English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Lesions that appear hyperintense in both Fluid Attenuated Inversion Recovery(FLAIR) and T2-weighted magnetic resonance images (MRIs) of the human brain arecommon in the brains of the elderly population and may be caused by ischemia ordemyelination. Lesions are biomarkers for various neurodegenerative diseases,making accurate quantification of them important for both disease diagnosis andprogression. Automatic lesion detection using supervised learning requiresmanually annotated images, which can often be impractical to acquire.Unsupervised lesion detection, on the other hand, does not require any manualdelineation; however, these methods can be challenging to construct due to thevariability in lesion load, placement of lesions, and voxel intensities. Herewe present a novel approach to address this problem using a convolutionalautoencoder, which learns to segment brain lesions as well as the white matter,gray matter, and cerebrospinal fluid by reconstructing FLAIR images as conicalcombinations of softmax layer outputs generated from the corresponding T1, T2,and FLAIR images. Some of the advantages of this model are that it accuratelylearns to segment lesions regardless of lesion load, and it can be used toquickly and robustly segment new images that were not in the training set.Comparisons with state-of-the-art segmentation methods evaluated on groundtruth manual labels indicate that the proposed method works well for generatingaccurate lesion segmentations without the need for manual annotations.

Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder