Retinal image contains rich information on the blood vessel and is highly related to vascular diseases. Fully automatic and accurate identification of arteries and veins from the complex background of retinal images is essential for analyzing eye-relevant diseases, and monitoring progressive eye diseases. However, popular methods, including deep learning-based models, performed unsatisfactorily in preserving the connectivity of both the arteries and veins. The results were shown to be disconnected or overlapped by the twos and thus manual calibration was needed to refine the results. To tackle the problem, this paper proposes a topological structure-constrained generative adversarial network (topGAN) to automatically identify and differentiate the arteries and veins from retinal images. The introduced topological structure term can automatically delineate the topological structure properties of retinal blood vessels and greatly improves the vascular connectivity of the entire arteriovenous classification results. We train and evaluate our model on both the AV-DRIVE public available dataset and the CVDG home-owned dataset, which consists of 40 images and 3119 images, respectively. Experiments demonstrate that integrating topological structure constraints can significantly improve the performance of arteriovenous classification. Our method achieves excellent performance with an accuracy of 94.3% on the AV-DRIVE dataset and 93.6% on the CVDG dataset.

The development of automated tools for brain morphometric analysis in infants has lagged significantly behind analogous tools for adults. This gap reflects the greater challenges in this domain due to: 1) a smaller-scaled region of interest, 2) increased motion corruption, 3) regional changes in geometry due to heterochronous growth, and 4) regional variations in contrast properties corresponding to ongoing myelination and other maturation processes. Nevertheless, there is a great need for automated image-processing tools to quantify differences between infant groups and other individuals, because aberrant cortical morphologic measurements (including volume, thickness, surface area, and curvature) have been associated with neuropsychiatric, neurologic, and developmental disorders in children. In this paper we present an automated segmentation and surface extraction pipeline designed to accommodate clinical MRI studies of infant brains in a population 0-2 year-olds. The algorithm relies on a single channel of T1-weighted MR images to achieve automated segmentation of cortical and subcortical brain areas, producing volumes of subcortical structures and surface models of the cerebral cortex. We evaluated the algorithm both qualitatively and quantitatively using manually labeled datasets, relevant comparator software solutions cited in the literature, and expert evaluations. The computational tools and atlases described in this paper will be distributed to the research community as part of the FreeSurfer image analysis package.

Nasopharyngeal carcinoma (NPC) is the most common malignant tumor of the nasopharynx. The delicate nature of the nasopharyngeal structures means that noninvasive magnetic resonance imaging (MRI) is the preferred diagnostic technique for NPC. However, NPC is a typically infiltrative tumor, usually with a small volume, and thus, it remains challenging to discriminate it from tightly connected surrounding tissues. To address this issue, this study proposes a voxel-wise discriminate method for locating and segmenting NPC from normal tissues in MRI sequences. The located NPC is refined to obtain its accurate segmentation results by an original multiviewed collaborative dictionary classification (CODL) model. The proposed CODL reconstructs a latent intact space and equips it with discriminative power for the collective multiview analysis task. Experiments on synthetic data demonstrate that CODL is capable of finding a discriminative space for multiview orthogonal data. We then evaluated the method on real NPC. Experimental results show that CODL could accurately discriminate and localize NPCs of different volumes. This method achieved superior performances in segmenting NPC compared with benchmark methods. Robust segmentation results show that CODL can effectively assist clinicians in locating NPC.

Brain age prediction based on children's brain MRI is an important biomarker for brain health and brain development analysis. In this paper, we consider the 3D brain MRI volume as a sequence of 2D images and propose a new framework using the recurrent neural network for brain age estimation. The proposed method is named as 2D-ResNet18+Long short-term memory (LSTM), which consists of four parts: 2D ResNet18 for feature extraction on 2D images, a pooling layer for feature reduction over the sequences, an LSTM layer, and a final regression layer. We apply the proposed method on a public multisite NIH-PD dataset and evaluate generalization on a second multisite dataset, which shows that the proposed 2D-ResNet18+LSTM method provides better results than traditional 3D based neural network for brain age estimation.

