Accurate segmentation of infant brain magnetic resonance images is crucial for studying brain development. However, infant images even within a narrow age-range differ drastically in size and contrast. Here, we investigated whether deep-learning based methods evaluated in iSeg-2017 can be generalized to accurately segment brain data acquired from other cohorts with different acquisition and ages. ISeg-2017 and UNC infant datasets were used to investigate three methods: SemiDenseNet, HyperDenseNet, and 3D-DenseSeg. Results demonstrate that HyperDenseNet has better segmentation performance and generalizability. Moreover, we built a joint segmentation-registration method by applying HyperDenseNet for segmentation. Results show that the joint method produced better performance compared with only registration or segmentation.
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