important yet challenging problem. In an Operating Room (OR) environment, the 3D body poses of surgeons and medical staff can provide important clues for surgical workflow analysis. For that purpose, we propose an algorithm for localizing and recovering body poses of multiple human in an OR environment under a multi-camera setup. Our model builds on 3D Pictorial Structures (3DPS) and 2D body part localization across all camera views, using Convolutional Neural Networks (ConvNets). To evaluate our algorithm, we introduce a dataset captured in a real OR environment. Our dataset is unique, challenging and publicly available with annotated ground truths. Our proposed algorithm yields to promising pose estimation results on this dataset

Kontextsensitivität für den Operationssaal der Zukunft

The operating room of the future is a topic of high interest. In this thesis, which is among the first in the recently defined field of Surgical Data Science, three major topics for automated context awareness in the OR of the future will be examined: improved surgical workflow analysis, the newly developed event impact factors, and as application combining these and other concepts the unified surgical display.Der Operationssaal der Zukunft ist ein Forschungsfeld von großer Bedeutung. In dieser Dissertation, die eine der ersten im kürzlich definierten Bereich „Surgical Data Science“ ist, werden drei Themen für die automatisierte Kontextsensitivität im OP der Zukunft untersucht: verbesserte chirurgische Worflowanalyse, die neuentwickelten „Event Impact Factors“ und als Anwendungsfall, der diese Konzepte mit anderen kombiniert, das vereinheitlichte chirurgische Display

Kontextsensitivität für den Operationssaal der Zukunft

The operating room of the future is a topic of high interest. In this thesis, which is among the first in the recently defined field of Surgical Data Science, three major topics for automated context awareness in the OR of the future will be examined: improved surgical workflow analysis, the newly developed event impact factors, and as application combining these and other concepts the unified surgical display.Der Operationssaal der Zukunft ist ein Forschungsfeld von großer Bedeutung. In dieser Dissertation, die eine der ersten im kürzlich definierten Bereich „Surgical Data Science“ ist, werden drei Themen für die automatisierte Kontextsensitivität im OP der Zukunft untersucht: verbesserte chirurgische Worflowanalyse, die neuentwickelten „Event Impact Factors“ und als Anwendungsfall, der diese Konzepte mit anderen kombiniert, das vereinheitlichte chirurgische Display

Situation Interpretation for Knowledge- and Model Based Laparoscopic Surgery

To manage the influx of information into surgical practice, new man-machine interaction methods are necessary to prevent information overflow. This work presents an approach to automatically segment surgeries into phases and select the most appropriate pieces of information for the current situation. This way, assistance systems can adopt themselves to the needs of the surgeon and not the other way around

Workflow-based Context-aware Control of Surgical Robots

Surgical assistance system such as medical robots enhanced the capabilities of medical procedures in the last decades. This work presents a new perspective on the use of workflows with surgical robots in order to improve the technical capabilities and the ease of use of such systems. This is accomplished by a 3D perception system for the supervision of the surgical operating room and a workflow-based controller, that allows to monitor the surgical process using workflow-tracking techniques