Search Results for author:
Found 8 papers, 1 papers with code
Selecting the Best Optimizers for Deep Learning based Medical Image Segmentation
To this end, we proposed a new cyclic optimization method (\textit{CLMR}) to address the efficiency and accuracy problems in deep learning based medical image segmentation.
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression.
Weakly Supervised Segmentation by A Deep Geodesic Prior
To alleviate this limitation, in this study, we propose a weakly supervised image segmentation method based on a deep geodesic prior.
PAN: Projective Adversarial Network for Medical Image Segmentation
Adversarial learning has been proven to be effective for capturing long-range and high-level label consistencies in semantic segmentation.
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.
Automatically Designing CNN Architectures for Medical Image Segmentation
Deep neural network architectures have traditionally been designed and explored with human expertise in a long-lasting trial-and-error process.
Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT
These measurements are derived as outcomes of precise segmentation of the heart and its substructures.
CardiacNET: Segmentation of Left Atrium and Proximal Pulmonary Veins from MRI Using Multi-View CNN
Anatomical and biophysical modeling of left atrium (LA) and proximal pulmonary veins (PPVs) is important for clinical management of several cardiac diseases.
