A New Paper in Computational Methods for Molecular Imaging Workshop of MICCAI 2014

Our fuzzy connectedness based co-segmentation paper was accepted for publication and presentation in “Computational Methods for Molecular Imaging” (CMMI) workshop of MICCAI 2014. The details of the paper is as follows:

Fuzzy Connectedness Image Co-Segmentation for Hybrid PET/MRI and PET/CT Scans

Ziyue Xu, Ulas Bagci, Jayaram K. Udupa, Daniel J. Mollura. Computational Methods for Molecular Imaging, MICCAI 2014.

Lecture Notes in Computational Vision and Biomechanics Volume 22, 2015, pp 15-24.

Abstract:
In this paper, we presented a 3-D computer-aided co-segmentation tool for lesion detection and quantification from hybrid PET/MRI and PET/CT scans. The proposed method was designed with a novel modality-specific visibility weighting scheme built upon a fuzzy connectedness (FC) image segmentation algorithm. In order to improve the determination of lesion margin, it is necessary to combine the complementary information of tissues from both anatomical and functional domains. Therefore, a robust image segmentation method that simultaneously segments lesions in joint domain is required. However, this task, named co-segmentation, is a challenging problem due to (1) unique challenges brought by each imaging modality, and (2) a lack of one-to-one region and boundary correspondences of lesions in different imaging modalities. Owing to these hurdles, the algorithm is desired to have a sufficient flexibility to utilize the strength of each modality. In this work, seed points were first selected from high uptake regions within PET images. Then, we delineated the lesion boundary using a hybrid approach based on novel affinity function design within the FC framework. Further, iterative relative FC (IRFC) was tested with automatically identified background seeds. The segmentation results were compared to the reference truths provided by radiologists. Experimental results show that the proposed method effectively utilizes multi-modality information for co-segmentation, with a high accuracy (mean DSC of $85\%$) and can be a viable alternative method to the state-of-the art joint segmentation method of random walk (RW) with higher efficiency.

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