Automatic segmentation of bone lesions in multimodal image series

Patients with malign bone lesions, for example bone metastases or multiple myeloma, suffer from pain, fractures and immobility. Imaging with CT and MRI allows the localization of these lesions. Functional parameters can be determined by dynamic contrast enhanced MRI (DCE MRI) or diffusion weighted MRI (DWI).

To quantify the bone lesions and the tumor mass the volume of the lesions is determined based on morphological imaging. For the volumetry of the lesions an accurate segmentation is needed. Manual segmentation of bone metastases and bone lesions in multiple myeloma is a very time consuming task and cannot be reproduced. In multiple myeloma a manual segmentation of all lesions is not possible because of the large number of lesions and the huge amount of image data in multiple modalities. Without automating of the segmentation and image analysis process the determination of the overall tumor mass is not feasible. With computerized tools that detect bone lesions in the image data automatically the analysis of all lesions is possible, even for large data sets in clinical trials.

The development of segmentation methods for bone lesions is at first performed on preclinical data under controlled conditions. In this project bone metastases in case of mamma carcinoma in rats are analyzed. Bone metastases show a soft tissue tumor part and changes in bone structure. The bone structure changes occur as bone resorption (osteolysis) or bone formation (osteoblastic lesion).

For the rat model tools are developed that segment automatically the bone lesion and the soft tissue tumor. With dynamic imaging techniques (dynamic contrast enhanced MRI – DCE MRI, diffusion weighted imaging – DWI) parameters for blood volume, vessel permeability and cell density can be determined. These functional parameters shall be included in an automatic classification of tumor tissue regions in active or necrotic tumor tissue.

Multiple myeloma is a malign hematological disease that shows similar effects on bone as in bone metastases. Bone lesions can occur as delimited focal lesions or as diffuse osteoporosis. With whole body MRI and CT these lesions can be detected but the large amount of image data and the multitude of lesions makes an exact quantitative analysis of all lesions impossible. Determination of the overall tumor mass requires an exact segmentation of all lesions. A manual segmentation of all lesions is too complex and therefore not viable. For a follow-up analysis currently only a reference lesion is selected and quantified. Without computerized tools that perform an automatic segmentation the quantification of the overall tumor mass is not possible. 

Here automatic segmentation methods for the delineation of bone lesions in multiple myeloma are developed to enable the quantification of the overall tumor mass. 

Functional parameters from DCE-MRI and DWI are used for the analysis of a therapy effect but also for a distinction between malign and benign tissue changes. Additional to the morphological information from CT and MRI these dynamic parameters shall be integrated in the automatic analysis of bone lesions in multiple myeloma.