CTS: A Consistency-Based Medical Image Segmentation Model
In medical image segmentation tasks, diffusion models have shown significant potential. However, mainstream diffusion models suffer from drawbacks such as multiple sampling times and slow prediction results. Recently, consistency models, as a standalone generative network, have resolved this issue....
Gespeichert in:
Veröffentlicht in: | arXiv.org 2024-05 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | arXiv.org |
container_volume | |
creator | Zhang, Kejia Zhang, Lan Pan, Haiwei Yu, Baolong |
description | In medical image segmentation tasks, diffusion models have shown significant potential. However, mainstream diffusion models suffer from drawbacks such as multiple sampling times and slow prediction results. Recently, consistency models, as a standalone generative network, have resolved this issue. Compared to diffusion models, consistency models can reduce the sampling times to once, not only achieving similar generative effects but also significantly speeding up training and prediction. However, they are not suitable for image segmentation tasks, and their application in the medical imaging field has not yet been explored. Therefore, this paper applies the consistency model to medical image segmentation tasks, designing multi-scale feature signal supervision modes and loss function guidance to achieve model convergence. Experiments have verified that the CTS model can obtain better medical image segmentation results with a single sampling during the test phase. |
format | Article |
fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_3055640863</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3055640863</sourcerecordid><originalsourceid>FETCH-proquest_journals_30556408633</originalsourceid><addsrcrecordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mSwdA4JtlJwVHDOzyvOLC5JzUuu1HVKLE5NUfBNTclMTsxR8MxNTE9VCE5Nz03NK0ksyczPU_DNT0nN4WFgTUvMKU7lhdLcDMpuriHOHroFRfmFpanFJfFZ-aVFeUCpeGMDU1MzEwMLM2Nj4lQBAN9tNUI</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3055640863</pqid></control><display><type>article</type><title>CTS: A Consistency-Based Medical Image Segmentation Model</title><source>Free E- Journals</source><creator>Zhang, Kejia ; Zhang, Lan ; Pan, Haiwei ; Yu, Baolong</creator><creatorcontrib>Zhang, Kejia ; Zhang, Lan ; Pan, Haiwei ; Yu, Baolong</creatorcontrib><description>In medical image segmentation tasks, diffusion models have shown significant potential. However, mainstream diffusion models suffer from drawbacks such as multiple sampling times and slow prediction results. Recently, consistency models, as a standalone generative network, have resolved this issue. Compared to diffusion models, consistency models can reduce the sampling times to once, not only achieving similar generative effects but also significantly speeding up training and prediction. However, they are not suitable for image segmentation tasks, and their application in the medical imaging field has not yet been explored. Therefore, this paper applies the consistency model to medical image segmentation tasks, designing multi-scale feature signal supervision modes and loss function guidance to achieve model convergence. Experiments have verified that the CTS model can obtain better medical image segmentation results with a single sampling during the test phase.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Consistency ; Diffusion rate ; Image segmentation ; Medical imaging ; Sampling</subject><ispartof>arXiv.org, 2024-05</ispartof><rights>2024. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>781,785</link.rule.ids></links><search><creatorcontrib>Zhang, Kejia</creatorcontrib><creatorcontrib>Zhang, Lan</creatorcontrib><creatorcontrib>Pan, Haiwei</creatorcontrib><creatorcontrib>Yu, Baolong</creatorcontrib><title>CTS: A Consistency-Based Medical Image Segmentation Model</title><title>arXiv.org</title><description>In medical image segmentation tasks, diffusion models have shown significant potential. However, mainstream diffusion models suffer from drawbacks such as multiple sampling times and slow prediction results. Recently, consistency models, as a standalone generative network, have resolved this issue. Compared to diffusion models, consistency models can reduce the sampling times to once, not only achieving similar generative effects but also significantly speeding up training and prediction. However, they are not suitable for image segmentation tasks, and their application in the medical imaging field has not yet been explored. Therefore, this paper applies the consistency model to medical image segmentation tasks, designing multi-scale feature signal supervision modes and loss function guidance to achieve model convergence. Experiments have verified that the CTS model can obtain better medical image segmentation results with a single sampling during the test phase.</description><subject>Consistency</subject><subject>Diffusion rate</subject><subject>Image segmentation</subject><subject>Medical imaging</subject><subject>Sampling</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mSwdA4JtlJwVHDOzyvOLC5JzUuu1HVKLE5NUfBNTclMTsxR8MxNTE9VCE5Nz03NK0ksyczPU_DNT0nN4WFgTUvMKU7lhdLcDMpuriHOHroFRfmFpanFJfFZ-aVFeUCpeGMDU1MzEwMLM2Nj4lQBAN9tNUI</recordid><startdate>20240515</startdate><enddate>20240515</enddate><creator>Zhang, Kejia</creator><creator>Zhang, Lan</creator><creator>Pan, Haiwei</creator><creator>Yu, Baolong</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20240515</creationdate><title>CTS: A Consistency-Based Medical Image Segmentation Model</title><author>Zhang, Kejia ; Zhang, Lan ; Pan, Haiwei ; Yu, Baolong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_30556408633</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Consistency</topic><topic>Diffusion rate</topic><topic>Image segmentation</topic><topic>Medical imaging</topic><topic>Sampling</topic><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Kejia</creatorcontrib><creatorcontrib>Zhang, Lan</creatorcontrib><creatorcontrib>Pan, Haiwei</creatorcontrib><creatorcontrib>Yu, Baolong</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Kejia</au><au>Zhang, Lan</au><au>Pan, Haiwei</au><au>Yu, Baolong</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>CTS: A Consistency-Based Medical Image Segmentation Model</atitle><jtitle>arXiv.org</jtitle><date>2024-05-15</date><risdate>2024</risdate><eissn>2331-8422</eissn><abstract>In medical image segmentation tasks, diffusion models have shown significant potential. However, mainstream diffusion models suffer from drawbacks such as multiple sampling times and slow prediction results. Recently, consistency models, as a standalone generative network, have resolved this issue. Compared to diffusion models, consistency models can reduce the sampling times to once, not only achieving similar generative effects but also significantly speeding up training and prediction. However, they are not suitable for image segmentation tasks, and their application in the medical imaging field has not yet been explored. Therefore, this paper applies the consistency model to medical image segmentation tasks, designing multi-scale feature signal supervision modes and loss function guidance to achieve model convergence. Experiments have verified that the CTS model can obtain better medical image segmentation results with a single sampling during the test phase.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | EISSN: 2331-8422 |
ispartof | arXiv.org, 2024-05 |
issn | 2331-8422 |
language | eng |
recordid | cdi_proquest_journals_3055640863 |
source | Free E- Journals |
subjects | Consistency Diffusion rate Image segmentation Medical imaging Sampling |
title | CTS: A Consistency-Based Medical Image Segmentation Model |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-18T11%3A59%3A43IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=CTS:%20A%20Consistency-Based%20Medical%20Image%20Segmentation%20Model&rft.jtitle=arXiv.org&rft.au=Zhang,%20Kejia&rft.date=2024-05-15&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E3055640863%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3055640863&rft_id=info:pmid/&rfr_iscdi=true |