Interactive volume segmentation can be approached via two decoupled modules: interaction-to-segmentation and segmentation propagation. Given a medical volume, a user first segments a slice (or several slices) via the interaction module and then …
Inverse consistency is a desirable property for image registration. We propose a simple technique to make a neural registration network inverse consistent by construction, as a consequence of its structure, as long as it parameterizes its output …
Multiple imaging modalities are often used for disease diagnosis, prediction, or population-based analyses. However, not all modalities might be available due to cost, different study designs, or changes in imaging technology. If the differences …
Multimodal learning has mainly focused on learning large models on, and fusing feature representations from, different modalities for better performances on downstream tasks. In this work, we take a detour from this trend and study the intrinsic …
Deep implicit functions (DIFs) have emerged as a powerful paradigm for many computer vision tasks such as 3D shape reconstruction, generation, registration, completion, editing, and understanding. However, given a set of 3D shapes with associated …
Inspired by recent findings that generative diffusion models learn semantically meaningful representations, we use them to discover the intrinsic hierarchical structure in biomedical 3D images using unsupervised segmentation. We show that features of …
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.
ICON (Inverse COnsistent RegistratioN) is a non-parametric deep learning registration approach which relies only on inverse consistency for regularity. As the regularization neither involves explicit smoothing or a penality on spatial derivatives no affine pre-registration is required.
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.
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.