segmentation

Votenet++: Registration Refinement For Multi-Atlas Segmentation

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 …

Image Segmentation

We have developed multiple image segmentation methods.

Osteoarthritis

Image analysis approaches for the quantitative analysis of osteoarthritis in the knee.

Adversarial Data Augmentation via Deformation Statistics

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 …

Anatomical Data Augmentation via Fluid-Based Image Registration

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 …

Local Temperature Scaling for Probability Calibration

For semantic segmentation, label probabilities are often uncalibrated as they are typically only the by-product of a segmentation task. Intersection over Union (IoU) and Dice score are often used as criteria for segmentation success, while metrics …

Votenet+: An Improved Deep Learning Label Fusion Method for Multi-Atlas Segmentation

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 …

DeepAtlas: Joint Semi-supervised Learning of Image Registration and Segmentation

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. …

VoteNet: A Deep Learning Label Fusion Method for Multi-atlas Segmentation

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. …

Contextual Additive Networks to Efficiently Boost 3D Image Segmentations

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 …