Multi-modal Perceptual Adversarial Learning for Longitudinal Prediction of Infant MR Images

2020 ◽  
pp. 284-294
Author(s):  
Liying Peng ◽  
Lanfen Lin ◽  
Yusen Lin ◽  
Yue Zhang ◽  
Roza M. Vlasova ◽  
...  
Keyword(s):  
Mr Images ◽  
Adversarial Learning

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.

scholarly journals Longitudinal Prediction of Infant MR Images With Multi-Contrast Perceptual Adversarial Learning

2021 ◽  
Vol 15 ◽  
Author(s):  
Liying Peng ◽  
Lanfen Lin ◽  
Yusen Lin ◽  
Yen-wei Chen ◽  
Zhanhao Mo ◽  
...  
Keyword(s):  
Missing Data ◽  
Neural Development ◽  
Brain Size ◽  
Generative Adversarial Networks ◽  
First Year ◽  
Data Imputation ◽  
Mr Images ◽  
Adversarial Learning ◽  
Infant Brain ◽  
First Year Of Life

The infant brain undergoes a remarkable period of neural development that is crucial for the development of cognitive and behavioral capacities (Hasegawa et al., 2018). Longitudinal magnetic resonance imaging (MRI) is able to characterize the developmental trajectories and is critical in neuroimaging studies of early brain development. However, missing data at different time points is an unavoidable occurrence in longitudinal studies owing to participant attrition and scan failure. Compared to dropping incomplete data, data imputation is considered a better solution to address such missing data in order to preserve all available samples. In this paper, we adapt generative adversarial networks (GAN) to a new application: longitudinal image prediction of structural MRI in the first year of life. In contrast to existing medical image-to-image translation applications of GANs, where inputs and outputs share a very close anatomical structure, our task is more challenging as brain size, shape and tissue contrast vary significantly between the input data and the predicted data. Several improvements over existing GAN approaches are proposed to address these challenges in our task. To enhance the realism, crispness, and accuracy of the predicted images, we incorporate both a traditional voxel-wise reconstruction loss as well as a perceptual loss term into the adversarial learning scheme. As the differing contrast changes in T1w and T2w MR images in the first year of life, we incorporate multi-contrast images leading to our proposed 3D multi-contrast perceptual adversarial network (MPGAN). Extensive evaluations are performed to assess the qualityand fidelity of the predicted images, including qualitative and quantitative assessments of the image appearance, as well as quantitative assessment on two segmentation tasks. Our experimental results show that our MPGAN is an effective solution for longitudinal MR image data imputation in the infant brain. We further apply our predicted/imputed images to two practical tasks, a regression task and a classification task, in order to highlight the enhanced task-related performance following image imputation. The results show that the model performance in both tasks is improved by including the additional imputed data, demonstrating the usability of the predicted images generated from our approach.

scholarly journals Automatic Meniscus Segmentation Using Adversarial Learning-Based Segmentation Network with Object-Aware Map in Knee MR Images

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1612
Author(s):  
Uju Jeon ◽  
Hyeonjin Kim ◽  
Helen Hong ◽  
Joonho Wang
Keyword(s):  
3D Reconstruction ◽  
Medial Meniscus ◽  
Lateral Meniscus ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Mr Images ◽  
Generative Adversarial Network ◽  
Adversarial Learning ◽  
Allograft Transplantation ◽  
Reconstruction Model

Meniscus segmentation from knee MR images is an essential step when analyzing the length, width, height, cross-sectional area, surface area for meniscus allograft transplantation using a 3D reconstruction model based on the patient’s normal meniscus. In this paper, we propose a two-stage DCNN that combines a 2D U-Net-based meniscus localization network with a conditional generative adversarial network-based segmentation network using an object-aware map. First, the 2D U-Net segments knee MR images into six classes including bone and cartilage with whole MR images at a resolution of 512 × 512 to localize the medial and lateral meniscus. Second, adversarial learning with a generator based on the 2D U-Net and a discriminator based on the 2D DCNN using an object-aware map segments the meniscus into localized regions-of-interest with a resolution of 64 × 64. The average Dice similarity coefficient of the meniscus was 85.18% at the medial meniscus and 84.33% at the lateral meniscus; these values were 10.79%p and 1.14%p, and 7.78%p and 1.12%p higher than the segmentation method without adversarial learning and without the use of an object-aware map with the Dice similarity coefficient at the medial meniscus and lateral meniscus, respectively. The proposed automatic meniscus localization through multi-class can prevent the class imbalance problem by focusing on local regions. The proposed adversarial learning using an object-aware map can prevent under-segmentation by repeatedly judging and improving the segmentation results, and over-segmentation by considering information only from the meniscus regions. Our method can be used to identify and analyze the shape of the meniscus for allograft transplantation using a 3D reconstruction model of the patient’s unruptured meniscus.

Adrenocortical Scintigraphy with 131I-6-Beta-Iodomethyl-NorchoIesterol (NP 59) in Bilateral Adrenocortical Carcinoma

1998 ◽  
Vol 37 (04) ◽  
pp. 141-145
Author(s):  
F. J. C. Pallarés ◽  
A. R. Bartual ◽  
Susana Tenes Rodrigo ◽  
F. J. Ampudia-Blasco ◽  
C. R. de Ávila y Ávalos ◽  
...  
Keyword(s):  
Case Report ◽  
Literature Review ◽  
Adrenocortical Carcinoma ◽  
Rapid Progression ◽  
Histological Findings ◽  
Mr Images ◽  
Imaging Procedure ◽  
Adrenocortical Scintigraphy

SummaryA case of a 49-year-old man suffering from bilateral adrenocortical carcinoma with local and secondary rapid progression is reported. The results of adrenocortical scintigraphy (NP 59) and histological findings allowed the diagnosis. This case report and a literature review showed the importance of using adrenocortical scintigraphy as a complementary imaging procedure of CT or MR images.

Measurement of Lipid Content in Gallbladder Bile Using in- and opposed-phase MR Images and in vivo Proton MR Spectroscopy

2002 ◽  
Vol 46 (2) ◽  
pp. 127
Author(s):  
Sun Jin Hur ◽  
Seok Hwan Shin ◽  
Geum Nan Jee ◽  
Eun Joo Yun ◽  
Soon Gu Cho ◽  
...  
Keyword(s):  
Lipid Content ◽  
Mr Spectroscopy ◽  
Gallbladder Bile ◽  
Mr Images ◽  
Proton Mr Spectroscopy
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