New paper in EJNMMI Research

My recent paper with Dr Gabriela Kramer-Marek (who directed whole pre-clinical experiments and imaging, The Institute of Cancer Research, London, UK) and Dr Daniel J Mollura was accepted to be published in journal of EJNMMI Research.

I will be publishing the pdf and necessary links for the paper once it becomes online.

Here is the details of the paper:

Automated computer quantification of breast cancer in small-animal models using PET-guided MR image co-segmentation

U.Bagci, G. Kramer-Marek, D.J. Mollura

In our paper,  we presented  a computer-assisted volume quantification method for positron emission tomography (PET)/MRI dual modality images using PET-guided MRI co-segmentation. Our aims in this study were (1) to determine anatomical tumor volumes automatically from MRI accurately and efficiently, (2) to evaluate and compare the accuracy of the proposed method with different radiotracers (18F-ZHER2-Affibody) and 18F-flourodeoxyglucose (18F-FDG), and (3) to confirm the proposed method’s determinations from PET/MRI scans in comparison with PET/CT scans.


The proposed PET-guided MR image co-segmentation algorithm provided an automated and efficient way of assessing anatomical tumor volumes and their spatial extent. We showed that although the 18F-ZHER2-Affibody radiotracer in PET imaging is often used for characterization of tumors rather than detection, sensitivity and specificity of the localized radiotracer in the tumor region were informative enough; therefore, roughly determined tumor regions from PET images guided the delineation process well in the anatomical image domain for extracting accurate tumor volume information. Furthermore, the use of $^{18}$F-FDG radiotracer was not as successful as the 18F-ZHER2-Affibody in guiding the delineation process due to false-positive uptake regions in the neighborhood of tumor regions; hence, the accuracy of the fully automated segmentation method changed dramatically. Last, we qualitatively showed that MRI yields superior identification of tumor boundaries when compared to conventional CT imaging.




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