Maternal nutrition is an important factor for infant neurodevelopment. However, prior magnetic resonance imaging (MRI) studies on maternal nutrients and infant brain have focused mostly on preterm infants or on few specific nutrients and few specific brain regions. We present a first study in term-born infants, comprehensively correlating 73 maternal nutrients with infant brain morphometry at the regional (61 regions) and voxel (over 300 000 voxel) levels. Both maternal nutrition intake diaries and infant MRI were collected at 1 month of life (0.9 ± 0.5 months) for 92 term-born infants (among them, 54 infants were purely breastfed and 19 were breastfed most of the time). Intake of nutrients was assessed via standardized food frequency questionnaire. No nutrient was significantly correlated with any of the volumes of the 61 autosegmented brain regions. However, increased volumes within subregions of the frontal cortex and corpus callosum at the voxel level were positively correlated with maternal intake of omega-3 fatty acids, retinol (vitamin A) and vitamin B12, both with and without correction for postmenstrual age and sex (P < 0.05, q < 0.05 after false discovery rate correction). Omega-3 fatty acids remained significantly correlated with infant brain volumes after subsetting to the 54 infants who were exclusively breastfed, but retinol and vitamin B12 did not. This provides an impetus for future larger studies to better characterize the effect size of dietary variation and correlation with neurodevelopmental outcomes, which can lead to improved nutritional guidance during pregnancy and lactation.

In brain tumor surgery, the quality and safety of the procedure can be impacted by intra-operative tissue deformation, called brain shift. Brain shift can move the surgical targets and other vital structures such as blood vessels, thus invalidating the pre-surgical plan. Intra-operative ultrasound (iUS) is a convenient and cost-effective imaging tool to track brain shift and tumor resection. Accurate image registration techniques that update pre-surgical MRI based on iUS are crucial but challenging. The MICCAI Challenge 2018 for Correction of Brain shift with Intra-Operative UltraSound (CuRIOUS2018) provided a public platform to benchmark MRI-iUS registration algorithms on newly released clinical datasets. In this work, we present the data, setup, evaluation, and results of CuRIOUS 2018, which received 6 fully automated algorithms from leading academic and industrial research groups. All algorithms were first trained with the public RESECT database, and then ranked based on a test dataset of 10 additional cases with identical data curation and annotation protocols as the RESECT database. The article compares the results of all participating teams and discusses the insights gained from the challenge, as well as future work.

Developmental dyslexia affects 40-60% of children with a familial risk (FHD+) compared to a general prevalence of 5-10%. Despite the increased risk, about half of FHD+ children develop typical reading abilities (FHD+Typical). Yet the underlying neural characteristics of favorable reading outcomes in at-risk children remain unknown. Utilizing a retrospective, longitudinal approach, this study examined whether putative protective neural mechanisms can be observed in FHD+Typical at the prereading stage. Functional and structural brain characteristics were examined in 47 FHD+ prereaders who subsequently developed typical (n = 35) or impaired (n = 12) reading abilities and 34 controls (FHD-Typical). Searchlight-based multivariate pattern analyses identified distinct activation patterns during phonological processing between FHD+Typical and FHD-Typical in right inferior frontal gyrus (RIFG) and left temporo-parietal cortex (LTPC) regions. Follow-up analyses on group-specific classification patterns demonstrated LTPC hypoactivation in FHD+Typical compared to FHD-Typical, suggesting this neural characteristic as an FHD+ phenotype. In contrast, RIFG showed hyperactivation in FHD+Typical than FHD-Typical, and its activation pattern was positively correlated with subsequent reading abilities in FHD+ but not controls (FHD-Typical). RIFG hyperactivation in FHD+Typical was further associated with increased interhemispheric functional and structural connectivity. These results suggest that some protective neural mechanisms are already established in FHD+Typical prereaders supporting their typical reading development.