{"title":"SparkGIS:组织图像分析研究中算法结果的有效比较与评估","authors":"Furqan Baig, Mudit Mehrotra, Hoang Vo, Fusheng Wang, Joel Saltz, Tahsin Kurc","doi":"10.1007/978-3-319-41576-5_10","DOIUrl":null,"url":null,"abstract":"<p><p>Algorithm evaluation provides a means to characterize variability across image analysis algorithms, validate algorithms by comparison of multiple results, and facilitate algorithm sensitivity studies. The sizes of images and analysis results in pathology image analysis pose significant challenges in algorithm evaluation. We present SparkGIS, a distributed, in-memory spatial data processing framework to query, retrieve, and compare large volumes of analytical image result data for algorithm evaluation. Our approach combines the in-memory distributed processing capabilities of Apache Spark and the efficient spatial query processing of Hadoop-GIS. The experimental evaluation of SparkGIS for heatmap computations used to compare nucleus segmentation results from multiple images and analysis runs shows that SparkGIS is efficient and scalable, enabling algorithm evaluation and algorithm sensitivity studies on large datasets.</p>","PeriodicalId":92335,"journal":{"name":"Biomedical data management and graph online querying : VLDB 2015 Workshops, Big-O(Q) and DMAH, Waikoloa, HI, USA, August 31-September 4, 2015, revised selected papers. International Conference on Very Large Data Bases (41st : 2015 : Wai...","volume":"9579 ","pages":"134-146"},"PeriodicalIF":0.0000,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6126541/pdf/nihms980882.pdf","citationCount":"0","resultStr":"{\"title\":\"SparkGIS: Efficient Comparison and Evaluation of Algorithm Results in Tissue Image Analysis Studies.\",\"authors\":\"Furqan Baig, Mudit Mehrotra, Hoang Vo, Fusheng Wang, Joel Saltz, Tahsin Kurc\",\"doi\":\"10.1007/978-3-319-41576-5_10\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Algorithm evaluation provides a means to characterize variability across image analysis algorithms, validate algorithms by comparison of multiple results, and facilitate algorithm sensitivity studies. The sizes of images and analysis results in pathology image analysis pose significant challenges in algorithm evaluation. We present SparkGIS, a distributed, in-memory spatial data processing framework to query, retrieve, and compare large volumes of analytical image result data for algorithm evaluation. Our approach combines the in-memory distributed processing capabilities of Apache Spark and the efficient spatial query processing of Hadoop-GIS. The experimental evaluation of SparkGIS for heatmap computations used to compare nucleus segmentation results from multiple images and analysis runs shows that SparkGIS is efficient and scalable, enabling algorithm evaluation and algorithm sensitivity studies on large datasets.</p>\",\"PeriodicalId\":92335,\"journal\":{\"name\":\"Biomedical data management and graph online querying : VLDB 2015 Workshops, Big-O(Q) and DMAH, Waikoloa, HI, USA, August 31-September 4, 2015, revised selected papers. International Conference on Very Large Data Bases (41st : 2015 : Wai...\",\"volume\":\"9579 \",\"pages\":\"134-146\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6126541/pdf/nihms980882.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical data management and graph online querying : VLDB 2015 Workshops, Big-O(Q) and DMAH, Waikoloa, HI, USA, August 31-September 4, 2015, revised selected papers. International Conference on Very Large Data Bases (41st : 2015 : Wai...\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/978-3-319-41576-5_10\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2016/6/24 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical data management and graph online querying : VLDB 2015 Workshops, Big-O(Q) and DMAH, Waikoloa, HI, USA, August 31-September 4, 2015, revised selected papers. International Conference on Very Large Data Bases (41st : 2015 : Wai...","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/978-3-319-41576-5_10","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2016/6/24 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
SparkGIS: Efficient Comparison and Evaluation of Algorithm Results in Tissue Image Analysis Studies.
Algorithm evaluation provides a means to characterize variability across image analysis algorithms, validate algorithms by comparison of multiple results, and facilitate algorithm sensitivity studies. The sizes of images and analysis results in pathology image analysis pose significant challenges in algorithm evaluation. We present SparkGIS, a distributed, in-memory spatial data processing framework to query, retrieve, and compare large volumes of analytical image result data for algorithm evaluation. Our approach combines the in-memory distributed processing capabilities of Apache Spark and the efficient spatial query processing of Hadoop-GIS. The experimental evaluation of SparkGIS for heatmap computations used to compare nucleus segmentation results from multiple images and analysis runs shows that SparkGIS is efficient and scalable, enabling algorithm evaluation and algorithm sensitivity studies on large datasets.