Ensemble of Fully Convolutional Neural Network for Brain Tumor Segmentation from Magnetic Resonance Images View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2019-01-26

AUTHORS

Avinash Kori , Mehul Soni , B. Pranjal , Mahendra Khened , Varghese Alex , Ganapathy Krishnamurthi

ABSTRACT

We utilize an ensemble of the fully convolutional neural networks (CNN) for segmentation of gliomas and its constituents from multimodal Magnetic Resonance Images (MRI). The ensemble comprises of 3 networks, two 3-D and one 2-D network. Of the 3 networks, 2 of them (one 2-D & one 3-D) utilize dense connectivity patterns while the other 3-D network makes use of the residual connection. Additionally, a 2-D fully convolutional semantic segmentation network was trained to distinguish between air, brain, and lesion in the slice and thereby localize the lesion the volume. Lesion localized by the above network was multiplied with the segmentation mask generated by the ensemble to reduce false positives. On the BraTS validation data (n = 66), the scheme utilized in this manuscript achieved a whole tumor, tumor core and active tumor dice of 0.89 0.76, 0.76 respectively, while on the BraTS test data (n = 191), our scheme achieved the whole tumor, tumor core and active tumor dice of 0.83 0.72, 0.69 respectively. More... »

PAGES

485-496

Book

TITLE

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

ISBN

978-3-030-11725-2
978-3-030-11726-9

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-11726-9_43

DOI

http://dx.doi.org/10.1007/978-3-030-11726-9_43

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111721805


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