scholarly journals Unsupervised domain adaptation via CycleGAN for white matter hyperintensity segmentation in multicenter MR images

2020 ◽  
Author(s):  
Julián Alberto Palladino ◽  
Diego Fernandez Slezak ◽  
Enzo Ferrante
Keyword(s):  
White Matter ◽  
Domain Adaptation ◽  
White Matter Hyperintensity ◽  
Mr Images ◽  
Unsupervised Domain Adaptation

scholarly journals Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images

2021 ◽  
pp. 102215
Author(s):  
Vaanathi Sundaresan ◽  
Giovanna Zamboni ◽  
Nicola K. Dinsdale ◽  
Peter M. Rothwell ◽  
Ludovica Griffanti ◽  
...  
Keyword(s):  
White Matter ◽  
Domain Adaptation ◽  
White Matter Hyperintensity ◽  
Mr Images ◽  
Brain Mr Images

scholarly journals Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images

2021 ◽  
Author(s):  
Vaanathi Sundaresan ◽  
Giovanna Zamboni ◽  
Nicola K. Dinsdale ◽  
Peter M. Rothwell ◽  
Ludovica Griffanti ◽  
...  
Keyword(s):  
White Matter ◽  
Transfer Learning ◽  
Domain Adaptation ◽  
White Matter Hyperintensity ◽  
Mr Images ◽  
Target Domain ◽  
Baseline Model ◽  
Adversarial Learning ◽  
Adversarial Training ◽  
Brain Mr Images

AbstractRobust automated segmentation of white matter hyperintensities (WMHs) in different datasets (domains) is highly challenging due to differences in acquisition (scanner, sequence), population (WMH amount and location) and limited availability of manual segmentations to train supervised algorithms. In this work we explore various domain adaptation techniques such as transfer learning and domain adversarial learning methods, including domain adversarial neural networks and domain unlearning, to improve the generalisability of our recently proposed triplanar ensemble network, which is our baseline model. We evaluated the domain adaptation techniques on source and target domains consisting of 5 different datasets with variations in intensity profile, lesion characteristics and acquired using different scanners. For transfer learning, we also studied various training options such as minimal number of unfrozen layers and subjects required for finetuning in the target domain. On comparing the performance of different techniques on the target dataset, unsupervised domain adversarial training of neural network gave the best performance, making the technique promising for robust WMH segmentation.

RA-SIFA: Unsupervised domain adaptation multi-modality cardiac segmentation network combining parallel attention module and residual attention unit

2021 ◽  
pp. 1-14
Author(s):  
Tiejun Yang ◽  
Xiaojuan Cui ◽  
Xinhao Bai ◽  
Lei Li ◽  
Yuehong Gong
Keyword(s):  
Image Segmentation ◽  
Domain Adaptation ◽  
Mr Images ◽  
Unsupervised Domain Adaptation ◽  
Cardiac Image ◽  
Cardiac Segmentation ◽  
Study Results ◽  
Whole Heart ◽  
Cardiac Image Segmentation ◽  
Feature Alignment

BACKGROUND: Convolutional neural network has achieved a profound effect on cardiac image segmentation. The diversity of medical imaging equipment brings the challenge of domain shift for cardiac image segmentation. OBJECTIVE: In order to solve the domain shift existed in multi-modality cardiac image segmentation, this study aims to investigate and test an unsupervised domain adaptation network RA-SIFA, which combines a parallel attention module (PAM) and residual attention unit (RAU). METHODS: First, the PAM is introduced in the generator of RA-SIFA to fuse global information, which can reduce the domain shift from the respect of image alignment. Second, the shared encoder adopts the RAU, which has residual block based on the spatial attention module to alleviate the problem that the convolution layer is insensitive to spatial position. Therefore, RAU enables to further reduce the domain shift from the respect of feature alignment. RA-SIFA model can realize the unsupervised domain adaption (UDA) through combining the image and feature alignment, and then solve the domain shift of cardiac image segmentation in a complementary manner. RESULTS: The model is evaluated using MM-WHS2017 datasets. Compared with SIFA, the Dice of our new RA-SIFA network is improved by 8.4%and 3.2%in CT and MR images, respectively, while, the average symmetric surface distance (ASD) is reduced by 3.4 and 0.8mm in CT and MR images, respectively. CONCLUSION: The study results demonstrate that our new RA-SIFA network can effectively improve the accuracy of whole-heart segmentation from CT and MR images.

scholarly journals Automated White Matter Hyperintensity Detection in Multiple Sclerosis Using 3D T2 FLAIR

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yi Zhong ◽  
David Utriainen ◽  
Ying Wang ◽  
Yan Kang ◽  
E. Mark Haacke
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Gray Matter ◽  
Signal Intensity ◽  
High Spatial Frequency ◽  
Inversion Recovery ◽  
White Matter Hyperintensity ◽  
Mr Images ◽  
Automated Algorithm ◽  
Fluid Attenuated Inversion Recovery

White matter hyperintensities (WMH) seen on T2WI are a hallmark of multiple sclerosis (MS) as it indicates inflammation associated with the disease. Automatic detection of the WMH can be valuable in diagnosing and monitoring of treatment effectiveness. T2 fluid attenuated inversion recovery (FLAIR) MR images provided good contrast between the lesions and other tissue; however the signal intensity of gray matter tissue was close to the lesions in FLAIR images that may cause more false positives in the segment result. We developed and evaluated a tool for automated WMH detection only using high resolution 3D T2 fluid attenuated inversion recovery (FLAIR) MR images. We use a high spatial frequency suppression method to reduce the gray matter area signal intensity. We evaluate our method in 26 MS patients and 26 age matched health controls. The data from the automated algorithm showed good agreement with that from the manual segmentation. The linear correlation between these two approaches in comparing WMH volumes was found to beY=1.04X+1.74  (R2=0.96). The automated algorithm estimates the number, volume, and category of WMH.

Importance-weighted conditional adversarial network for unsupervised domain adaptation

2020 ◽  
Vol 155 ◽  
pp. 113404 ◽  
Author(s):  
Peng Liu ◽  
Ting Xiao ◽  
Cangning Fan ◽  
Wei Zhao ◽  
Xianglong Tang ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Unsupervised Domain Adaptation ◽  
Adversarial Network
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