Search Results for author:
Found 18 papers, 6 papers with code
InstantGroup: Instant Template Generation for Scalable Group of Brain MRI Registration
Template generation is a critical step in groupwise image registration, which involves aligning a group of subjects into a common space.
Robust Image Registration with Absent Correspondences in Pre-operative and Follow-up Brain MRI Scans of Diffuse Glioma Patients
Registration of pre-operative and follow-up brain MRI scans is challenging due to the large variation of tissue appearance and missing correspondences in tumour recurrence regions caused by tumour mass effect.
Unsupervised Deformable Image Registration with Absent Correspondences in Pre-operative and Post-Recurrence Brain Tumor MRI Scans
Registration of pre-operative and post-recurrence brain images is often needed to evaluate the effectiveness of brain gliomas treatment.
Affine Medical Image Registration with Coarse-to-Fine Vision Transformer
Comprehensive results demonstrate that our method is superior to the existing CNNs-based affine registration methods in terms of registration accuracy, robustness and generalizability while preserving the runtime advantage of the learning-based methods.
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.
Conditional Deformable Image Registration with Convolutional Neural Network
In this paper, we propose a conditional image registration method and a new self-supervised learning paradigm for deep deformable image registration.
Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Prediction
There is a rising need for computational models that can complementarily leverage data of different modalities while investigating associations between subjects for population-based disease analysis.
Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks
Deep learning-based methods have recently demonstrated promising results in deformable image registration for a wide range of medical image analysis tasks.
Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networks
However, these approaches often ignore the topology preservation of the transformation and the smoothness of the transformation which is enforced by a global smoothing energy function alone.
CELNet: Evidence Localization for Pathology Images using Weakly Supervised Learning
Despite deep convolutional neural networks boost the performance of image classification and segmentation in digital pathology analysis, they are usually weak in interpretability for clinical applications or require heavy annotations to achieve object localization.
A Fine-Grain Error Map Prediction and Segmentation Quality Assessment Framework for Whole-Heart Segmentation
In this work, we attempt to address the pixel-wise error map prediction problem and the per-case mask quality assessment problem using a unified deep learning (DL) framework.
A Unified Mammogram Analysis Method via Hybrid Deep Supervision
In this work, we present a unified mammogram analysis framework for both whole-mammogram classification and segmentation.
Deep supervision with additional labels for retinal vessel segmentation task
In this way, the network will pay more attention to the boundary areas of vessels and achieve a better performance, especially in tiny vessels detecting.
Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks
While it is often easy for researchers to use data augmentation to expand the size of training sets, constructing and generating generic augmented data that is able to teach the network the desired invariance and robustness properties using traditional data augmentation techniques is challenging in practice.
3D Randomized Connection Network with Graph-based Label Inference
In this paper, a novel 3D deep learning network is proposed for brain MR image segmentation with randomized connection, which can decrease the dependency between layers and increase the network capacity.
Feature Sensitive Label Fusion with Random Walker for Atlas-based Image Segmentation
This label fusion method is formulated on a graph, which embraces both label priors from atlases and anatomical priors from target image.
Graph-Based Optimization with Tubularity Markov Tree for 3D Vessel Segmentation
In this paper, we propose a graph-based method for 3D vessel tree structure segmentation based on a new tubularity Markov tree model (TMT ), which works as both new energy function and graph construction method.
