OBJECTIVE AND DESIGN: The design and implementation of ImageMiner, a software platform for performing comparative analysis of expression patterns in imaged microscopy specimens such as tissue microarrays (TMAs), is described. ImageMiner is a federated system of services that provides a reliable set of analytical and data management capabilities for investigative research applications in pathology. It provides a library of image processing methods, including automated registration, segmentation, feature extraction, and classification, all of which have been tailored, in these studies, to support TMA analysis. The system is designed to leverage high-performance computing machines so that investigators can rapidly analyze large ensembles of imaged TMA specimens. To support deployment in collaborative, multi-institutional projects, ImageMiner features grid-enabled, service-based components so that multiple instances of ImageMiner can be accessed remotely and federated. RESULTS: The experimental evaluation shows that: (1) ImageMiner is able to support reliable detection and feature extraction of tumor regions within imaged tissues; (2) images and analysis results managed in ImageMiner can be searched for and retrieved on the basis of image-based features, classification information, and any correlated clinical data, including any metadata that have been generated to describe the specified tissue and TMA; and (3) the system is able to reduce computation time of analyses by exploiting computing clusters, which facilitates analysis of larger sets of tissue samples.
OBJECTIVE AND DESIGN: The design and implementation of ImageMiner, a software platform for performing comparative analysis of expression patterns in imaged microscopy specimens such as tissue microarrays (TMAs), is described. ImageMiner is a federated system of services that provides a reliable set of analytical and data management capabilities for investigative research applications in pathology. It provides a library of image processing methods, including automated registration, segmentation, feature extraction, and classification, all of which have been tailored, in these studies, to support TMA analysis. The system is designed to leverage high-performance computing machines so that investigators can rapidly analyze large ensembles of imaged TMA specimens. To support deployment in collaborative, multi-institutional projects, ImageMiner features grid-enabled, service-based components so that multiple instances of ImageMiner can be accessed remotely and federated. RESULTS: The experimental evaluation shows that: (1) ImageMiner is able to support reliable detection and feature extraction of tumor regions within imaged tissues; (2) images and analysis results managed in ImageMiner can be searched for and retrieved on the basis of image-based features, classification information, and any correlated clinical data, including any metadata that have been generated to describe the specified tissue and TMA; and (3) the system is able to reduce computation time of analyses by exploiting computing clusters, which facilitates analysis of larger sets of tissue samples.
Authors: Wenjin Chen; Peter Meer; Bogdan Georgescu; Wei He; Lauri A Goodell; David J Foran Journal: Comput Methods Programs Biomed Date: 2005-07 Impact factor: 5.428
Authors: Hye Won Lee; Yu Rang Park; Jaehyun Sim; Rae Woong Park; Woo Ho Kim; Ju Han Kim Journal: Arch Pathol Lab Med Date: 2006-07 Impact factor: 5.534
Authors: H Moch; P Schraml; L Bubendorf; M Mirlacher; J Kononen; T Gasser; M J Mihatsch; O P Kallioniemi; G Sauter Journal: Am J Pathol Date: 1999-04 Impact factor: 4.307
Authors: Gustavo Ayala; Dagong Wang; Gerburg Wulf; Anna Frolov; Rile Li; Janusz Sowadski; Thomas M Wheeler; Kun Ping Lu; Lere Bao Journal: Cancer Res Date: 2003-10-01 Impact factor: 12.701
Authors: Jun Kong; Lee A D Cooper; Fusheng Wang; David A Gutman; Jingjing Gao; Candace Chisolm; Ashish Sharma; Tony Pan; Erwin G Van Meir; Tahsin M Kurc; Carlos S Moreno; Joel H Saltz; Daniel J Brat Journal: IEEE Trans Biomed Eng Date: 2011-09-23 Impact factor: 4.538
Authors: C Chennubhotla; L P Clarke; A Fedorov; D Foran; G Harris; E Helton; R Nordstrom; F Prior; D Rubin; J H Saltz; E Shalley; A Sharma Journal: Yearb Med Inform Date: 2017-09-11
Authors: Joel Saltz; Ashish Sharma; Ganesh Iyer; Erich Bremer; Feiqiao Wang; Alina Jasniewski; Tammy DiPrima; Jonas S Almeida; Yi Gao; Tianhao Zhao; Mary Saltz; Tahsin Kurc Journal: Cancer Res Date: 2017-11-01 Impact factor: 12.701