Back

Automated Brain Masking of Fetal Functional MRI

Rutherford, S.; Sturmfels, P.; Angstadt, M.; Hect, J.; Wiens, J.; van den Heuval, M. I.; Scheinost, D.; Thomason, M.; Sripada, C.

2019-07-13 neuroscience
10.1101/525386 bioRxiv
Show abstract

AO_SCPLOWBSTRACTC_SCPLOWFetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite rapid and widespread growth of this approach, at present we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses (gestational age 24-39 weeks, M=30.9, SD=4.2), we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open source workflow for fetal functional MRI data preprocessing.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Imaging Neuroscience
● 282 papers in training set
Top 0.3%
11.7%
Similar papers in this journal
2
NeuroImage
○ 903 papers in training set
Top 1%
11.7%
Similar papers in this journal
3
Human Brain Mapping
○ 329 papers in training set
Top 0.4%
10.8%
Similar papers in this journal
4
Aperture Neuro
● 20 papers in training set
Top 0.1%
6.6%
Similar papers in this journal
5
PLOS ONE
● 5266 papers in training set
Top 27%
5.5%
Similar papers in this journal
6
Frontiers in Neuroscience
○ 256 papers in training set
Top 0.5%
5.4%
50% of probability mass above
7
Magnetic Resonance Imaging
○ 23 papers in training set
Top 0.1%
5.4%
8
Developmental Cognitive Neuroscience
○ 96 papers in training set
Top 0.4%
3.2%
9
Medical Image Analysis
○ 35 papers in training set
Top 0.2%
3.2%
10
Scientific Reports
○ 3612 papers in training set
Top 37%
2.7%
11
Neuroinformatics
○ 46 papers in training set
Top 0.3%
2.4%
12
IEEE Transactions on Medical Imaging
● 21 papers in training set
Top 0.2%
2.4%
13
PLOS Computational Biology
● 1863 papers in training set
Top 12%
2.4%
14
Journal of Neuroscience Methods
○ 122 papers in training set
Top 0.8%
2.4%
15
Frontiers in Neuroimaging
○ 11 papers in training set
Top 0.1%
2.1%
16
NeuroImage: Clinical
○ 144 papers in training set
Top 1%
2.1%
17
Brain Informatics
○ 10 papers in training set
Top 0.1%
1.9%
18
Scientific Data
○ 209 papers in training set
Top 1%
1.7%
19
Magnetic Resonance in Medicine
○ 85 papers in training set
Top 0.4%
1.7%
20
Brain Structure and Function
○ 93 papers in training set
Top 1%
1.1%
21
Frontiers in Psychiatry
○ 87 papers in training set
Top 2%
1.1%
22
eneuro
● 439 papers in training set
Top 8%
0.8%
23
eLife
● 5828 papers in training set
Top 67%
0.8%
24
Frontiers in Neuroinformatics
○ 41 papers in training set
Top 0.8%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.