Topological Fragmentation of Medical 3D Surface Mesh Models for Multi-Hierarchy Anatomical Classification

High resolution 3D mesh representations of patient anatomy with appendant functional classification are of high importance in the field of clinical education and therapy planning. Thereby, classification is not always possible directly from patient morphology, thus necessitating tool support. In this work a hierarchical mesh data model for multi-hierarchy anatomical classification is introduced, allowing labeling of a patient model according to various medical taxonomies. The classification regions are thereby specified utilizing a spline representation to be placed and deformed by a medical expert at low effort. Furthermore, application of randomized dilation allows conversion of the specified regions on the surface into fragmented and closed sub-meshes, comprising the entire anatomical structure.As proof of concept, the semi-automated classification method is implemented for VTK library and visualization of the multihierarchy anatomical model is validated with OpenGL, successfully extracting sub-meshes of the brain lobes and preparing classification regions according to Brodmann area taxonomy

A Display System for Surgical Navigation in ORL Surgery Abstract

Minimally invasive surgery, developed during the last 15 years becomes a major field of surgical intervention. The patient benefits from little damage around the focus of surgery. Special skills of the surgeon are needed to work in narrow body cavities with little space for the endoscope and surgical tools. The surgeon’s navigation during surgery can be supported by modern visualisation techniques based on 3D medical image data. A tool for intra-operative navigation was developed providing three orthogonal sections through the image volume, a 3D display of surgical tools relative to sensitive anatomical structures, a virtual endoscopic image together with the video image through the endoscope. The user interface has a simple layout for easy handling to meet the needs in the surgical theatre

Strategies for Training Deep Learning Models in Medical Domains with Small Reference Datasets

With the steady progress of Deep Learning (DL), powerful tools are now present for sophisticated segmentation tasks. Nevertheless, the generally very high demand for training data and precise reference segmentations often cannot be met in medical domains when processing small and individual studies or acquisition protocols. As common strategies, reinforcement learning or transfer learning are applicable but coherent with immense effort due to domain-specific adjustment. In this work the applicability of a U-net cascade for training on a very low amount of abdominal MRI datasets of the parenchyma is evaluated and strategies to compensate for the lack of training data are discussed. Although the model accuracy when training on 13 MRI volumes with achievable JI=89.41 is rather low, results are still good enough for manual post-processing utilizing a Graph cut (GC) approach with medium demand for user interaction. This way, the DL models are retrained, when additional test data sets become available to subsequently improve the classification accuracy. With only 2 additional GC postprocessed datasets, the accuracy after model re-training is increased to JI= 89.87. Besides, the applicability of Generative Adversial Networks (GAN) in the medical domain is evaluated discussing to synthesize axial CT slices together with perfect ground truth reference segmentations. It is shown for abdominal CT slices of the parenchyma, that in case of lack of training data, synthesized slices, that can be derived at arbitrary number, help to significantly improve the DL training process when only an insufficient amount of data is available. While training on 2,200 real images only leads to accuracy JI=88.75, the enrichment with 2,200 additional images synthesized from a GAN trained on 5,000 datasets only leads to an increase up to JI=92.02. Even if the DL model is exclusively trained on 4,400 computer-generated images, the classification accuracy on real-world data is notable with JI=90.81

Numerical Simulation for eHealth: Grid-enabled Medical Simulation Services

The European GEMSS Project 1 is concerned with the creation of medical Grid service prototypes and their evaluation in a secure service-oriented infrastructure for distributed on-demand supercomputing- the GEMSS test-bed. The medical prototype applications include maxillo-facial surgery simulation, neuro-surgery support, radio-surgery planning, inhaled drug-delivery simulation, cardiovascular simulation and tomographic image reconstruction. GEMSS will enable the wide-spread use of these computationally demanding tools originating from projects such as BloodSim, SimBio, COPHIT and RAPT as Grid services. The numerical High-Performance Computing core includes parallel Finite Element software and Computational Fluid Dynamics simulation as well as parallel optimization methods and parallel Monte Carlo simulation. Targeted end-user groups include bio-mechanics laboratories, hospital surgery/radiology units, diagnostic and therapeutic departments, designers of medical devices in industry as well as small enterprises involved in consultancy on bio-medical simulations. GEMSS addresses security, privacy and legal issues related to the Grid provision of medical simulation and image processing services and the development of suitable business models for sustainable use. The first prototype of the GEMSS middleware is expected to be released in February 2004