Deep learning models have been successful in computer vision and medical image analysis. However, training these models frequently requires large labeled image sets whose creation is often very time and labor intensive, for example, in the context of …
We introduce a fluid-based image augmentation method for medical image analysis. In contrast to existing methods, our framework generates anatomically meaningful images via interpolation from the geodesic subspace underlying given samples. Our …
Multi-atlas segmentation (MAS) is a popular image segmen- tation technique for medical images. In this work, we improve the performance of MAS by correcting registration errors be- fore label fusion. Specifically, we use a volumetric displace- ment …
In this work, we improve the performance of multi-atlas segmentation (MAS) by integrating the recently proposed VoteNet model with the joint label fusion (JLF) approach. Specifically, we first illustrate that using a deep convolutional neural network …
Deep convolutional neural networks (CNNs) are state-of-theart for semantic image segmentation, but typically require many labeled training samples. Obtaining 3D segmentations of medical images for supervised training is difficult and labor intensive. …
Deep learning (DL) approaches are state-of-the-art for many medical image segmentation tasks. They offer a number of advantages: they can be trained for specific tasks, computations are fast at test time, and segmentation quality is typically high. …
Semantic segmentation for 3D medical images is an important task for medical image analysis which would benefit from more efficient approaches. We propose a 3D segmentation framework of cascaded fully convolutional networks (FCNs) with contextual …
Segmentation is a fundamental task for extracting semantically meaningful regions from an image. The goal of segmentation algorithms is to accurately assign object labels to each image location. However, image-noise, shortcomings of algorithms, and …