Add like
Add dislike
Add to saved papers

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.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app