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Retinal fluid segmentation in OCT images using adversarial loss based convolutional neural networks | IEEE Conference Publication | IEEE Xplore

Retinal fluid segmentation in OCT images using adversarial loss based convolutional neural networks


Abstract:

This paper proposes a novel method in order to obtain voxel-level segmentation for three fluid lesion types (IR-F/SRF/PED) in OCT images provided by the ReTOUCH challenge...Show More

Abstract:

This paper proposes a novel method in order to obtain voxel-level segmentation for three fluid lesion types (IR-F/SRF/PED) in OCT images provided by the ReTOUCH challenge [1]. The method is based on a deep neural network consisting of encoding and de-coding blocks connected with skip-connections which was trained using a combined cost function comprising of cross-entropy, dice and adversarial loss terms. The segmentation results on a held-out validation set shows that the network architecture and the loss functions used has resulted in improved retinal fluid segmentation. Our method was ranked fourth in the ReTOUCH challenge.
Date of Conference: 04-07 April 2018
Date Added to IEEE Xplore: 24 May 2018
ISBN Information:
Electronic ISSN: 1945-8452
Conference Location: Washington, DC, USA

References

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