Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment

This paper describes a scalable active learning pipeline prototype for large-scale brain mapping that leverages high performance computing power. It enables high-throughput evaluation of algorithm results, which, after human review, are used for iterative machine learning model training. Image processing and machine learning are performed in a batch layer. Benchmark testing of image processing using pMATLAB shows that a 100$\times$ increase in throughput (10,000%) can be achieved while total processing time only increases by 9% on Xeon-G6 CPUs and by 22% on Xeon-E5 CPUs, indicating robust scalability. The images and algorithm results are provided through a serving layer to a browser-based user interface for interactive review. This pipeline has the potential to greatly reduce the manual annotation burden and improve the overall performance of machine learning-based brain mapping.Comment: 6 pages, 5 figures, submitted to IEEE HPEC 2020 proceeding

Developing a Series of AI Challenges for the United States Department of the Air Force

Through a series of federal initiatives and orders, the U.S. Government has been making a concerted effort to ensure American leadership in AI. These broad strategy documents have influenced organizations such as the United States Department of the Air Force (DAF). The DAF-MIT AI Accelerator is an initiative between the DAF and MIT to bridge the gap between AI researchers and DAF mission requirements. Several projects supported by the DAF-MIT AI Accelerator are developing public challenge problems that address numerous Federal AI research priorities. These challenges target priorities by making large, AI-ready datasets publicly available, incentivizing open-source solutions, and creating a demand signal for dual use technologies that can stimulate further research. In this article, we describe these public challenges being developed and how their application contributes to scientific advances

A Cell Cycle Role for the Epigenetic Factor CTCF-L/BORIS

CTCF is a ubiquitous epigenetic regulator that has been proposed as a master keeper of chromatin organisation. CTCF-like, or BORIS, is thought to antagonise CTCF and has been found in normal testis, ovary and a large variety of tumour cells. The cellular function of BORIS remains intriguing although it might be involved in developmental reprogramming of gene expression patterns. We here unravel the expression of CTCF and BORIS proteins throughout human epidermis. While CTCF is widely distributed within the nucleus, BORIS is confined to the nucleolus and other euchromatin domains. Nascent RNA experiments in primary keratinocytes revealed that endogenous BORIS is present in active transcription sites. Interestingly, BORIS also localises to interphase centrosomes suggesting a role in the cell cycle. Blocking the cell cycle at S phase or mitosis, or causing DNA damage, produced a striking accumulation of BORIS. Consistently, ectopic expression of wild type or GFP- BORIS provoked a higher rate of S phase cells as well as genomic instability by mitosis failure. Furthermore, downregulation of endogenous BORIS by specific shRNAs inhibited both RNA transcription and cell cycle progression. The results altogether suggest a role for BORIS in coordinating S phase events with mitosis

Full-Length L1CAM and Not Its Δ2Δ27 Splice Variant Promotes Metastasis through Induction of Gelatinase Expression

Tumour-specific splicing is known to contribute to cancer progression. In the case of the L1 cell adhesion molecule (L1CAM), which is expressed in many human tumours and often linked to bad prognosis, alternative splicing results in a full-length form (FL-L1CAM) and a splice variant lacking exons 2 and 27 (SV-L1CAM). It has not been elucidated so far whether SV-L1CAM, classically considered as tumour-associated, or whether FL-L1CAM is the metastasis-promoting isoform. Here, we show that both variants were expressed in human ovarian carcinoma and that exposure of tumour cells to pro-metastatic factors led to an exclusive increase of FL-L1CAM expression. Selective overexpression of one isoform in different tumour cells revealed that only FL-L1CAM promoted experimental lung and/or liver metastasis in mice. In addition, metastasis formation upon up-regulation of FL-L1CAM correlated with increased invasive potential and elevated Matrix metalloproteinase (MMP)-2 and -9 expression and activity in vitro as well as enhanced gelatinolytic activity in vivo. In conclusion, we identified FL-L1CAM as the metastasis-promoting isoform, thereby exemplifying that high expression of a so-called tumour-associated variant, here SV-L1CAM, is not per se equivalent to a decisive role of this isoform in tumour progression

Machine learning for medical ultrasound: status, methods, and future opportunities

Abstract Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization

Self-supervised feature extraction for 3D axon segmentation

© 2020 IEEE. Existing learning-based methods to automatically trace axons in 3D brain imagery often rely on manually annotated segmentation labels. Labeling is a labor-intensive process and is not scalable to whole-brain analysis, which is needed for improved understanding of brain function. We propose a self-supervised auxiliary task that utilizes the tube-like structure of axons to build a feature extractor from unlabeled data. The proposed auxiliary task constrains a 3D convolutional neural network (CNN) to predict the order of permuted slices in an input 3D volume. By solving this task, the 3D CNN is able to learn features without ground-truth labels that are useful for downstream segmentation with the 3D U-Net model. To the best of our knowledge, our model is the first to perform automated segmentation of axons imaged at subcellular resolution with the SHIELD technique. We demonstrate improved segmentation performance over the 3D U-Net model on both the SHIELD PVGPe dataset and the BigNeuron Project, single neuron Janelia dataset

Predicting Cognitive Load and Operational Performance in a Simulated Marksmanship Task

Modern operational environments can place significant demands on a service member's cognitive resources, increasing the risk of errors or mishaps due to overburden. The ability to monitor cognitive burden and associated performance within operational environments is critical to improving mission readiness. As a key step toward a field-ready system, we developed a simulated marksmanship scenario with an embedded working memory task in an immersive virtual reality environment. As participants performed the marksmanship task, they were instructed to remember numbered targets and recall the sequence of those targets at the end of the trial. Low and high cognitive load conditions were defined as the recall of three- and six-digit strings, respectively. Physiological and behavioral signals recorded included speech, heart rate, breathing rate, and body movement. These features were input into a random forest classifier that significantly discriminated between the low- and high-cognitive load conditions (AUC = 0.94). Behavioral features of gait were the most informative, followed by features of speech. We also showed the capability to predict performance on the digit recall (AUC = 0.71) and marksmanship (AUC = 0.58) tasks. The experimental framework can be leveraged in future studies to quantify the interaction of other types of stressors and their impact on operational cognitive and physical performance

Simple and Effective Ultrasound Needle Guidance System

Abstract-In this paper, we describe our prototype of an ultrasound guidance system to address the need for an easy-touse, cost-effective, and portable technology to improve ultrasound-guided procedures. The system consists of a lockable, articulating needle guide that attaches to an ultrasound probe and a user-interface that provides real-time visualization of the predicted needle trajectory overlaid on the ultrasound image. Our needle guide ensures proper needle alignment with the ultrasound imaging plane. Moreover, the calculated needle trajectory is superimposed on the real-time ultrasound image, eliminating the need for the practitioner to estimate the target trajectory, and thereby reducing injuries from needle readjustment. Finally, the guide is lockable to prevent needle deviation from the desired trajectory during insertion. This feature will also allow the practitioner to free one hand to complete simple tasks that usually require a second practitioner to perform. Overall, our system eliminates the experience required to develop the fine hand movement and dexterity needed for traditional ultrasound-guided procedures. The system has the potential to increase efficiency, safety, quality, and reduce costs for a wide range of ultrasound-guided procedures. Furthermore, in combination with portable ultrasound machines, this system will enable these procedures to be more easily performed by unskilled practitioners in non-ideal situations such as the battlefield and other disaster relief areas