Automatic Accurate Infant Cerebellar Tissue Segmentation with Densely Connected Convolutional Network
The human cerebellum has been recognized as a key brain structure for motor control and cognitive function regulation. Investigation of brain functional development in the early life has recently been focusing on both cerebral and cerebellar development. Accurate segmentation of the infant cerebellum into different tissues is among the most important steps for quantitative development studies. However, this is extremely challenging due to the weak tissue contrast, extremely folded structures, and severe partial volume effect. To date, there are very few works touching infant cerebellum segmentation. We tackle this challenge by proposing a densely connected convolutional network to learn robust feature representations of different cerebellar tissues towards automatic and accurate segmentation. Specifically, we develop a novel deep neural network architecture by directly connecting all the layers to ensure maximum information flow even among distant layers in the network. This is distinct from all previous studies. Importantly, the outputs from all previous layers are passed to all subsequent layers as contextual features that can guide the segmentation. Our method achieved superior performance than other state-of-the-art methods when applied to Baby Connectome Project (BCP) data consisting of both 6- and 12-month-old infant brain images.
This work was supported by the National Institutes of Health (MH109773, MH100217, MH070890, EB006733, EB008374, EB009634, AG041721, AG042599, MH088520, MH108914, and MH107815). This work also utilizes approaches developed by an NIH grant (1U01MH110274) and the efforts of the UNC/UMN Baby Connectome Project Consortium.
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