This application claims the benefit of DE 10 2010 063 551.0, filed on Dec. 20, 2010.
The present embodiments relate to automatic generation of structure datasets as are used for planning radiotherapy.
The procedure when planning radiotherapy for a person being examined is to record and archive image datasets of the person being examined. If the physician plans the radiotherapy, structures that should be as little damaged as possible during the radiotherapy are identified on the image datasets. The physician analyzes the recorded image data and in the image data, identifies organs such as, for example, a liver, a kidney or bone. If organs at risk or objects to be protected surrounding the tumor to be irradiated are identified, a start may be made on planning the radiotherapy. However, the identification of the individual structures in the image dataset may be very time-consuming.
The present embodiments may obviate one or more of the drawbacks or limitations in the related art. For example, the generation of a structure dataset, as is used for planning radiotherapy, may be accelerated and improved.
In a first embodiment, a system is provided for automatic generation of structure datasets that are used for planning radiotherapy. The system has a receiver unit that receives an image dataset of a person being examined. A central segmentation unit is also provided. The receiver unit automatically forwards the image dataset to the central segmentation unit. The central segmentation unit has an identification unit for identifying the regions of the body and a plurality of segmentation modules. Each segmentation module of the plurality of segmentation module segments the image dataset using different segmentation methods. Once the identification unit identifies the regions of the body, the identification unit selects a segmentation module on the basis of the region of the body identified and automatically forwards the image dataset to the selected segmentation module. The selected segmentation module segments the image dataset and identifies predetermined structures in the image dataset. The selected segmentation module generates a structure dataset that is automatically saved in a data memory of the system.
A central segmentation unit is provided, to which the image datasets are transferred. The segmentation unit may initially identify the region of the body and select one segmentation module of a plurality of segmentation modules. Each segmentation module of the plurality of segmentation modules is especially suitable for segmenting a particular area of the body or for identifying particular organs. As a result, the structure dataset may be generated and saved in a simple and efficient manner. By using a central segmentation unit, many different and also very complex segmentation algorithms may be used, which improve and accelerate the segmentation. The physician need only retrieve the generated structure dataset and possibly briefly check the identified structures, and may then immediately start planning the radiotherapy.
Each segmentation module of the plurality of segmentation modules is optimized in order to recognize predetermined structures in the image dataset. Depending on the region of the body examined, various structures that are differently embedded in the surrounding tissue or bones are to be recognized. Some segmentation algorithms are better at recognizing sharp edges, while other algorithms, for example, work on the basis of regions and are suitable for recognizing homogeneous image areas. By selecting the segmentation module on the basis of the region of the body to be segmented, the segmentation method best suited for the region of the body and the organs contained in the region of the body may be used. Such combinations of algorithms may be preconfigured and parameterized using presets.
In one embodiment, the structure dataset is a DICOM radiotherapy structure dataset (DICOM-RT structure dataset).
The present embodiments also relate to a method for automatic generation of the structure datasets, the recorded image dataset automatically being transferred to the central segmentation unit in a first act. A region of the body that is represented in the image dataset is automatically identified. In a next act, a segmentation module is automatically selected from a plurality of segmentation modules in the central segmentation unit on the basis of the region of the body identified. The segmentation modules each segment the image dataset using different segmentation methods. In a further act of the method, the image dataset is automatically transferred to the selected segmentation module, and the selected segmentation module automatically segments the image dataset received in order to identify predetermined structures in the image dataset for the generation of the structure dataset. In a further act, the generated structure dataset is stored in a data memory.
Organ edges may be determined in the image dataset during the automatic segmentation, and the organs mapped in the image dataset are identified, so that an organ-specific structure dataset may be generated and saved.
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The image dataset may also be a combined dataset, in which CT, MR, PET and ultrasound images are combined. The structure datasets may also contain points of interest (POIs). The points of interest are, for example, points for planning radiotherapy. If the segmentation module is unable to segment organ boundaries, the segmentation module may at least draw in a center point of the organ or an approximate box around the organ. Since the generated image data is automatically fed to the system for generation of the structure datasets, there is more time for generating the structure datasets, and more complex segmentation algorithms may be used. The physician no longer has to trigger or perform the segmentation himself or herself. This represents a very large time gain, and the generation of the structure dataset is improved, since more complex algorithms may be used by using the central segmentation unit.
While the present invention has been described above by reference to various embodiments, it should be understood that many changes and modifications can be made to the described embodiments. It is therefore intended that the foregoing description be regarded as illustrative rather than limiting, and that it be understood that all equivalents and/or combinations of embodiments are intended to be included in this description.
Number | Date | Country | Kind |
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DE102010063551.0 | Dec 2010 | DE | national |