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2018 GTC San Jose

S8251 - Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation

Session Speakers
Session Description

In medical imaging, acquisition procedures and imaging signals vary across different modalities and, thus, researchers often treat them independently, introducing different models for each imaging modality. To mitigate the number of modality-specific designs, we introduced a simple yet powerful pipeline for medical image segmentation that combines fully convolutional networks (FCNs) with fully convolutional residual networks (FC-ResNets). FCNs are used to obtain normalized images, which are then iteratively refined by means of a FC-ResNet to generate a segmentation prediction. We'll show results that highlight the potential of the proposed pipeline, by matching state-of-the-art performance on a variety of medical imaging modalities, including electron microscopy, computed tomography, and magnetic resonance imaging.


Additional Information
Medical Imaging and Radiology
Healthcare & Life Sciences
Intermediate technical
Talk
25 minutes
Session Schedule