My recent work on the theory and algorithms of image co-segmentation and its applications in joint delineation of lesions from MRI-PET, PET-CT, and MRI-PET-CT (also PET images alone) is accepted for publication in Medical Image Analysis journal. I am glad to receive fantastic reviews from respected reviewers for this submission. Earlier version of the study appeared in MICCAI 2012, and the core algorithm was highlighted in AuntMinnie after the presentation at RSNA 2012.
The pdf and the relevant links will be activated below soon after the paper becomes online in the Elsevier website.
Joint Segmentation of Anatomical and Functional Images: Applications in Quantification of Lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT Images,
U. Bagci*, J.K. Udupa, N. Mendhiratta, B. Foster, Z. Xu, J. Yao, X. Chen, D.J. Mollura.
We present a novel method for the joint segmentation of anatomical and functional images. Our proposed methodology unifies the domains of anatomical and functional images, represents them in a product lattice, and performs simultaneous delineation of regions based on random walk image segmentation. Furthermore, we also propose a simple yet effective object/background seed localization method to make the proposed segmentation process fully automatic. Our study uses PET, PET-CT, MRI-PET, and fused MRI-PET-CT scans (77 studies in all) from 56 patients who had various lesions in different body regions. We validated the effectiveness of the proposed method on different PET phantoms as well as on clinical images with respect to the ground truth segmentation provided by clinicians. Experimental results indicate that the presented method is superior to threshold and Bayesian methods commonly used in PET image segmentation, is more accurate and robust compared to the other PET-CT segmentation methods recently published in the literature, and also it is general in the sense of simultaneously segmenting multiple scans in real-time with high accuracy needed in routine clinical use.