Science Bowl Research Articles

Fully automated segmentation of the left ventricle in cine cardiac MRI using neural network regression.

Left ventricle (LV) structure and functions are the primary assessment performed in most clinical cardiac MRI protocols. Fully automated LV segmentation might improve the efficiency and reproducibility of clinical assessment. To develop and validate a fully automated neural network regression-based algorithm for segmentation of the LV in cardiac MRI, with full coverage from apex to base across all cardiac phases, utilizing both short axis (SA) and long axis (LA) scans. Cross-sectional survey; diagnostic accuracy. In all, 200 subjects with coronary artery diseases and regional wall motion abnormalities from the public 2011 Left Ventricle Segmentation Challenge (LVSC) database; 1140 subjects with a mix of normal and abnormal cardiac functions from the public Kaggle Second Annual Data Science Bowl database. 1.5T, steady-state free precession. Reference standard data generated by experienced cardiac radiologists. Quantitative measurement and comparison via Jaccard and Dice index, modified Hausdorff distance (MHD), and blood volume. Paired t-tests compared to previous work. Tested against the LVSC database, we obtained 0.77 ± 0.11 (Jaccard index) and 1.33 ± 0.71 mm (MHD), both metrics demonstrating statistically significant improvement (P < 0.001) compared to previous work. Tested against the Kaggle database, the signed difference in evaluated blood volume was +7.2 ± 13.0 mL and -19.8 ± 18.8 mL for the end-systolic (ES) and end-diastolic (ED) phases, respectively, with a statistically significant improvement (P < 0.001) for the ED phase. A fully automated LV segmentation algorithm was developed and validated against a diverse set of cardiac cine MRI data sourced from multiple imaging centers and scanner types. The strong performance overall is suggestive of practical clinical utility. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Estimation of the Volume of the Left Ventricle From MRI Images Using Deep Neural Networks.

Segmenting human left ventricle (LV) in magnetic resonance imaging images and calculating its volume are important for diagnosing cardiac diseases. The latter task became the topic of the Second Annual Data Science Bowl organized by Kaggle. The dataset consisted of a large number of cases with only systole and diastole volume labels. We designed a system based on neural networks to solve this problem. It began with a detector to detect the regions of interest (ROI) containing LV chambers. Then a deep neural network named hypercolumns fully convolutional network was used to segment LV in ROI. The 2-D segmentation results were integrated across different images to estimate the volume. With ground-truth volume labels, this model was trained end-to-end. To improve the result, an additional dataset with only segmentation labels was used. The model was trained alternately on these two tasks. We also proposed a variance estimation method for the final prediction. Our algorithm ranked the fourth on the test set in this competition.

Automatic localization of the left ventricular blood pool centroid in short axis cardiac cine MR images.

In this paper, we develop and validate an open source, fully automatic algorithm to localize the left ventricular (LV) blood pool centroid in short axis cardiac cine MR images, enabling follow-on automated LV segmentation algorithms. The algorithm comprises four steps: (i) quantify motion to determine an initial region of interest surrounding the heart, (ii) identify potential 2D objects of interest using an intensity-based segmentation, (iii) assess contraction/expansion, circularity, and proximity to lung tissue to score all objects of interest in terms of their likelihood of constituting part of the LV, and (iv) aggregate the objects into connected groups and construct the final LV blood pool volume and centroid. This algorithm was tested against 1140 datasets from the Kaggle Second Annual Data Science Bowl, as well as 45 datasets from the STACOM 2009 Cardiac MR Left Ventricle Segmentation Challenge. Correct LV localization was confirmed in 97.3% of the datasets. The mean absolute error between the gold standard and localization centroids was 2.8 to 4.7mm, or 12 to 22% of the average endocardial radius. Graphical abstract Fully automated localization of the left ventricular blood pool in short axis cardiac cine MR images.

Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences.

