166 research outputs found
Multi-dataset Training for Medical Image Segmentation as a Service
Deep Learning tools are widely used for medical image segmentation. The results produced by these techniques depend to a great extent on the data sets used to train the used network. Nowadays many cloud service providers offer the required resources to train networks and deploy deep learning networks. This makes the idea of segmentation as a cloud-based service attractive. In this paper we study the possibility of training, a generalized configurable, Keras U-Net to test the feasibility of training with images acquired, with specific instruments, to perform predictions on data from other instruments. We use, as our application example, the segmentation of Optic Disc and Cup which can be applied to glaucoma detection. We use two publicly available data sets (RIM-One V3 and DRISHTI) to train either independently or combining their data.Ministerio de Economía y Competitividad TEC2016-77785-
Reconstruction-driven Dynamic Refinement based Unsupervised Domain Adaptation for Joint Optic Disc and Cup Segmentation
Glaucoma is one of the leading causes of irreversible blindness. Segmentation
of optic disc (OD) and optic cup (OC) on fundus images is a crucial step in
glaucoma screening. Although many deep learning models have been constructed
for this task, it remains challenging to train an OD/OC segmentation model that
could be deployed successfully to different healthcare centers. The
difficulties mainly comes from the domain shift issue, i.e., the fundus images
collected at these centers usually vary greatly in the tone, contrast, and
brightness. To address this issue, in this paper, we propose a novel
unsupervised domain adaptation (UDA) method called Reconstruction-driven
Dynamic Refinement Network (RDR-Net), where we employ a due-path segmentation
backbone for simultaneous edge detection and region prediction and design three
modules to alleviate the domain gap. The reconstruction alignment (RA) module
uses a variational auto-encoder (VAE) to reconstruct the input image and thus
boosts the image representation ability of the network in a self-supervised
way. It also uses a style-consistency constraint to force the network to retain
more domain-invariant information. The low-level feature refinement (LFR)
module employs input-specific dynamic convolutions to suppress the
domain-variant information in the obtained low-level features. The
prediction-map alignment (PMA) module elaborates the entropy-driven adversarial
learning to encourage the network to generate source-like boundaries and
regions. We evaluated our RDR-Net against state-of-the-art solutions on four
public fundus image datasets. Our results indicate that RDR-Net is superior to
competing models in both segmentation performance and generalization abilit
EDDense-Net: Fully Dense Encoder Decoder Network for Joint Segmentation of Optic Cup and Disc
Glaucoma is an eye disease that causes damage to the optic nerve, which can
lead to visual loss and permanent blindness. Early glaucoma detection is
therefore critical in order to avoid permanent blindness. The estimation of the
cup-to-disc ratio (CDR) during an examination of the optical disc (OD) is used
for the diagnosis of glaucoma. In this paper, we present the EDDense-Net
segmentation network for the joint segmentation of OC and OD. The encoder and
decoder in this network are made up of dense blocks with a grouped
convolutional layer in each block, allowing the network to acquire and convey
spatial information from the image while simultaneously reducing the network's
complexity. To reduce spatial information loss, the optimal number of filters
in all convolution layers were utilised. In semantic segmentation, dice pixel
classification is employed in the decoder to alleviate the problem of class
imbalance. The proposed network was evaluated on two publicly available
datasets where it outperformed existing state-of-the-art methods in terms of
accuracy and efficiency. For the diagnosis and analysis of glaucoma, this
method can be used as a second opinion system to assist medical
ophthalmologists
Retinal Optic Disc Segmentation using Conditional Generative Adversarial Network
This paper proposed a retinal image segmentation method based on conditional
Generative Adversarial Network (cGAN) to segment optic disc. The proposed model
consists of two successive networks: generator and discriminator. The generator
learns to map information from the observing input (i.e., retinal fundus color
image), to the output (i.e., binary mask). Then, the discriminator learns as a
loss function to train this mapping by comparing the ground-truth and the
predicted output with observing the input image as a condition.Experiments were
performed on two publicly available dataset; DRISHTI GS1 and RIM-ONE. The
proposed model outperformed state-of-the-art-methods by achieving around 0.96%
and 0.98% of Jaccard and Dice coefficients, respectively. Moreover, an image
segmentation is performed in less than a second on recent GPU.Comment: 8 pages, Submitted to 21st International Conference of the Catalan
Association for Artificial Intelligence (CCIA 2018
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