KEYWORDS: Tumors, Magnetic resonance imaging, Image segmentation, 3D image processing, 3D acquisition, Radiotherapy, Image acquisition, Lung, 3D metrology, Temporal resolution
Respiration causes movement and shape changes in thoracic tumors, which has a direct influence on the radio-therapy planning process. Current methods for the estimation of tumor mobility are either two-dimensional (fluoroscopy, 2D dynamic MRI) or based on radiation (3D (+t) CT, implanted gold markers). With current advances in dynamic MRI acquisition, 3D+t image sequences of the thorax can be acquired covering the thorax over the whole breathing cycle. In this work, methods are presented for the interactive segmentation of tumors in dynamic images, the calculation of tumor trajectories, dynamic tumor volumetry and dynamic tumor rotation/deformation based on 3D dynamic MRI. For volumetry calculation, a set of 21 related partial volume correcting volumetry algorithms has been evaluated based on tumor surrogates. Conventional volumetry based on voxel counting yielded a root mean square error of 29% compared to a root mean square error of 11% achieved by the algorithm performing best among the different volumetry methods. The new workflow has been applied to a set of 26 patients. Preliminary results indicate, that 3D dynamic MRI reveals important aspects of tumor behavior during the breathing cycle. This might imply the possibility to further improve high-precision radiotherapy techniques.
The endoluminal brachytherapy of peripherally located bronchial carcinoma is difficult because of the complexity to position an irradiation catheter led by a bronchoscope to a desired spot inside a human lung. Furthermore the size of the bronchoscope permits only rarely the insertion of a catheter into the fine segment bronchi. We are developing an image-guided navigation system which indicates a path for guidance to the desired bronchus. Thereby a thin catheter with an enclosed navigation probe can be led up directly to the target bronchus, either by the use of the video of the bronchoscope or by the use of virtual bronchoscopy. Because of the thin bronchi and their moving soft tissue, the navigation system has to be very precise. This accuracy is reached by a gradually registering navigation component which improves the accuracy in the course of the intervention through mapping the already covered path to the preoperatively generated graph based bronchial tree description. The system includes components for navigation, segmentation, preoperative planning, and intraoperative guidance. Furthermore the visualization of the path can be adapted to the lung specialist's habits (video of bronchoscope, 2D, 3D, virtual bronchoscopy etc.).
The paper presents a new method for the semiautomatic segmentation of anatomical or pathological structures in MRI, CT or ultrasound images. The concept of bounding-object segmentation is based on an efficient combination of a new interactive approach with well known automatic segmentation algorithms. The efficiency of this new method is based on the transparent interaction between a 3D scene as well arbitrary 2D views of the scene. Bounding-object segmentation can also be described as a combination of interactive 3D segmentation with region-based, level-set-based, and/or texture based 3D-segmentation algorithms.
KEYWORDS: Visualization, Medical imaging, Image segmentation, Surgery, 3D vision, Data acquisition, 3D image processing, Image processing, Image processing algorithms and systems, Data processing
The aim of the Medical Imaging Interaction Toolkit (MITK) is to facilitate the creation of clinically usable
image-based software. Clinically usable software for image-guided procedures and image analysis require a high
degree of interaction to verify and, if necessary, correct results from (semi-)automatic algorithms. MITK is
a class library basing on and extending the Insight Toolkit (ITK) and the Visualization Toolkit (VTK). ITK
provides leading-edge registration and segmentation algorithms and forms the algorithmic basis. VTK has
powerful visualization capabilities, but only low-level support for interaction (like picking methods, rotation,
movement and scaling of objects). MITK adds support for high level interactions with data like, for example, the
interactive construction and modification of data objects. This includes concepts for interactions with multiple
states as well as undo-capabilities. Furthermore, VTK is designed to create one kind of view on the data
(either one 2D visualization or a 3D visualization). MITK facilitates the realization of multiple, different views
on the same data (like multiple, multiplanar reconstructions and a 3D rendering). Hierarchically structured
combinations of any number and type of data objects (image, surface, vessels, etc.) are possible. MITK can
handle 3D+t data, which are required for several important medical applications, whereas VTK alone supports
only 2D and 3D data. The benefit of MITK is that it supplements those features to ITK and VTK that are
required for convenient to use, interactive and by that clinically usable image-based software, and that are
outside the scope of both. MITK will be made open-source (http://www.mitk.org).
Computer-assisted surgery aims at a decreased surgical risk and a reduced recovery time of patients. However, its use is still limited to complex cases because of the high effort. It is often caused by the extensive medical image analysis. Especially, image segmentation requires a lot of manual work. Surgeons and radiologists are suffering from usability problems of many workstations.
In this work, we present a dedicated workplace for interactive segmentation integratd within the CHILI (tele-)radiology system. The software comes with a lot of improvements with respect to its graphical user interface, the segmentation process and the segmentatin methods. We point out important software requirements and give insight into the concepts which were implemented. Further examples and applications illustrate the software system.
We propose a procedure for the intraoperative generation of attributed relational vessel graphs. It builds the prerequisite for a vessel-based registration of a virtual, patient-individual, preoperative, three-dimensional liver model with the intraopeatively deformed liver by graph matching. An image processing pipeline is proposed to extract an abstract representation of the vascular anatomy from intraoperatively acquired three-dimensional ultrasound. The procedure is transferable to other vascularized soft tissues like the brain or the kidneys. We believe that our approach is suitable for intraoperative application as basis for efficient vessel-based registration of the surgical volume of interest. By reducing the problem of intraoperative registration in visceral surgery to the mapping of corresponding attributed relational vessel graphs a fast and reliable registration seems feasible even in the depth of deformed vascularized soft tissues like in human livers.
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