{"id":164491,"date":"2013-01-01T00:00:00","date_gmt":"2013-01-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/anatomy-detection-and-localization-in-3d-medical-images\/"},"modified":"2018-10-16T21:50:57","modified_gmt":"2018-10-17T04:50:57","slug":"anatomy-detection-and-localization-in-3d-medical-images","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/anatomy-detection-and-localization-in-3d-medical-images\/","title":{"rendered":"Anatomy Detection and Localization in 3D Medical Images"},"content":{"rendered":"<div class=\"asset-content\">\n<p>This chapter discusses the use of regression forests for the automatic detection and simultaneous localization of multiple anatomical regions within Computed Tomography (CT) and Magnetic Resonance (MR) three-dimensional images. Important applications include: organ-specific tracking of radiation dose over time; selective retrieval of patient images from radiological database systems; semantic visual navigation; and the initialization of organ-specific image processing operations. We present a continuous parametrization of the anatomy localization problem, which allows it to be addressed effectively by multivariate random regression forests. A single pass of our probabilistic algorithm enables the direct mapping from voxels to organ location and size, with training focusing on maximizing the confidence of output predictions. As a by-product, our method produces salient anatomical landmarks, i.e. automatically selected \u201canchor\u201d regions which help localize organs of interest with high confidence. This chapter builds upon the work in [80, 277] and demonstrates the flexibility of forests in dealing with both CT or multi-channel MR images. Quantitative validation is performed on two groundtruth labelled databases: i) a database of 400 highly variable CT scans, and ii) a database of 33 full-body, multi-channel MR scans. In both cases localization errors are shown to be lower and more stable than those from more conventional atlas-based registration approaches. The simplicity of the regressor\u2019s context-rich visual features yield typical run-times of only 4 seconds per volume. This anatomy recognition algorithm is now part of the commercial product Microsoft Amalga Unified Intelligence System.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This chapter discusses the use of regression forests for the automatic detection and simultaneous localization of multiple anatomical regions within Computed Tomography (CT) and Magnetic Resonance (MR) three-dimensional images. Important applications include: organ-specific tracking of radiation dose over time; selective retrieval of patient images from radiological database systems; semantic visual navigation; and the initialization of [&hellip;]<\/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":"","msr-author-ordering":null,"msr_publishername":"Springer","msr_publisher_other":"","msr_booktitle":"Decision Forests for Computer Vision and Medical Image Analysis","msr_chapter":"","msr_edition":"Decision Forests for Computer Vision and Medical Image Analysis","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"A. Criminisi, O. Pauly, D. Robertson, B. Glocker, E. Konukoglu, D. Mateus, A. Martinez M\u00f6ller, S.G. Nekolla, N. Navab, J. 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