171 research outputs found
Benchmark on automatic 6-month-old infant brain segmentation algorithms: the iSeg-2017 challenge
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR images, making tissue segmentation very challenging. Despite many efforts were devoted to brain segmentation, only few studies have focused on the segmentation of 6-month infant brain images. With the idea of boosting methodological development in the community, iSeg-2017 challenge (http://iseg2017.web.unc.edu) provides a set of 6-month infant subjects with manual labels for training and testing the participating methods. Among the 21 automatic segmentation methods participating in iSeg-2017, we review the 8 top-ranked teams, in terms of Dice ratio, modified Hausdorff distance and average surface distance, and introduce their pipelines, implementations, as well as source codes. We further discuss limitations and possible future directions. We hope the dataset in iSeg-2017 and this review article could provide insights into methodological development for the community.Peer ReviewedPostprint (published version
Role of deep learning in infant brain MRI analysis
Deep learning algorithms and in particular convolutional networks have shown tremendous success in medical image analysis applications, though relatively few methods have been applied to infant MRI data due numerous inherent challenges such as inhomogenous tissue appearance across the image, considerable image intensity variability across the first year of life, and a low signal to noise setting. This paper presents methods addressing these challenges in two selected applications, specifically infant brain tissue segmentation at the isointense stage and presymptomatic disease prediction in neurodevelopmental disorders. Corresponding methods are reviewed and compared, and open issues are identified, namely low data size restrictions, class imbalance problems, and lack of interpretation of the resulting deep learning solutions. We discuss how existing solutions can be adapted to approach these issues as well as how generative models seem to be a particularly strong contender to address them
Brain MRI Tumor Segmentation with Adversarial Networks
Deep Learning is a promising approach to either automate or simplify several
tasks in the healthcare domain. In this work, we introduce SegAN-CAT, an
approach to brain tumor segmentation in Magnetic Resonance Images (MRI), based
on Adversarial Networks. In particular, we extend SegAN, successfully applied
to the same task in a previous work, in two respects: (i) we used a different
model input and (ii) we employed a modified loss function to train the model.
We tested our approach on two large datasets, made available by the Brain Tumor
Image Segmentation Benchmark (BraTS). First, we trained and tested some
segmentation models assuming the availability of all the major MRI contrast
modalities, i.e., T1-weighted, T1 weighted contrast-enhanced, T2-weighted, and
T2-FLAIR. However, as these four modalities are not always all available for
each patient, we also trained and tested four segmentation models that take as
input MRIs acquired only with a single contrast modality. Finally, we proposed
to apply transfer learning across different contrast modalities to improve the
performance of these single-modality models. Our results are promising and show
that not SegAN-CAT is able to outperform SegAN when all the four modalities are
available, but also that transfer learning can actually lead to better
performances when only a single modality is available
Learning to segment fetal brain tissue from noisy annotations
Automatic fetal brain tissue segmentation can enhance the quantitative
assessment of brain development at this critical stage. Deep learning methods
represent the state of the art in medical image segmentation and have also
achieved impressive results in brain segmentation. However, effective training
of a deep learning model to perform this task requires a large number of
training images to represent the rapid development of the transient fetal brain
structures. On the other hand, manual multi-label segmentation of a large
number of 3D images is prohibitive. To address this challenge, we segmented 272
training images, covering 19-39 gestational weeks, using an automatic
multi-atlas segmentation strategy based on deformable registration and
probabilistic atlas fusion, and manually corrected large errors in those
segmentations. Since this process generated a large training dataset with noisy
segmentations, we developed a novel label smoothing procedure and a loss
function to train a deep learning model with smoothed noisy segmentations. Our
proposed methods properly account for the uncertainty in tissue boundaries. We
evaluated our method on 23 manually-segmented test images of a separate set of
fetuses. Results show that our method achieves an average Dice similarity
coefficient of 0.893 and 0.916 for the transient structures of younger and
older fetuses, respectively. Our method generated results that were
significantly more accurate than several state-of-the-art methods including
nnU-Net that achieved the closest results to our method. Our trained model can
serve as a valuable tool to enhance the accuracy and reproducibility of fetal
brain analysis in MRI
CT-LungNet: A Deep Learning Framework for Precise Lung Tissue Segmentation in 3D Thoracic CT Scans
Segmentation of lung tissue in computed tomography (CT) images is a precursor
to most pulmonary image analysis applications. Semantic segmentation methods
using deep learning have exhibited top-tier performance in recent years,
however designing accurate and robust segmentation models for lung tissue is
challenging due to the variations in shape, size, and orientation.
Additionally, medical image artifacts and noise can affect lung tissue
segmentation and degrade the accuracy of downstream analysis. The practicality
of current deep learning methods for lung tissue segmentation is limited as
they require significant computational resources and may not be easily
deployable in clinical settings. This paper presents a fully automatic method
that identifies the lungs in three-dimensional (3D) pulmonary CT images using
deep networks and transfer learning. We introduce (1) a novel 2.5-dimensional
image representation from consecutive CT slices that succinctly represents
volumetric information and (2) a U-Net architecture equipped with pre-trained
InceptionV3 blocks to segment 3D CT scans while maintaining the number of
learnable parameters as low as possible. Our method was quantitatively assessed
using one public dataset, LUNA16, for training and testing and two public
datasets, namely, VESSEL12 and CRPF, only for testing. Due to the low number of
learnable parameters, our method achieved high generalizability to the unseen
VESSEL12 and CRPF datasets while obtaining superior performance over Luna16
compared to existing methods (Dice coefficients of 99.7, 99.1, and 98.8 over
LUNA16, VESSEL12, and CRPF datasets, respectively). We made our method publicly
accessible via a graphical user interface at medvispy.ee.kntu.ac.ir
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