Unsupervised Free-View Groupwise Segmentation for M3 Cardiac Images Using Synchronized Spectral Network

Image-based diagnosis and population study on cardiac problems require automatic segmentation on increasingly large amount of data from different protocols, different views, and different patients. However, current algorithms are often limited to regulated settings such as fixed view and single image from one specific modality, where the supervised learning methods can be easily employed but with restricted usability. In this paper, we propose the unsupervised free-view groupwise M

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segmentation: a simultaneous segmentation for a group of

M

ulti-modality,

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ulti-chamber, from

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ulti-subject images from an arbitrary imaging view. To achieve the segmentation, we particularly develop the Synchronized Spectral Network (SSN) model for the joint decomposing, synchronizing, and clustering the spectral representations of free-view M

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cardiac images. The SSN model generates a set of synchronized superpixels where the corresponding chamber regions share the same superpixel label, which naturally provides simultaneous cardiac segmentation. The segmentation is quantitatively evaluated by more than 10000 images (MR and CT) from 93 subjects and high dice metric (> 85%) is consistently achieved in validation. Our method provides a powerful segmentation tool for cardiac images under non-regulated imaging environment.