Lymphoma PET-CT fusion segmentation method based on small target position probability
The invention discloses a lymphoma PET-CT fusion segmentation method based on small target position probability, and the method comprises the steps: preheating an organ segmentation network of a CT image, and freezing the network after preheating; generating a random learnable parameter as a general...
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Patent |
| Sprache: | chi ; eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | ZHOU QIANWEI REN NANYIN SU YIPING GUAN QIU HU HAIGEN |
| description | The invention discloses a lymphoma PET-CT fusion segmentation method based on small target position probability, and the method comprises the steps: preheating an organ segmentation network of a CT image, and freezing the network after preheating; generating a random learnable parameter as a general lymphoma position prototype, combining the position prototype with the predicted organ mask of the frozen part, and generating a position prototype emphasizing small target lymphoma and a position prototype constraining an interference target; a new nn-UNet convolutional network is initialized to segment the whole body lymphoma, and the small target lymphoma position prototype and the interference target position prototype assist the 3D PET input image to segment the whole body lymphoma; and the nn-UNet convolutional neural network is used for carrying out segmentation by using a combined loss formed by Dice loss and Cross-Entropy Loss. According to the invention, the performance of automatic segmentation of syste |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN117522899A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN117522899A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN117522899A3</originalsourceid><addsrcrecordid>eNrjZAj1qcwtyMjPTVQIcA3RdQ5RSCstzszPUyhOTc9NzStJLAFxclNLMvJTFJISi1NTFECSuYk5OQoliUXpqSUKBfnFmWBVBUX5SYlJmTmZJZU8DKxpiTnFqbxQmptB0c01xNlDN7UgPz61uCAxOTUvtSTe2c_Q0NzUyMjC0tLRmBg1ALrDOT0</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Lymphoma PET-CT fusion segmentation method based on small target position probability</title><source>esp@cenet</source><creator>ZHOU QIANWEI ; REN NANYIN ; SU YIPING ; GUAN QIU ; HU HAIGEN</creator><creatorcontrib>ZHOU QIANWEI ; REN NANYIN ; SU YIPING ; GUAN QIU ; HU HAIGEN</creatorcontrib><description>The invention discloses a lymphoma PET-CT fusion segmentation method based on small target position probability, and the method comprises the steps: preheating an organ segmentation network of a CT image, and freezing the network after preheating; generating a random learnable parameter as a general lymphoma position prototype, combining the position prototype with the predicted organ mask of the frozen part, and generating a position prototype emphasizing small target lymphoma and a position prototype constraining an interference target; a new nn-UNet convolutional network is initialized to segment the whole body lymphoma, and the small target lymphoma position prototype and the interference target position prototype assist the 3D PET input image to segment the whole body lymphoma; and the nn-UNet convolutional neural network is used for carrying out segmentation by using a combined loss formed by Dice loss and Cross-Entropy Loss. According to the invention, the performance of automatic segmentation of syste</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ; PHYSICS</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240206&DB=EPODOC&CC=CN&NR=117522899A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25555,76308</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240206&DB=EPODOC&CC=CN&NR=117522899A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>ZHOU QIANWEI</creatorcontrib><creatorcontrib>REN NANYIN</creatorcontrib><creatorcontrib>SU YIPING</creatorcontrib><creatorcontrib>GUAN QIU</creatorcontrib><creatorcontrib>HU HAIGEN</creatorcontrib><title>Lymphoma PET-CT fusion segmentation method based on small target position probability</title><description>The invention discloses a lymphoma PET-CT fusion segmentation method based on small target position probability, and the method comprises the steps: preheating an organ segmentation network of a CT image, and freezing the network after preheating; generating a random learnable parameter as a general lymphoma position prototype, combining the position prototype with the predicted organ mask of the frozen part, and generating a position prototype emphasizing small target lymphoma and a position prototype constraining an interference target; a new nn-UNet convolutional network is initialized to segment the whole body lymphoma, and the small target lymphoma position prototype and the interference target position prototype assist the 3D PET input image to segment the whole body lymphoma; and the nn-UNet convolutional neural network is used for carrying out segmentation by using a combined loss formed by Dice loss and Cross-Entropy Loss. According to the invention, the performance of automatic segmentation of syste</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZAj1qcwtyMjPTVQIcA3RdQ5RSCstzszPUyhOTc9NzStJLAFxclNLMvJTFJISi1NTFECSuYk5OQoliUXpqSUKBfnFmWBVBUX5SYlJmTmZJZU8DKxpiTnFqbxQmptB0c01xNlDN7UgPz61uCAxOTUvtSTe2c_Q0NzUyMjC0tLRmBg1ALrDOT0</recordid><startdate>20240206</startdate><enddate>20240206</enddate><creator>ZHOU QIANWEI</creator><creator>REN NANYIN</creator><creator>SU YIPING</creator><creator>GUAN QIU</creator><creator>HU HAIGEN</creator><scope>EVB</scope></search><sort><creationdate>20240206</creationdate><title>Lymphoma PET-CT fusion segmentation method based on small target position probability</title><author>ZHOU QIANWEI ; REN NANYIN ; SU YIPING ; GUAN QIU ; HU HAIGEN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN117522899A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>ZHOU QIANWEI</creatorcontrib><creatorcontrib>REN NANYIN</creatorcontrib><creatorcontrib>SU YIPING</creatorcontrib><creatorcontrib>GUAN QIU</creatorcontrib><creatorcontrib>HU HAIGEN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>ZHOU QIANWEI</au><au>REN NANYIN</au><au>SU YIPING</au><au>GUAN QIU</au><au>HU HAIGEN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Lymphoma PET-CT fusion segmentation method based on small target position probability</title><date>2024-02-06</date><risdate>2024</risdate><abstract>The invention discloses a lymphoma PET-CT fusion segmentation method based on small target position probability, and the method comprises the steps: preheating an organ segmentation network of a CT image, and freezing the network after preheating; generating a random learnable parameter as a general lymphoma position prototype, combining the position prototype with the predicted organ mask of the frozen part, and generating a position prototype emphasizing small target lymphoma and a position prototype constraining an interference target; a new nn-UNet convolutional network is initialized to segment the whole body lymphoma, and the small target lymphoma position prototype and the interference target position prototype assist the 3D PET input image to segment the whole body lymphoma; and the nn-UNet convolutional neural network is used for carrying out segmentation by using a combined loss formed by Dice loss and Cross-Entropy Loss. According to the invention, the performance of automatic segmentation of syste</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | chi ; eng |
| recordid | cdi_epo_espacenet_CN117522899A |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING IMAGE DATA PROCESSING OR GENERATION, IN GENERAL PHYSICS |
| title | Lymphoma PET-CT fusion segmentation method based on small target position probability |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-15T03%3A51%3A14IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=ZHOU%20QIANWEI&rft.date=2024-02-06&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN117522899A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |