Computer Vision News - October 2016

Every month, Computer Vision News reviews a challenge related to our field. If you can’t find time to read challenges, but are interested in the new methods proposed by the scientific community to solve them, this section is for you. This month we have chosen to review the Second Annual Data Science Bowl , intended to catalyze a change in cardiac diagnostics: Transforming How We Diagnose Heart Disease . The website of the challenge, with all its related resources, is here ; the Kaggle page is here . Background The Second Annual Data Science Bowl , created and sponsored by Booz Allen Hamilton with Kaggle , was designed to take action to transform how we diagnose heart disease . Thousands of people are diagnosed every day with heart failure, a life- threatening event. Data science applied to cardiology can help physicians save more lives. Declining cardiac function is a key indicator of heart disease: assessing the heart's squeezing ability can therefore give clues about the heart conditions, enable an early diagnose and improve the effectiveness of heart disease treatment. Two are the properties which need to be measured: end-systolic and end- diastolic volumes (i.e., the size of one chamber of the heart at the beginning and middle of each heartbeat). From these two measures, it is possible to calculate the ejection fraction (EF), which is the percentage of blood ejected from the left ventricle at each heartbeat. To learn more about the medical considerations behind these measures, see the study by RSIP Vision on cardiac left ventricle segmentation . Challenge 26 Computer Vision News Challenge DSB - Transforming HowWe Diagnose Heart Disease