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

Deep neural networks have demonstrated very promising performance on accuratesegmentation of challenging organs (e.g., pancreas) in abdominal CT and MRIscans. The current deep learning approaches conduct pancreas segmentation byprocessing sequences of 2D image slices independently through deep, denseper-pixel masking for each image, without explicitly enforcing spatialconsistency constraint on segmentation of successive slices. We propose a newconvolutional/recurrent neural network architecture to address the contextuallearning and segmentation consistency problem. A deep convolutional sub-networkis first designed and pre-trained from scratch. The output layer of thisnetwork module is then connected to recurrent layers and can be fine-tuned forcontextual learning, in an end-to-end manner. Our recurrent sub-network is atype of Long short-term memory (LSTM) network that performs segmentation on animage by integrating its neighboring slice segmentation predictions, in theform of a dependent sequence processing. Additionally, a novelsegmentation-direct loss function (named Jaccard Loss) is proposed and deepnetworks are trained to optimize Jaccard Index (JI) directly. Extensiveexperiments are conducted to validate our proposed deep models, on quantitativepancreas segmentation using both CT and MRI scans. Our method outperforms thestate-of-the-art work on CT [11] and MRI pancreas segmentation [1],respectively.

Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function