Raimundo Sierra

Generation of variable anatomical models for surgical training simulators

The generation of variable surgical scenes is a key element for effective training with surgery simulators. Our current research aims at a high fidelity hysteroscopy simulator which challenges the trainee with a new surgical scene in every training session. We previously reported on methods able to generate a broad range of pathologies within an existing healthy organ model. This paper presents the methods necessary to produce variable models of the healthy organ. In order to build a database of uteri, a volunteer study was conducted. The segmentation was carried out interactively, also covering the establishment of an anatomically meaningful correspondence between the individual organs. The variability of the shape parameters has been characterized by principal component analysis. A new method has been developed and tested, allowing the derivation of realistic new instances based on the stochastic model and complying with non-linear shape constraints which are defined and interactively controlled by medical experts.

Virtual reality based simulation of hysteroscopic interventions

Virtual reality based simulation is an appealing option to supplement traditional clinical education. However, the formal integration of training simulators into the medical curriculum is still lacking. Especially, the lack of a reasonable level of realism supposedly hinders the widespread use of this technology. Therefore, we try to tackle this situation with a reference surgical simulator of the highest possible fidelity for procedural training. This overview describes all elements that have been combined into our training system as well as first results of simulator validation. Our framework allows the rehearsal of several aspects of hysteroscopyfor instance, correct fluid management, handling of excessive bleeding, appropriate removal of intra-uterine tumors, or the use of the surgical instrument.

Pathology design for surgical training simulators

Realistic generation of variable anatomical organ models and pathologies are crucial for a sophisticated surgical training simulator. A training scene needs to be different in every session in order to exhaust the full potential of virtual reality based training. We previously reported on a cellular automaton able to generate leiomyomas found in the uterine cavity. This paper presents an alternative approach for the design of macroscopic findings of pathologies and describes the incorporation of these models into a healthy virtual organ. The pathologies implemented are leiomyomas and polyps protruding to different extents into the uterine cavity. The results presented are part of a virtual reality based hysteroscopy simulator that is under development.

Generation of Pathologies for Surgical Training Simulators

In the past few years virtual reality based systems have been proposed and realized for many medical interventions. These simulators have the potential to provide training on a wide variety of pathologies. So far, realistic generation of anatomical variance and pathologies have not been treated as a specific issue. We report on a cellular automaton, specially developed to generate macroscopic findings fulfilling the requirements for a sophisticated simulation. The specific pathology investigated are leiomyomas protruding to different extents into the uterine cavity. The automaton presented is part of a virtual reality based hysteroscopy simulator which is currently under development.

Coherent Scene Generation for Surgical Simulators

The idea of using computer-based surgical simulators for training of prospective surgeons has been a topic of research for more than a decade. However, surgical simulation is still far from being included into the medical curriculum. Still open questions are the level of simulation realism which is needed for effective learning, the identification of surgical skill components which are to be trained, as well as the validation of the training effect. We are striving to address these problems with a new generation of highly realistic simulators. A key element of realism is the variable training scene, reflecting differences in individual patients. In this paper we describe the complete generation process of these case-by-case scenarios.

Hydrometra simulation for VR-based hysteroscopy training

During hysteroscopy a hydrometra is maintained, i.e. the uterus is distended with liquid media to access and visualize the uterine cavity. The pressure and flow induced by the liquid are crucial tools for he gynecologists during surgery to obtain a clear view of the operation site. This paper presents two different aspects of hydrometra simulation, namely the distension of the uterine muscle and the liquid flow simulation in the cavity. The deformation of the organs shape is computed offline based on finite element calculations whereas the flow is approximated on the fly by solving the simplified Navier-Stokes equations. The real-time capabilities of the presented algorithms as well as the level of fidelity achieved by the proposed methods are discussed.

Pathology Growth Model Based on Particles

Virtual reality based surgical simulators offer the possibility to provide training on a wide range of findings of different pathologies. Current research aims at a high fidelity hysteroscopy simulator. Different methods for the generation of pathologies have been investigated to realize the first surgical simulator that challenges the trainee with a new scene in every training session. In this paper, a particles-based tumor growth model is presented that overcomes different limitations of previous approaches. It allows for a realistic generation of both polyps and myomas protruding to different extents into the uterine cavity. The model incorporates several biological as well as mechanical factors, which influence the growth process and thus the appearance of the pathologies.

Evaluation of different pathology generation strategies for surgical training simulators

Abstract During the last few years, several surgical training simulators have been proposed. One of the main advantages of these simulators is the ability to provide riskless training on a wide range of different cases in a compressed period of time. Therefore, the generation of variable surgical scenes is a crucial component of a simulator. This paper compares three different approaches for the generation of pathologies specifically suited for surgical training simulators. The generated models can be embedded in the healthy organ model to challenge the trainee with a new case in every training.

Computer-Supported Segmentation of Radiological Data

Segmentation is in many cases the bottleneck when trying to use radiological image data in many clinically important applications as radiological diagnosis, monitoring, radiotherapy, and surgical planning. The availability of efficient segmentation methods is a critical issue especially in the case of large 3-D medical datasets as obtained today by the routine use of 3-D imaging methods like magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US).