400 research outputs found
Brain segmentation based on multi-atlas guided 3D fully convolutional network ensembles
In this study, we proposed and validated a multi-atlas guided 3D fully
convolutional network (FCN) ensemble model (M-FCN) for segmenting brain regions
of interest (ROIs) from structural magnetic resonance images (MRIs). One major
limitation of existing state-of-the-art 3D FCN segmentation models is that they
often apply image patches of fixed size throughout training and testing, which
may miss some complex tissue appearance patterns of different brain ROIs. To
address this limitation, we trained a 3D FCN model for each ROI using patches
of adaptive size and embedded outputs of the convolutional layers in the
deconvolutional layers to further capture the local and global context
patterns. In addition, with an introduction of multi-atlas based guidance in
M-FCN, our segmentation was generated by combining the information of images
and labels, which is highly robust. To reduce over-fitting of the FCN model on
the training data, we adopted an ensemble strategy in the learning procedure.
Evaluation was performed on two brain MRI datasets, aiming respectively at
segmenting 14 subcortical and ventricular structures and 54 brain ROIs. The
segmentation results of the proposed method were compared with those of a
state-of-the-art multi-atlas based segmentation method and an existing 3D FCN
segmentation model. Our results suggested that the proposed method had a
superior segmentation performance
Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data
Training deep fully convolutional neural networks (F-CNNs) for semantic image
segmentation requires access to abundant labeled data. While large datasets of
unlabeled image data are available in medical applications, access to manually
labeled data is very limited. We propose to automatically create auxiliary
labels on initially unlabeled data with existing tools and to use them for
pre-training. For the subsequent fine-tuning of the network with manually
labeled data, we introduce error corrective boosting (ECB), which emphasizes
parameter updates on classes with lower accuracy. Furthermore, we introduce
SkipDeconv-Net (SD-Net), a new F-CNN architecture for brain segmentation that
combines skip connections with the unpooling strategy for upsampling. The
SD-Net addresses challenges of severe class imbalance and errors along
boundaries. With application to whole-brain MRI T1 scan segmentation, we
generate auxiliary labels on a large dataset with FreeSurfer and fine-tune on
two datasets with manual annotations. Our results show that the inclusion of
auxiliary labels and ECB yields significant improvements. SD-Net segments a 3D
scan in 7 secs in comparison to 30 hours for the closest multi-atlas
segmentation method, while reaching similar performance. It also outperforms
the latest state-of-the-art F-CNN models.Comment: Accepted at MICCAI 201
Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks
Segmentation of both white matter lesions and deep grey matter structures is
an important task in the quantification of magnetic resonance imaging in
multiple sclerosis. Typically these tasks are performed separately: in this
paper we present a single segmentation solution based on convolutional neural
networks (CNNs) for providing fast, reliable segmentations of multimodal
magnetic resonance images into lesion classes and normal-appearing grey- and
white-matter structures. We show substantial, statistically significant
improvements in both Dice coefficient and in lesion-wise specificity and
sensitivity, compared to previous approaches, and agreement with individual
human raters in the range of human inter-rater variability. The method is
trained on data gathered from a single centre: nonetheless, it performs well on
data from centres, scanners and field-strengths not represented in the training
dataset. A retrospective study found that the classifier successfully
identified lesions missed by the human raters.
Lesion labels were provided by human raters, while weak labels for other
brain structures (including CSF, cortical grey matter, cortical white matter,
cerebellum, amygdala, hippocampus, subcortical GM structures and choroid
plexus) were provided by Freesurfer 5.3. The segmentations of these structures
compared well, not only with Freesurfer 5.3, but also with FSL-First and
Freesurfer 6.0
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