Template Registration with Missing Parts: Application to the Segmentation of M. Tuberculosis Infected Lungs

Many techniques have been proposed to segment organs from images, however the segmentation of diseased organs remains challenging and frequently requires lots of user interaction. The challenge consists of segmenting an organ while its appearance and its shape vary due to the presence of the disease in addition to individual variations. We propose a template registration technique that can be used to recover the complete segmentation of a diseased organ from a partial segmentation. The usual template registration method is modified in such a way that it is robust to missing parts. The proposed method is used to segment Mycobacterium tuberculosis infected lungs in CT images of experimentally infected mice. Using synthetic data, we evaluate and compare the performance of the proposed algorithm with the usual sum of squared difference cost function.