{"id":163213,"date":"2012-10-01T00:00:00","date_gmt":"2012-10-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/immediate-roi-search-for-3d-medical-images\/"},"modified":"2018-10-16T21:43:32","modified_gmt":"2018-10-17T04:43:32","slug":"immediate-roi-search-for-3d-medical-images","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/immediate-roi-search-for-3d-medical-images\/","title":{"rendered":"Immediate ROI Search for 3D Medical Images"},"content":{"rendered":"

The objective of this work is a scalable, real-time, visual search engine for 3-D medical images, where a user is able to select a query Region Of Interest (ROI) and automatically detect the corresponding regions within all returned images.<\/p>\n

We make three contributions: (i) we show that with appropriate o\ufb00-line processing, images can be retrieved and ROIs registered in real time; (ii) we propose and evaluate a number of scalable exemplar-based image registration schemes; (iii) we propose a discriminative method for learning to rank the returned images based on the content of the ROI. The retrieval system is demonstrated on MRI data from the ADNI dataset [9], and it is shown that the learnt ranking function outperforms the baseline.<\/p>\n","protected":false},"excerpt":{"rendered":"

The objective of this work is a scalable, real-time, visual search engine for 3-D medical images, where a user is able to select a query Region Of Interest (ROI) and automatically detect the corresponding regions within all returned images. We make three contributions: (i) we show that with appropriate o\ufb00-line processing, images can be retrieved […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-163213","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"MICCAI workshop on Medical Content-based Retrieval for Clinical Decision Support (MCBR-CDS)","msr_affiliation":"","msr_published_date":"2012-10-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"https:\/\/www.robots.ox.ac.uk\/~vgg\/publications\/2012\/Simonyan12a\/simonyan12a.pdf","msr_doi":"","msr_publication_uploader":[{"type":"url","title":"https:\/\/www.robots.ox.ac.uk\/~vgg\/publications\/2012\/Simonyan12a\/simonyan12a.pdf","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":0,"url":"https:\/\/www.robots.ox.ac.uk\/~vgg\/publications\/2012\/Simonyan12a\/simonyan12a.pdf"}],"msr-author-ordering":[{"type":"text","value":"K. Simonyan","user_id":0,"rest_url":false},{"type":"text","value":"M. Modat","user_id":0,"rest_url":false},{"type":"text","value":"S. Ourselin","user_id":0,"rest_url":false},{"type":"text","value":"A. Criminisi","user_id":0,"rest_url":false},{"type":"text","value":"A. Zisserman","user_id":0,"rest_url":false},{"type":"user_nicename","value":"antcrim","user_id":31055,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=antcrim"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[169659],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":169659,"post_title":"Project InnerEye - Democratizing Medical Imaging AI","post_name":"medical-image-analysis","post_type":"msr-project","post_date":"2008-10-07 05:22:18","post_modified":"2023-07-28 05:51:32","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/medical-image-analysis\/","post_excerpt":"InnerEye is a research project that uses state of the art\u00a0machine learning\u00a0technology to build innovative tools for the automatic, quantitative analysis of three-dimensional medical images.","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/169659"}]}}]},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/163213"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":3,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/163213\/revisions"}],"predecessor-version":[{"id":538477,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/163213\/revisions\/538477"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=163213"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=163213"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=163213"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=163213"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=163213"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=163213"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=163213"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=163213"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=163213"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=163213"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=163213"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=163213"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=163213"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=163213"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=163213"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=163213"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}