CTCF is an architectural protein with a critical role in connecting higher-order chromatin folding in pluripotent stem cells. Recent reports have suggested that CTCF binding is more dynamic during development than previously appreciated. Here, we set out to understand the extent to which shifts in genome-wide CTCF occupancy contribute to the 3D reconfiguration of fine-scale chromatin folding during early neural lineage commitment. Unexpectedly, we observe a sharp decrease in CTCF occupancy during the transition from naï ve/primed pluripotency to multipotent primary neural progenitor cells (NPCs). Many pluripotency gene-enhancer interactions are anchored by CTCF, and its occupancy is lost in parallel with loop decommissioning during differentiation. Conversely, CTCF binding sites in NPCs are largely preexisting in pluripotent stem cells. Only a small number of CTCF sites arise de novo in NPCs. We identify another zinc finger protein, Yin Yang 1 (YY1), at the base of looping interactions between NPC-specific genes and enhancers. Putative NPC-specific enhancers exhibit strong YY1 signal when engaged in 3D contacts and negligible YY1 signal when not in loops. Moreover, siRNA knockdown of Yy1 specifically disrupts interactions between key NPC enhancers and their target genes. YY1-mediated interactions between NPC regulatory elements are often nested within constitutive loops anchored by CTCF. Together, our results support a model in which YY1 acts as an architectural protein to connect developmentally regulated looping interactions; the location of YY1-mediated interactions may be demarcated in development by a preexisting topological framework created by constitutive CTCF-mediated interactions.

BACKGROUND AND PURPOSE: Most brain lesions are characterized by hyperintense signal on FLAIR. We sought to develop an automated deep learning-based method for segmentation of abnormalities on FLAIR and volumetric quantification on clinical brain MRIs across many pathologic entities and scanning parameters. We evaluated the performance of the algorithm compared with manual segmentation and existing automated methods. MATERIALS AND METHODS: We adapted a U-Net convolutional neural network architecture for brain MRIs using 3D volumes. This network was retrospectively trained on 295 brain MRIs to perform automated FLAIR lesion segmentation. Performance was evaluated on 92 validation cases using Dice scores and voxelwise sensitivity and specificity, compared with radiologists' manual segmentations. The algorithm was also evaluated on measuring total lesion volume. RESULTS: Our model demonstrated accurate FLAIR lesion segmentation performance (median Dice score, 0.79) on the validation dataset across a large range of lesion characteristics. Across 19 neurologic diseases, performance was significantly higher than existing methods (Dice, 0.56 and 0.41) and approached human performance (Dice, 0.81). There was a strong correlation between the predictions of lesion volume of the algorithm compared with true lesion volume (ϭ 0.99). Lesion segmentations were accurate across a large range of image-acquisition parameters on Ͼ30 different MR imaging scanners. CONCLUSIONS: A 3D convolutional neural network adapted from a U-Net architecture can achieve high automated FLAIR segmentation performance on clinical brain MR imaging across a variety of underlying pathologies and image acquisition parameters. The method provides accurate volumetric lesion data that can be incorporated into assessments of disease burden or into radiologic reports. ABBREVIATIONS: BIANCA ϭ Brain Intensity Abnormality Classification Algorithm; CNN ϭ convolutional neural network; FDR ϭ false discovery rate; LST ϭ lesion segmentation tool; RMdSPE ϭ root median squared percentage error; RMSPE ϭ root mean squared percentage error; SVID ϭ small-vessel ischemic disease A pproximately 36 million MR imaging studies are performed annually in the United States, and this number is rising. 1 Approximately 65% of these MRIs are used to assess the central nervous system. The FLAIR sequence is universally used to identify and characterize imaging abnormalities in terms of location, size, and extent, due to its broad utility across many pathologies and lesion appearances. Specific applications of FLAIR include, among numerous others, primary and metastatic brain tumors; demyelinating, autoimmune, infectious, and inflammatory conditions; and ischemia. 2-4 Because of its general utility, FLAIR is acquired on nearly every clinical brain MRI. There is a growing need to develop fully automated, rapid, precise, quantitative assessments of FLAIR abnormalities to standardize quantitative descriptions of pathology. A quantitative lesion-burden assessment has the potential to reduce er...

In the era of personalized medicine, the emphasis of health care is shifting from populations to individuals. Artificial intelligence (AI) is capable of learning without explicit instruction and has emerging applications in medicine, particularly radiology. Whereas much attention has focused on teaching radiology trainees about AI, here our goal is to instead focus on how AI might be developed to better teach radiology trainees. While the idea of using AI to improve education is not new, the application of AI to medical and radiological education remains very limited. Based on the current educational foundation, we highlight an AI-integrated framework to augment radiology education and provide use case examples informed by our own institution’s practice. The coming age of “AI-augmented radiology” may enable not only “precision medicine” but also what we describe as “precision medical education,” where instruction is tailored to individual trainees based on their learning styles and needs.

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