{"id":10,"date":"2017-01-17T23:00:00","date_gmt":"2017-01-17T23:00:00","guid":{"rendered":"http:\/\/fast.hevs.ch:3001\/?p=10"},"modified":"2017-03-24T10:42:48","modified_gmt":"2017-03-24T10:42:48","slug":"lung-database-paper-selected-as-candidate-for-the-robert-f-wagner-best-student-paper-award","status":"publish","type":"post","link":"https:\/\/medgift.hevs.ch\/wordpress\/lung-database-paper-selected-as-candidate-for-the-robert-f-wagner-best-student-paper-award\/","title":{"rendered":"Lung database paper selected as candidate for the Robert F. Wagner Best Student paper Award"},"content":{"rendered":"

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The paper ‘Making Sense of Large Data Sets without Annotations:Analyzing Age-related Correlations from Lung CT Scans’ by\u00a0Yashin Dicente et al.<\/a>\u00a0is one of the finalists for the Robert F. Wagner Best Student paper Award at SPIE2017. The conference will take place from the 11-16 February 2017 in Orlando, Florida, US.<\/span><\/p>\n

Abstract:
<\/strong><\/span>\u00a0The analysis of large data sets can help to gain knowledge about specic organs or on specic diseases, just as\u00a0big data analysis does in many non-medical areas. This article aims to gain information from 3D volumes, so\u00a0the visual content of lung CT scans of a large number of patients. In the case of the described data set, only\u00a0little annotation is available on the patients that were all part of an ongoing screening program and besides age\u00a0and gender no information on the patient and the ndings was available for this work. This is a scenario that\u00a0can happen regularly as image data sets are produced and become available in increasingly large quantities but\u00a0manual annotations are often not available and also clinical data such as text reports are often harder to share.\u00a0We extracted a set of visual features from 12,414 CT scans of 9,348 patients that had CT scans of the lung taken\u00a0in the context of a national lung screening program in Belarus. Lung elds were segmented by two segmentation\u00a0algorithms and only cases where both algorithms were able to nd left and right lung and had a Dice coecient\u00a0above 0.95 were analyzed. This assures that only segmentations of good quality were used to extract features\u00a0of the lung. Patients ranged in age from 0 to 106 years. Data analysis shows that age can be predicted with a\u00a0fairly high accuracy for persons under 15 years. Relatively good results were also obtained between 30 and 65
years where a steady trend is seen. For young adults and older people the results are not as good as variability\u00a0is very high in these groups. Several visualizations of the data show the evolution patters of the lung texture,\u00a0size and density with age. The experiments allow learning the evolution of the lung and the gained results show\u00a0that even with limited meta{data we can extract interesting information from large{scale visual data. These\u00a0age-related changes (for example of the lung volume, the density histogram of the tissue) can also be taken into\u00a0account for the interpretation of new cases. The database used includes patients that had suspicions on a chest\u00a0X-ray, so it is not a group of healthy people, and only tendencies and not a model of a healthy lung at a specic\u00a0age can be derived.<\/p>\n

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The paper ‘Making Sense of Large Data Sets without Annotations:Analyzing Age-related Correlations from Lung CT Scans’ by\u00a0Yashin Dicente et al.\u00a0is one of the finalists for the Robert F. Wagner Best Student paper Award at SPIE2017. The conference will take place from the 11-16 February 2017 in Orlando, Florida, US. Abstract:\u00a0The analysis of large data sets … <\/p>\n