Search Results for author:
Found 11 papers, 3 papers with code
Weighted Monte Carlo augmented spherical Fourier-Bessel convolutional layers for 3D abdominal organ segmentation
Filter-decomposition-based group equivariant convolutional neural networks show promising stability and data efficiency for 3D image feature extraction.
Robust-DefReg: A Robust Deformable Point Cloud Registration Method based on Graph Convolutional Neural Networks
Point cloud registration is a fundamental problem in computer vision that aims to estimate the transformation between corresponding sets of points.
Adaptive aggregation of Monte Carlo augmented decomposed filters for efficient group-equivariant convolutional neural network
Experiments on group equivariant tests show how our methods can achieve superior performance to parameter-sharing group equivariant networks.
Mask Mining for Improved Liver Lesion Segmentation
We propose a novel procedure to improve liver and lesion segmentation from CT scans for U-Net based models.
Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss function
At present, lesion segmentation is still performed manually (or semi-automatically) by medical experts.
Ranked #3 on
Liver Segmentation
on LiTS2017
(Dice metric)
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
Automated Multiscale 3D Feature Learning for Vessels Segmentation in Thorax CT Images
Following their idea of feature learning instead of hand-crafted filters, we have extended the method to learn 3D features.
Fully Convolutional Deep Network Architectures for Automatic Short Glass Fiber Semantic Segmentation from CT scans
We present the first attempt to perform short glass fiber semantic segmentation from X-ray computed tomography volumetric datasets at medium (3. 9 {\mu}m isotropic) and low (8. 3 {\mu}m isotropic) resolution using deep learning architectures.
Reference Setup for Quantitative Comparison of Segmentation Techniques for Short Glass Fiber CT Data
We suggest both the metrics and this data set as a reference for studying the performance of different algorithms.
Instance Segmentation of Fibers from Low Resolution CT Scans via 3D Deep Embedding Learning
We propose a novel approach for automatic extraction (instance segmentation) of fibers from low resolution 3D X-ray computed tomography scans of short glass fiber reinforced polymers.
MMSE Estimation for Poisson Noise Removal in Images
Poisson noise suppression is an important preprocessing step in several applications, such as medical imaging, microscopy, and astronomical imaging.
