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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we present a novel automated method for White Matter (WM)lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach isbased on a cascade of two 3D patch-wise convolutional neural networks (CNN).The first network is trained to be more sensitive revealing possible candidatelesion voxels while the second network is trained to reduce the number ofmisclassified voxels coming from the first network. This cascaded CNNarchitecture tends to learn well from small sets of training data, which can bevery interesting in practice, given the difficulty to obtain manual labelannotations and the large amount of available unlabeled Magnetic ResonanceImaging (MRI) data. We evaluate the accuracy of the proposed method on thepublic MS lesion segmentation challenge MICCAI2008 dataset, comparing it withrespect to other state-of-the-art MS lesion segmentation tools. Furthermore,the proposed method is also evaluated on two private MS clinical datasets,where the performance of our method is also compared with different recentpublic available state-of-the-art MS lesion segmentation methods. At the timeof writing this paper, our method is the best ranked approach on the MICCAI2008challenge, outperforming the rest of 60 participant methods when using all theavailable input modalities (T1-w, T2-w and FLAIR), while still in the top-rank(3rd position) when using only T1-w and FLAIR modalities. On clinical MS data,our approach exhibits a significant increase in the accuracy segmenting of WMlesions when compared with the rest of evaluated methods, highly correlating($r \ge 0.97$) also with the expected lesion volume.

Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach