Marco Nolden

The Medical Imaging Interaction Toolkit

Thoroughly designed, open-source toolkits emerge to boost progress in medical imaging. The Insight Toolkit (ITK) provides this for the algorithmic scope of medical imaging, especially for segmentation and registration. But medical imaging algorithms have to be clinically applied to be useful, which additionally requires visualization and interaction. The Visualization Toolkit (VTK) has powerful visualization capabilities, but only low-level support for interaction. In this paper, we present the Medical Imaging Interaction Toolkit (MITK). The goal of MITK is to significantly reduce the effort required to construct specifically tailored, interactive applications for medical image analysis. MITK allows an easy combination of algorithms developed by ITK with visualizations created by VTK and extends these two toolkits with those features, which are outside the scope of both. MITK adds support for complex interactions with multiple states as well as undo-capabilities, a very important prerequisite for convenient user interfaces. Furthermore, MITK facilitates the realization of multiple, different views of the same data (as a multiplanar reconstruction and a 3D rendering) and supports the visualization of 3D+t data, whereas VTK is only designed to create one kind of view of 2D or 3D data. MITK reuses virtually everything from ITK and VTK. Thus, it is not at all a competitor to ITK or VTK, but an extension, which eases the combination of both and adds the features required for interactive, convenient to use medical imaging software. MITK is an open-source project (www.mitk.org).

Interactive segmentation framework of the Medical Imaging Interaction Toolkit

Interactive methods are indispensable for real world applications of segmentation in medicine, at least to allow for convenient and fast verification and correction of automated techniques. Besides traditional interactive tasks such as adding or removing parts of a segmentation, adjustment of contours or the placement of seed points, the relatively recent Graph Cut and Random Walker segmentation methods demonstrate an interest in advanced interactive strategies for segmentation. Though the value of toolkits and extensible applications is generally accepted for the development of new segmentation algorithms, the topic of interactive segmentation applications is rarely addressed by current toolkits and applications. In this paper, we present the extension of the Medical Imaging Interaction Toolkit (MITK) with a framework for the development of interactive applications for image segmentation. The framework provides a clear structure for the development of new applications and offers a plugin mechanism to easily extend existing applications with additional segmentation tools. In addition, the framework supports shape-based interpolation and multi-level undo/redo of modifications to binary images. To demonstrate the value of the framework, we also present a free, open-source application named InteractiveSegmentation for manual segmentation of medical images (including 3D+t), which is built based on the extended MITK framework. The application includes several features to effectively support manual segmentation, which are not found in comparable freely available applications. InteractiveSegmentation is fully developed and successfully and regularly used in several projects. Using the plugin mechanism, the application enables developers of new algorithms to begin algorithmic work more quickly.

The Medical Imaging Interaction Toolkit (MITK) - a toolkit facilitating the creation of interactive software by extending VTK and ITK

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).

The extensible open-source rigid and affine image registration module of the Medical Imaging Interaction Toolkit (MITK)

Although non-rigid registration methods are available or under development for many specific problems in medicine, rigid and affine registration is an important task that is often performed for pre-aligning images before using non-rigid registration. In this paper, we present a free and open-source application for rigid and affine image registration, which is designed both for developers and for end-users. The application is based on the Medical Imaging Interaction Toolkit (MITK) and allows for inter-modality and intra-modality rigid 2D-2D and 3D-3D registration of medical images such as CT, MRI, or ultrasound. The framework as well as the application can be easily extended by adding new transforms, metrics and optimizers. Thus, developers of new algorithms are enabled to test and use their algorithms more quickly, spending less work on user interfaces. Additionally, the framework provides the possibility to use image masks to restrict the evaluation of metric values by the optimizer on certain areas of the images.

Toward knowledge-based liver surgery: holistic information processing for surgical decision support

PurposeMalignant neoplasms of the liver are among the most frequent cancers worldwide. Given the diversity of options for liver cancer therapy, the choice of treatment depends on various parameters including patient condition, tumor size and location, liver function, and previous interventions. To address this issue, we present the first approach to treatment strategy planning based on holistic processing of patient-individual data, practical knowledge (i.e., case knowledge), and factual knowledge (e.g., clinical guidelines and studies).MethodsThe contributions of this paper are as follows: (1) a formalized dynamic patient model that incorporates all the heterogeneous data acquired for a specific patient in the whole course of disease treatment; (2) a concept for formalizing factual knowledge; and (3) a technical infrastructure that enables storing, accessing, and processing of heterogeneous data to support clinical decision making.ResultsOur patient model, which currently covers 602 patient-individual parameters, was successfully instantiated for 184 patients. It was sufficiently comprehensive to serve as the basis for the formalization of a total of 72 rules extracted from studies on patients with colorectal liver metastases or hepatocellular carcinoma. For a subset of 70 patients with these diagnoses, the system derived an average of

MITK-ToF—Range data within MITK

37 \pm 15

A quality-refinement process for medical imaging applications.

37±15 assertions per patient.ConclusionThe proposed concept paves the way for holistic treatment strategy planning by enabling joint storing and processing of heterogeneous data from various information sources.

Towards an open-source semantic data infrastructure for integrating clinical and scientific data in cognition-guided surgery

Due to rapid developments in the research areas of medical imaging, medical image processing and robotics, computer assistance is no longer restricted to diagnostics and surgical planning but has been expanded to surgical and radiological interventions. From a software engineering point of view, the systems for image-guided therapy (IGT) are highly complex. To address this issue, we presented an open source extension to the well-known Medical Imaging Interaction Toolkit (MITK) for developing IGT systems, called MITK-IGT. The contribution of this paper is two-fold: Firstly, we extended MITK-IGT such that it (1) facilitates the handling of navigation tools, (2) provides reusable graphical user interface (UI) components, and (3) features standardized exception handling. Secondly, we developed a software prototype for computer-assisted needle insertions, using the new features, and tested it with a new Tabletop field generator (FG) for the electromagnetic tracking system NDI Aurora ®. To our knowledge, we are the first to have integrated this new FG into a complete navigation system and have conducted tests under clinical conditions. In conclusion, we enabled simplified development of imageguided therapy software and demonstrated the utilizability of applications developed with MITK-IGT in the clinical workflow.