Moment-Consistent Contrastive CycleGAN for Cross-Domain Pancreatic Image Segmentation.

CT and MR are currently the most common imaging techniques for pancreatic cancer diagnosis. Accurate segmentation of the pancreas in CT and MR images can provide significant help in the diagnosis and treatment of pancreatic cancer. Traditional supervised segmentation methods require a large number of labeled CT and MR training data, which is usually time-consuming and laborious. Meanwhile, due to domain shift, traditional segmentation networks are difficult to be deployed on different imaging modality datasets. Cross-domain segmentation can utilize labeled source domain data to assist unlabeled target domains in solving the above problems. In this paper, a cross-domain pancreas segmentation algorithm is proposed based on Moment-Consistent Contrastive Cycle Generative Adversarial Networks (MC-CCycleGAN). MC-CCycleGAN is a style transfer network, in which the encoder of its generator is used to extract features from real images and style transfer images, constrain feature extraction through a contrastive loss, and fully extract structural features of input images during style transfer while eliminate redundant style features. The multi-order central moments of the pancreas are proposed to describe its anatomy in high dimensions and a contrastive loss is also proposed to constrain the moment consistency, so as to maintain consistency of the pancreatic structure and shape before and after style transfer. Multi-teacher knowledge distillation framework is proposed to transfer the knowledge from multiple teachers to a single student, so as to improve the robustness and performance of the student network. The experimental results have demonstrated the superiority of our framework over state-of-the-art domain adaptation methods.

DRFNet: a deep radiomic fusion network for nAMD/PCV differentiation in OCT images.

Objective.Neovascular age-related macular degeneration (nAMD) and polypoidal choroidal vasculopathy (PCV) present many similar clinical features. However, there are significant differences in the progression of nAMD and PCV. and it is crucial to make accurate diagnosis for treatment. In this paper, we propose a structure-radiomic fusion network (DRFNet) to differentiate PCV and nAMD in optical coherence tomography (OCT) images.Approach.The subnetwork (RIMNet) is designed to automatically segment the lesion of nAMD and PCV. Another subnetwork (StrEncoder) is designed to extract deep structural features of the segmented lesion. The subnetwork (RadEncoder) is designed to extract radiomic features from the segmented lesions based on radiomics. 305 eyes (155 with nAMD and 150 with PCV) are included and manually annotated CNV region in this study. The proposed method was trained and evaluated by 4-fold cross validation using the collected data and was compared with the advanced differentiation methods.Main results.The proposed method achieved high classification performace of nAMD/PCV differentiation in OCT images, which was an improvement of 4.68 compared with other best method.Significance. The presented structure-radiomic fusion network (DRFNet) has great performance of diagnosing nAMD and PCV and high clinical value by using OCT instead of indocyanine green angiography.

Relationship between abnormal intrinsic functional connectivity of subcortices and autism symptoms in high-functioning adults with autism spectrum disorder.

PURPOSE: This study explores subcortices and their intrinsic functional connectivity (iFC) in autism spectrum disorder (ASD) adults and investigates their relationship with clinical severity. METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 74 ASD patients, and 63 gender and age-matched typically developing (TD) adults. Independent component analysis (ICA) was conducted to evaluate subcortical patterns of basal ganglia (BG) and thalamus. These two brain areas were treated as regions of interest to further calculate whole-brain FC. In addition, we employed multivariate machine learning to identify subcortices-based FC brain patterns and clinical scores to classify ASD adults from those TD subjects. RESULTS: In ASD individuals, autism diagnostic observation schedule (ADOS) was negatively correlated with the BG network. Similarly, social responsiveness scale (SRS) was negatively correlated with the thalamus network. The BG-based iFC analysis revealed adults with ASD versus TD had lower FC, and its FC with the right medial temporal lobe (MTL), was positively correlated with SRS and ADOS separately. ASD could be predicted with a balanced accuracy of around 60.0 % using brain patterns and 84.7 % using clinical variables. CONCLUSION: Our results revealed the abnormal subcortical iFC may be related to autism symptoms.