{"id":757420,"date":"2021-06-29T18:29:14","date_gmt":"2021-06-30T01:29:14","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=757420"},"modified":"2022-12-01T18:12:58","modified_gmt":"2022-12-02T02:12:58","slug":"automated-deep-segmentation-of-healthy-organs-in-psma-pet-ct-images","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/automated-deep-segmentation-of-healthy-organs-in-psma-pet-ct-images\/","title":{"rendered":"Automated Deep Segmentation of Healthy Organs in PSMA PET\/CT Images"},"content":{"rendered":"<p>As PET imaging of prostate-specific membrane antigen (PSMA) becomes more widely-adopted following FDA approval, the role of healthy organ segmentation with high PSMA expression is expected to increase. For example, significant correlations were found between pre-therapy PSMA-PET standardized uptake values (SUVs) in healthy organs and absorbed dose during therapy (Violet et al., 2019). On the other hand, segmenting regions of physiological uptake can be used to better estimate abnormal uptake, which has been shown to be correlated with outcome in patients receiving [<sup>177<\/sup>Lu]Lu-PSMA-617 radioligand therapy (Seifert et al., 2020). Manual segmentation of organs is very labor-intensive and often not feasible in large research trials. The objective of this work was to evaluate the ability of convolutional neural networks to perform fully-automated and robust segmentation and classification of organs with high tracer uptake in PSMA PET images.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>As PET imaging of prostate-specific membrane antigen (PSMA) becomes more widely-adopted following FDA approval, the role of healthy organ segmentation with high PSMA expression is expected to increase. For example, significant correlations were found between pre-therapy PSMA-PET standardized uptake values (SUVs) in healthy organs and absorbed dose during therapy (Violet et al., 2019). On the [&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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,13553],"msr-publication-type":[193715],"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-757420","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-medical-health-genomics","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2021-5-1","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"Journal of Nuclear Medicine","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":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/jnm.snmjournals.org\/content\/62\/supplement_1\/1410.abstract","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Ivan Klyuzhin","user_id":0,"rest_url":false},{"type":"text","value":"Guillaume Chausse","user_id":0,"rest_url":false},{"type":"text","value":"Ingrid Bloise","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Juan M. Lavista Ferres","user_id":39552,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Juan M. Lavista Ferres"},{"type":"text","value":"Carlos Uribe","user_id":0,"rest_url":false},{"type":"text","value":"Arman Rahmim","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[696544,780706],"msr_project":[778522],"publication":[],"video":[],"download":[],"msr_publication_type":"article","related_content":{"projects":[{"ID":778522,"post_title":"AI for Health","post_name":"ai-for-health","post_type":"msr-project","post_date":"2023-05-16 14:26:13","post_modified":"2024-10-14 15:42:21","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/ai-for-health\/","post_excerpt":"AI for Health is a philanthropic program launched by Microsoft, which aims to support nonprofits, researchers, and organizations working on global health challenges. 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