segmentation

Aladdin

This software allows for joint AtLAs builDing and Diffeomorphic regIstration learNing (Aladdin) with pairwise alignment. In contrast to existing atlas-building approaches it uses the atlas as a bridge and incorporates pairwise similarity measures between images which are related indirectly through their atlas registrations.

OAI Analysis 2

This software contains open-source analysis approaches for the Osteoarthritis Initiative (OAI) magnetic resonance image (MRI) data. The analysis code is largely written in Python with the help of ITK and VTK for data I/O and mesh processing as well as PyTorch for the deep learning approaches for segmentation and registration.

Pediatric Airway Atlas

The project aims to provide an analytical for measuring the normality of children’s airways. We build an age-based atlas on multiple CT images of normal subjects. First, we use a segmentation model to extract the airway.

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 …

VoteNet

VoteNet is a deep-learning-based label fusion strategy for multi-atlas segmentation (MAS) which locally selects a set of reliable atlases whose labels are then fused via plurality voting. By selecting a good initial atlas set MAS with VoteNet significantly outperforms a number of other label fusion strategies as well as a direct deep-learning (DL) segmentation approach.

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 …