Automated left ventricular (LV) segmentation is crucial for efficient quantification of cardiac function and morphology to aid subsequent management of cardiac pathologies. In this paper, we parameterize the complete (all short axis slices and phases) LV segmentation task in terms of the radial distances between the LV centerpoint and the endo- and epicardial contours in polar space. We then utilize convolutional neural network regression to infer these parameters. Utilizing parameter regression, as opposed to conventional pixel classification, allows the network to inherently reflect domain-specific physical constraints. We have benchmarked our approach primarily against the publicly-available left ventricle segmentation challenge (LVSC) dataset, which consists of 100 training and 100 validation cardiac MRI cases representing a heterogeneous mix of cardiac pathologies and imaging parameters across multiple centers. Our approach attained a .77 Jaccard index, which is the highest published overall result in comparison to other automated algorithms. To test general applicability, we also evaluated against the Kaggle Second Annual Data Science Bowl, where the evaluation metric was the indirect clinical measures of LV volume rather than direct myocardial contours. Our approach attained a Continuous Ranked Probability Score (CRPS) of .0124, which would have ranked tenth in the original challenge. With this we demonstrate the effectiveness of convolutional neural network regression paired with domain-specific features in clinical segmentation.

Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)

This paper demonstrates a computer-aided diagnosis (CAD) system for lung cancer classification of CT scans with unmarked nodules, a dataset from the Kaggle Data Science Bowl, 2017. Thresholding was used as an initial segmentation approach to segment out lung tissue from the rest of the CT scan. Thresholding produced the next best lung segmentation. The initial approach was to directly feed the segmented CT scans into 3D CNNs for classification, but this proved to be inadequate. Instead, a modified U-Net trained on LUNA16 data (CT scans with labeled nodules) was used to first detect nodule candidates in the Kaggle CT scans. The U-Net nodule detection produced many false positives, so regions of CTs with segmented lungs where the most likely nodule candidates were located as determined by the U-Net output were fed into 3D Convolutional Neural Networks (CNNs) to ultimately classify the CT scan as positive or negative for lung cancer. The 3D CNNs produced a test set Accuracy of 86.6%. The performance of our CAD system outperforms the current CAD systems in literature which have several training and testing phases that each requires a lot of labeled data, while our CAD system has only three major phases (segmentation, nodule candidate detection, and malignancy classification), allowing more efficient training and detection and more generalizability to other cancers.

Public Access to Federally Funded Scientific Research: Building Communities of Collaboration.

The author comments on a federal initiative implemented by the U.S. Department of Energy (DOE) Office of Scientific and Technical Information (OSTI) that allows public access to federally funded scientific research. Topics discussed include the impact of scholarly scientific literature on programming gaps in curriculum development and on students' critical thinking development, as well as her recollections of volunteering as a Civility Award Judge for the 2015 Tennessee Science Bowl.

The IEEE Oceanic Engineering Society at Forty: The Challenges of an Evolving Society

The IEEE oceanic engineering society (OES) has completed forty years of active engagement: the initial eight years in the form of the oceanography coordinating committee (OCC), followed by seven years as the council of oceanic engineering (COE), then the past twenty-five years in the form of an IEEE society. During those forty years, the OCC/COE/OES has existed to serve society, the profession, the oceanic engineering community, and the professional interests of IEEE members working in oceanic engineering. OES has sponsored a strong set of conferences, including OCEANS, the offshore technology conference (OTC), the Undersea Technology (UT) symposium, the United States/European Union (U.S/EU) Baltic International Symposium series, and a variety of specialty workshops. The OES publishes an influential group of science and technology publications, including its flagship IEEE JOURNAL OF OCEANIC ENGINEERING (JOE), a quarterly newsletter, and a "just-in-time" electronic newsletter. The OES has sought to encourage engineering students to enter the oceanic engineering field through a variety of student-focused activities, including the OCEANS conference student-posters program, human-powered submarine races, the Ocean Science Bowl competitions, and an academic scholarship. Chapters have been established in eight IEEE global regions of the world. These forums and activities have been established and maintained to allow OES members and others in the oceanic engineering community to learn and grow by interacting with each other and sharing their ideas and contributions for solution to technical problems and to the broader challenges of society. As the society has moved through its various eras described later, the OES has invented ways to serve its members and the community at-large and will continue to adapt, innovate, modify, and otherwise support the changing needs of the profession.

