.png){kind=icon}
Return
Realistic image normalization for multi-Domain segmentation
.png){kind=icon}
OA
AI
DOI：10.1016/j.media.2021.102191.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Image normalization is a building block in medical image analysis. Conventional approaches are customarily employed on a per-dataset basis. This strategy, however, prevents the current normalization algorithms from fully exploiting the complex joint information available across multiple datasets. Consequently, ignoring such joint information has a direct impact on the processing of segmentation algorithms. This paper proposes to revisit the conventional image normalization approach by, instead, learning a common normalizing function across multiple datasets. Jointly normalizing multiple datasets is shown to yield consistent normalized images as well as an improved image segmentation when intensity shifts are large. To do so, a fully automated adversarial and task-driven normalization approach is employed as it facilitates the training of realistic and interpretable images while keeping performance on par with the state-of-the-art. The adversarial training of our network aims at finding the optimal transfer function to improve both, jointly, the segmentation accuracy and the generation of realistic images. We have evaluated the performance of our normalizer on both infant and adult brain images from the iSEG, MRBrainS and ABIDE datasets. The results indicate that our contribution does provide an improved realism to the normalized images, while retaining a segmentation accuracy at par with the state-of-the-art learnable normalization approaches. (c) 2021 Elsevier B.V. All rights reserved.
Keywords:
3D MRI
Brain segmentation
Data harmonization
Generative adversarial networks
Intensity normalization
Journal
IF:
11.8
Papers:
3.9K
Citations:
2.4W
Organization
Cited Papers
Evaluating White Matter Lesion Segmentations with Refined SOrensen-Dice Analysis.png)
SCIENTIFIC REPORTS
OAAI
SCIENTIFIC REPORTSIF3.9
OAAIShareSaveShareSaveBenchmark on Automatic Six-Month-Old Infant Brain Segmentation Algorithms: The iSeg-2017 ChallengeOAAI
OAAIShareSaveShareSaveLearning normalized inputs for iterative estimation in medical image segmentationMEDICAL IMAGE ANALYSISOAAI
MEDICAL IMAGE ANALYSISIF11.8
OAAIShareSave
View more