DeepAI AI Chat
Log In Sign Up

Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data

by   Yuankai Huo, et al.

Whole brain segmentation on a structural magnetic resonance imaging (MRI) is essential in non-invasive investigation for neuroanatomy. Historically, multi-atlas segmentation (MAS) has been regarded as the de facto standard method for whole brain segmentation. Recently, deep neural network approaches have been applied to whole brain segmentation by learning random patches or 2D slices. Yet, few previous efforts have been made on detailed whole brain segmentation using 3D networks due to the following challenges: (1) fitting entire whole brain volume into 3D networks is restricted by the current GPU memory, and (2) the large number of targeting labels (e.g., > 100 labels) with limited number of training 3D volumes (e.g., < 50 scans). In this paper, we propose the spatially localized atlas network tiles (SLANT) method to distribute multiple independent 3D fully convolutional networks to cover overlapped sub-spaces in a standard atlas space. This strategy simplifies the whole brain learning task to localized sub-tasks, which was enabled by combing canonical registration and label fusion techniques with deep learning. To address the second challenge, auxiliary labels on 5111 initially unlabeled scans were created by MAS for pre-training. From empirical validation, the state-of-the-art MAS method achieved mean Dice value of 0.76, 0.71, and 0.68, while the proposed method achieved 0.78, 0.73, and 0.71 on three validation cohorts. Moreover, the computational time reduced from > 30 hours using MAS to 15 minutes using the proposed method. The source code is available online


page 2

page 3

page 5


3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles

Detailed whole brain segmentation is an essential quantitative technique...

Reproducibility Evaluation of SLANT Whole Brain Segmentation Across Clinical Magnetic Resonance Imaging Protocols

Whole brain segmentation on structural magnetic resonance imaging (MRI) ...

Whole Brain Segmentation with Full Volume Neural Network

Whole brain segmentation is an important neuroimaging task that segments...

Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features

Sub-cortical brain structure segmentation in Magnetic Resonance Images (...

QuickNAT: Segmenting MRI Neuroanatomy in 20 seconds

Whole brain segmentation from structural magnetic resonance imaging is a...

AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation

Whole brain segmentation using deep learning (DL) is a very challenging ...