This work presents a novel deep learning architecture
called BNU-Net for the purpose of cardiac segmentation
based on short-axis MRI images. Its name is derived from the
Batch Normalized (BN) U-Net architecture for medical image
segmentation. New generations of deep neural networks (NN)
are called convolutional NN (CNN). CNNs like U-Net have been
widely used for image classification tasks. CNNs are supervised
training models which are trained to learn hierarchies of features
automatically and robustly perform classification. Our architecture
consists of an encoding path for feature extraction and a
decoding path that enables precise localization. We compare this
approach with a parallel approach named U-Net. Both BNU-Net
and U-Net are cardiac segmentation approaches: while BNU-Net
employs batch normalization to the results of each convolutional
layer and applies an exponential linear unit (ELU) approach
that operates as activation function, U-Net does not apply batch
normalization and is based on Rectified Linear Units (ReLU).
The presented work (i) facilitates various image preprocessing
techniques, which includes affine transformations and elastic
deformations, and (ii) segments the preprocessed images using
the new deep learning architecture. We evaluate our approach
on a dataset containing 805 MRI images from 45 patients.
The experimental results reveal that our approach accomplishes
comparable or better performance than other state-of-the-art
approaches in terms of the Dice coefficient and the average
perpendicular distance.
Index Terms—Magnetic Resonance Imaging; Batch Normalization;
Exponential Linear Units
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Learning Cortical Parcellations Using Graph Neural Networks
Deep learning has been applied to magnetic resonance imaging (MRI) for a variety of purposes, ranging from the acceleration of image acquisition and image denoising to tissue segmentation and disease diagnosis. Convolutional neural networks have been particularly useful for analyzing MRI data due to the regularly sampled spatial and temporal nature of the data. However, advances in the field of brain imaging have led to network- and surface-based analyses that are often better represented in the graph domain. In this analysis, we propose a general purpose cortical segmentation method that, given resting-state connectivity features readily computed during conventional MRI pre-processing and a set of corresponding training labels, can generate cortical parcellations for new MRI data. We applied recent advances in the field of graph neural networks to the problem of cortical surface segmentation, using resting-state connectivity to learn discrete maps of the human neocortex. We found that graph neural networks accurately learn low-dimensional representations of functional brain connectivity that can be naturally extended to map the cortices of new datasets. After optimizing over algorithm type, network architecture, and training features, our approach yielded mean classification accuracies of 79.91% relative to a previously published parcellation. We describe how some hyperparameter choices including training and testing data duration, network architecture, and algorithm choice affect model performance.
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- Award ID(s):
- 1734430
- NSF-PAR ID:
- 10397889
- Date Published:
- Journal Name:
- Frontiers in Neuroscience
- Volume:
- 15
- ISSN:
- 1662-453X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Background Cognitive training may partially reverse cognitive deficits in people with HIV (PWH). Previous functional MRI (fMRI) studies demonstrate that working memory training (WMT) alters brain activity during working memory tasks, but its effects on resting brain network organization remain unknown.
Purpose To test whether WMT affects PWH brain functional connectivity in resting‐state fMRI (rsfMRI).
Study Type Prospective.
Population A total of 53 PWH (ages 50.7 ± 1.5 years, two women) and 53
HIV ‐seronegative controls (SN , ages 49.5 ± 1.6 years, six women).Field Strength/Sequence Axial single‐shot gradient‐echo echo‐planar imaging at 3.0 T was performed at baseline (TL1), at 1‐month (TL2), and at 6‐months (TL3), after WMT.
Assessment All participants had rsfMRI and clinical assessments (including neuropsychological tests) at TL1 before randomization to Cogmed WMT (adaptive training,
n = 58: 28 PWH, 30 SN; nonadaptive training,n = 48: 25 PWH, 23 SN), 25 sessions over 5–8 weeks. All assessments were repeated at TL2 and at TL3. The functional connectivity estimated by independent component analysis (ICA) or graph theory (GT) metrics (eigenvector centrality, etc.) for different link densities (LDs) were compared between PWH and SN groups at TL1 and TL2.Statistical Tests Two‐way analyses of variance (ANOVA) on GT metrics and two‐sample
t ‐tests on FC or GT metrics were performed. Cognitive (eg memory) measures were correlated with eigenvector centrality (eCent) using Pearson's correlations. The significance level was set atP < 0.05 after false discovery rate correction.Results The ventral default mode network (vDMN) eCent differed between PWH and SN groups at TL1 but not at TL2 (
P = 0.28). In PWH, vDMN eCent changes significantly correlated with changes in the memory ability in PWH (r = −0.62 at LD = 50%) and vDMN eCent before training significantly correlated with memory performance changes (r = 0.53 at LD = 50%).Data Conclusion ICA and GT analyses showed that adaptive WMT normalized graph properties of the vDMN in PWH.
Evidence Level 1
Technical Efficacy 1
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fnins.2021.797500
