Co-segmentation of functional and anatomical images

Ulas Bagci; Jayaram K. Udupa; Jianhua Yao; Daniel J. Mollura

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012, Proceedings, Part III. Lecture Notes in Computer Science, volume 7512, pages 459–467.

Conceptual schematic of PET-derived seed cues guiding joint segmentation of aligned PET and CT images.
Conceptual illustration of the paper’s PET-CT co-segmentation approach. Generated illustration; editorial review required.

Abstract

This paper presents a novel method for segmenting functional and anatomical structures simultaneously. The proposed method unifies domains of anatomical and functional images (PET-CT), represents them in a product lattice, and performs simultaneous delineation of regions based on a random walk image segmentation. In addition, we propose a simple yet efficient object/background seed localization method, where background and foreground object cues are automatically obtained from PET images and propagated onto the corresponding anatomical images (CT). In our experiments, abnormal anatomies on PET-CT images from human subjects are segmented synergistically by the proposed fully automatic co-segmentation method with high precision (mean DSC of 91.44%) in seconds (avg. 40 seconds).

Abstract source

This 2012 MICCAI paper presents a method for jointly segmenting PET and CT images using random-walk segmentation and automatically located PET-derived seeds.

Summary

Bagci, Ulas; Udupa, Jayaram K.; Yao, Jianhua; and Mollura, Daniel J. describe a method for jointly segmenting functional and anatomical structures in PET-CT images. The approach represents the image domains in a product lattice and uses random-walk segmentation to delineate regions in both modalities. It also identifies foreground and background cues in PET images and propagates them to the corresponding CT images.

In experiments on PET-CT images from human subjects, the authors report a mean Dice similarity coefficient (DSC) of 91.44% and an average processing time of 40 seconds. These are results reported for the study’s evaluation; the abstract does not establish prospective clinical validation or clinical readiness. Read the paper on Springer Nature Link or its PubMed record.

Cite this work

Ulas Bagci; Jayaram K. Udupa; Jianhua Yao; Daniel J. Mollura (2012). Co-segmentation of functional and anatomical images. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012, Proceedings, Part III. Lecture Notes in Computer Science, volume 7512, pages 459–467. https://doi.org/10.1007/978-3-642-33454-2_57

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