Paper Title
An Automation Segmentation Framework of Building an Anatomical Mouse Model For Bioluminescence Tomography

Bioluminescence tomography is known as a highly ill-posed inverse problem. To improve the reconstruction performance by introducing anatomical structures as a priori knowledge, an automatic segmentation framework has been proposed in this paper to extract the mouse whole-body organs and tissues, which enables to build up a heterogeneous mouse model for reconstruction of bioluminescence tomography. Finally, an in vivo mouse experiment has been conducted to evaluate this framework by using an X-ray computed tomography system and a multi-view bioluminescence imaging system. The findings suggest that the proposed method can realize fast automatic segmentation of mouse anatomical structures, ultimately enhancing the reconstruction performance of bioluminescence tomography. Keywords- Medical image processing, automatic image segmentation, anatomical mouse model, bioluminescence tomography.