Ocean bowling

Coach Scott Carpenter, a biology teacher at Lexington High School in Massachusetts, says that “some [students] want to show that they can win on a football field, and some want to show that they know science better than anyone else.”His team of four sophomores and one senior proved their mettle when they won the 1998 National Ocean Science Bowl on April 27.

Science ExpOlympics: An Outreach Program of Competitive and Noncompetitive Events for High School Students

Austin Peay State University's biennial Science ExpOlympics involves competitive events, activities and demonstrations, and a science bowl competition for regional high school students. The purpose of the APSU Science ExpOlympics is threefold: to show all of the attending high school students that the sciences can be fun and interesting, to challenge those high school students who are already interested in the sciences, and to promote APSU as a potential university for graduating high school students. More than 2000 high school students have attended the Science ExpOlympics program since its inception in 1983. The Science ExpOlympics program has been jointly sponsored by the Biology, Chemistry, Geography and Geology, Industrial Technology, Mathematics and Computer Sciences, Physics, and Psychology departments.

Outstanding Student Affiliate Chapters Generate a Special Chemistry and Enhance Chemical Literacy

The chapter of the student affiliates of the American Chemical Society at the University of Tennessee at Martin has won three commendable and eighteen consecutive outstanding ratings, the most consecutive ratings of any student affiliate chapter, from the Society Committee on Education of the American Chemical Society. The activities of the chapter involve a great deal of pedagogic or “literal” chemistry, that is the members learn and communicate several aspects of chemistry from the chapter programs. In addition, a “special” chemistry or camaraderie results when members prepare for and participate in the various club projects and activities. This article briefly describes specific activities of UTM student affiliates that help to develop both the literal and the special chemistry among members. Finally, The High School Science Bowl, which is a most electrifying and rejuvenating activity, is described in greater detail for the information of other chemistry clubs.

Los Angeles High School Team Scores Victory In National Science Bowl

There is widespread concern that U.S. teenagers show little knowledge and less interest in science and math. Anyone thus troubled would have been cheered by an event on May 1 in Washington, D.C.—and further heartened by the enthusiastic reaction on the other side of the continent. Teams from Van Nuys High School in Los Angeles and Taylor Allderdice High School in Pittsburgh faced off for the championship of the fifth annual National Science Bowl. In a rapid-fire contest—answering questions on astronomy, biology, chemistry, computer science, earth science, math, and physics—Van Nuys raced to a 104-58 victory. Mira Loma High School from Sacramento, Calif., finished third, and Armand Hammer World College from Montezuma, N.M., was fourth. The teams' proficiency was humbling for scientists in the audience. The science bowl aims to encourage high school students from across the nation to excel in math and science and pursue careers in these fields, say the cosponsors—the Department of ...

Atlanta High School Students Edge Houston, Win National Science Bowl

Atlanta High School Students Edge Houston, Win National Science Bowl

Texas Students Repeat Science Bowl Win

If the sulfide ion is a base, it will... BZZZZ! The buzzer screamed, interrupting the question. It will extract a hydrogen ion from water, came the response. That correct answer, worth four points, near the end of the final round, enabled Lubbock High School to win the second annual National Science Bowl. The Lubbock, Tex., team defended the title it won last year, beating an equally strong team from La Cueva High School, Albuquerque, N.M., by 74 to 70. The competition at Department of Energy headquarters in Washington, D.C., late last month brought together 30 high school teams from 23 states and Puerto Rico. They qualified for the national finals by winning regional contests at DOE-funded facilities across the country, in a process that began in January. Participation was triple last year's (C&EN, May 13, 1991, page 35), with more than 12,000 students from 2000 schools taking part in regional bowls. The competition was organized ...

Texas Students Win First Science Bowl

RETURN TO ISSUEPREVNewsNEXTTexas Students Win First Science BowlRICHARD SELTZERCite this: Chem. Eng. News 1991, 69, 19, 35Publication Date (Print):May 13, 1991Publication History Published online10 November 2010Published inissue 13 May 1991https://doi.org/10.1021/cen-v069n019.p035Copyright © 1991 AMERICAN CHEMICAL SOCIETYArticle Views8Altmetric-Citations1LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (569 KB) SUBJECTS:Students Get e-Alerts