Search Results for author:
Found 26 papers, 14 papers with code
Self-supervised Learning of Dense Hierarchical Representations for Medical Image Segmentation
This paper demonstrates a self-supervised framework for learning voxel-wise coarse-to-fine representations tailored for dense downstream tasks.
DG-TTA: Out-of-domain medical image segmentation through Domain Generalization and Test-Time Adaptation
In this study, we propose to combine domain generalization and test-time adaptation to create a highly effective approach for reusing pre-trained models in unseen target domains.
Shape Matters: Detecting Vertebral Fractures Using Differentiable Point-Based Shape Decoding
In this study, we specifically explore the use of shape auto-encoders for vertebrae, taking advantage of advancements in automated multi-label segmentation and the availability of large datasets for unsupervised learning.
Unsupervised 3D registration through optimization-guided cyclical self-training
State-of-the-art deep learning-based registration methods employ three different learning strategies: supervised learning, which requires costly manual annotations, unsupervised learning, which heavily relies on hand-crafted similarity metrics designed by domain experts, or learning from synthetic data, which introduces a domain shift.
A denoised Mean Teacher for domain adaptive point cloud registration
Self-training with the Mean Teacher is an established approach to this problem but is impaired by the inherent noise of the pseudo labels from the teacher.
Chasing Clouds: Differentiable Volumetric Rasterisation of Point Clouds as a Highly Efficient and Accurate Loss for Large-Scale Deformable 3D Registration
Learning-based registration for large-scale 3D point clouds has been shown to improve robustness and accuracy compared to classical methods and can be trained without supervision for locally rigid problems.
Anatomy-guided domain adaptation for 3D in-bed human pose estimation
As a remedy, we present a novel domain adaptation method, adapting a model from a labeled source to a shifted unlabeled target domain.
Adapting the Mean Teacher for keypoint-based lung registration under geometric domain shifts
We build on a keypoint-based registration model, combining graph convolutions for geometric feature learning with loopy belief optimization, and propose to reduce the domain shift through self-ensembling.
Voxelmorph++ Going beyond the cranial vault with keypoint supervision and multi-channel instance optimisation
Extending the method to semantic features sets new stat-of-the-art performance on inter-subject abdominal CT registration.
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation
The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 as provided in the testing set (N=137).
The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021
Current approaches for deformable medical image registration often struggle to fulfill all of the following criteria: versatile applicability, small computation or training times, and the being able to estimate large deformations.
Deep learning based geometric registration for medical images: How accurate can we get without visual features?
As in other areas of medical image analysis, e. g. semantic segmentation, deep learning is currently driving the development of new approaches for image registration.
Development and Characterization of a Chest CT Atlas
For the entire study cohort, the optimized pipeline achieves a registration success rate of 91. 7%.
Unsupervised learning of multimodal image registration using domain adaptation with projected Earth Move's discrepancies
We believe that unsupervised domain adaptation can be beneficial in overcoming the current limitations for multimodal registration, where good metrics are hard to define.
Tackling the Problem of Large Deformations in Deep Learning Based Medical Image Registration Using Displacement Embeddings
Though, deep learning based medical image registration is currently starting to show promising advances, often, it still fells behind conventional frameworks in terms of registration accuracy.
Learning to map between ferns with differentiable binary embedding networks
Current deep learning methods are based on the repeated, expensive application of convolutions with parameter-intensive weight matrices.
Segmentation of Retinal Low-Cost Optical Coherence Tomography Images using Deep Learning
It is shown that the CNN-based approach can segment the retina with high accuracy, whereas the segmentation of the PED proves to be challenging.
Learning Deformable Point Set Registration with Regularized Dynamic Graph CNNs for Large Lung Motion in COPD Patients
Our results indicate that the inherent geometric structure of the extracted keypoints is sufficient to establish descriptive point features, which yield a significantly improved performance and robustness of our registration framework.
Closing the Gap between Deep and Conventional Image Registration using Probabilistic Dense Displacement Networks
Nonlinear image registration continues to be a fundamentally important tool in medical image analysis.
FastReg: Fast Non-Rigid Registration via Accelerated Optimisation on the Manifold of Diffeomorphisms
We present an implementation of a new approach to diffeomorphic non-rigid registration of medical images.
Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge
This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017.
Multi-Kernel Diffusion CNNs for Graph-Based Learning on Point Clouds
Graph convolutional networks are a new promising learning approach to deal with data on irregular domains.
Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registration
Our method is designed for large respiratory motion by integrating sparse keypoint correspondences into a dense continuous optimization framework.
TernaryNet: Faster Deep Model Inference without GPUs for Medical 3D Segmentation using Sparse and Binary Convolutions
We propose a new scheme that approximates both trainable weights and neural activations in deep networks by ternary values and tackles the open question of backpropagation when dealing with non-differentiable functions.
