9,889 research outputs found
Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.
Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging studies, and potentially for segmenting other neural regions as well
End-to-end learning of brain tissue segmentation from imperfect labeling
Segmenting a structural magnetic resonance imaging (MRI) scan is an important
pre-processing step for analytic procedures and subsequent inferences about
longitudinal tissue changes. Manual segmentation defines the current gold
standard in quality but is prohibitively expensive. Automatic approaches are
computationally intensive, incredibly slow at scale, and error prone due to
usually involving many potentially faulty intermediate steps. In order to
streamline the segmentation, we introduce a deep learning model that is based
on volumetric dilated convolutions, subsequently reducing both processing time
and errors. Compared to its competitors, the model has a reduced set of
parameters and thus is easier to train and much faster to execute. The contrast
in performance between the dilated network and its competitors becomes obvious
when both are tested on a large dataset of unprocessed human brain volumes. The
dilated network consistently outperforms not only another state-of-the-art deep
learning approach, the up convolutional network, but also the ground truth on
which it was trained. Not only can the incredible speed of our model make large
scale analyses much easier but we also believe it has great potential in a
clinical setting where, with little to no substantial delay, a patient and
provider can go over test results.Comment: Published as a conference paper at IJCNN 2017 Preprint versio
Knowing what you know in brain segmentation using Bayesian deep neural networks
In this paper, we describe a Bayesian deep neural network (DNN) for
predicting FreeSurfer segmentations of structural MRI volumes, in minutes
rather than hours. The network was trained and evaluated on a large dataset (n
= 11,480), obtained by combining data from more than a hundred different sites,
and also evaluated on another completely held-out dataset (n = 418). The
network was trained using a novel spike-and-slab dropout-based variational
inference approach. We show that, on these datasets, the proposed Bayesian DNN
outperforms previously proposed methods, in terms of the similarity between the
segmentation predictions and the FreeSurfer labels, and the usefulness of the
estimate uncertainty of these predictions. In particular, we demonstrated that
the prediction uncertainty of this network at each voxel is a good indicator of
whether the network has made an error and that the uncertainty across the whole
brain can predict the manual quality control ratings of a scan. The proposed
Bayesian DNN method should be applicable to any new network architecture for
addressing the segmentation problem.Comment: Submitted to Frontiers in Neuroinformatic
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