Learning Shape Priors by Pairwise Comparison for Robust Semantic Segmentation
Semantic segmentation is important in medical image analysis. Inspired by the strong ability of traditional image analysis techniques in capturing shape priors and inter-subject similarity, many deep learning (DL) models have been recently proposed to exploit such prior information and achieved robu...
Uloženo v:
| Vydáno v: | Proceedings (International Symposium on Biomedical Imaging) s. 38 - 41 |
|---|---|
| Hlavní autoři: | , , , , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
13.04.2021
|
| Témata: | |
| ISSN: | 1945-8452 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Semantic segmentation is important in medical image analysis. Inspired by the strong ability of traditional image analysis techniques in capturing shape priors and inter-subject similarity, many deep learning (DL) models have been recently proposed to exploit such prior information and achieved robust performance. However, these two types of important prior information are usually studied separately in existing models. In this paper, we propose a novel DL model to model both type of priors within a single framework. Specifically, we introduce an extra encoder into the classic encoder-decoder structure to form a Siamese structure for the encoders, where one of them takes a target image as input (the image-encoder), and the other concatenates a template image and its foreground regions as input (the template-encoder). The template-encoder encodes the shape priors and appearance characteristics of each foreground class in the template image. A cosine similarity based attention module is proposed to fuse the information from both encoders, to utilize both types of prior information encoded by the template-encoder and model the inter-subject similarity for each foreground class. Extensive experiments on two public datasets demonstrate that our proposed method can produce superior performance to competing methods. |
|---|---|
| AbstractList | Semantic segmentation is important in medical image analysis. Inspired by the strong ability of traditional image analysis techniques in capturing shape priors and inter-subject similarity, many deep learning (DL) models have been recently proposed to exploit such prior information and achieved robust performance. However, these two types of important prior information are usually studied separately in existing models. In this paper, we propose a novel DL model to model both type of priors within a single framework. Specifically, we introduce an extra encoder into the classic encoder-decoder structure to form a Siamese structure for the encoders, where one of them takes a target image as input (the image-encoder), and the other concatenates a template image and its foreground regions as input (the template-encoder). The template-encoder encodes the shape priors and appearance characteristics of each foreground class in the template image. A cosine similarity based attention module is proposed to fuse the information from both encoders, to utilize both types of prior information encoded by the template-encoder and model the inter-subject similarity for each foreground class. Extensive experiments on two public datasets demonstrate that our proposed method can produce superior performance to competing methods. |
| Author | Xie, Cong Cao, Shilei Wang, Liansheng Ma, Kai Wei, Dong Zheng, Yefeng Liu, Hualuo |
| Author_xml | – sequence: 1 givenname: Cong surname: Xie fullname: Xie, Cong organization: Xiamen University – sequence: 2 givenname: Hualuo surname: Liu fullname: Liu, Hualuo organization: Tencent Jarvis Lab – sequence: 3 givenname: Shilei surname: Cao fullname: Cao, Shilei organization: Tencent Jarvis Lab – sequence: 4 givenname: Dong surname: Wei fullname: Wei, Dong organization: Tencent Jarvis Lab – sequence: 5 givenname: Kai surname: Ma fullname: Ma, Kai organization: Tencent Jarvis Lab – sequence: 6 givenname: Liansheng surname: Wang fullname: Wang, Liansheng email: lswang@xmu.edu.cn organization: Xiamen University – sequence: 7 givenname: Yefeng surname: Zheng fullname: Zheng, Yefeng organization: Tencent Jarvis Lab |
| BookMark | eNotj81KAzEURqMo2NY-gSB5gan5uZNMllqsFioWq-tyZ-ZOjThJSUakb2_BfptzVge-MbsIMRBjt1LMpBTubrl5WEKlpJwpoeTMgdZOmzM2lsaUIBUYec5G0kFZVFCqKzbN-UscZwG0gBF7WRGm4MOObz5xT3ydfEyZ1we-Rp9-fSY-j_0ek88x8C4m_hbrnzzwDfUYBt8cZddTGHDwMVyzyw6_M01PnLCPxeP7_LlYvT4t5_erwiuhh6I1ZWsciMYAYEOgO6xMQ2UH1jqLJVQCVaOtqIWsK2ctkGsbI0nXqNrW6gm7-e96Itruk-8xHban9_oPbQpRHw |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ISBI48211.2021.9433936 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISBN | 1665412461 9781665412469 |
| EISSN | 1945-8452 |
| EndPage | 41 |
| ExternalDocumentID | 9433936 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: Fundamental Research Funds for the Central Universities funderid: 10.13039/501100012226 |
| GroupedDBID | 23N 6IE 6IF 6IK 6IL 6IN AAJGR AAWTH ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK IPLJI M43 OCL RIE RIL RNS |
| ID | FETCH-LOGICAL-i203t-d65d6940c644ace43fa86ce5f47797a5480a2c370b01b89774e9dc61e3ba2dd73 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000786144100009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Wed Aug 27 02:27:47 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i203t-d65d6940c644ace43fa86ce5f47797a5480a2c370b01b89774e9dc61e3ba2dd73 |
| PageCount | 4 |
| ParticipantIDs | ieee_primary_9433936 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-April-13 |
| PublicationDateYYYYMMDD | 2021-04-13 |
| PublicationDate_xml | – month: 04 year: 2021 text: 2021-April-13 day: 13 |
| PublicationDecade | 2020 |
| PublicationTitle | Proceedings (International Symposium on Biomedical Imaging) |
| PublicationTitleAbbrev | ISBI |
| PublicationYear | 2021 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0000744304 |
| Score | 2.1626618 |
| Snippet | Semantic segmentation is important in medical image analysis. Inspired by the strong ability of traditional image analysis techniques in capturing shape priors... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 38 |
| SubjectTerms | Analytical models Biological system modeling Deep learning Fuses Image analysis Image segmentation Inter-subject similarity Semantic segmentation Shape Shape priors |
| Title | Learning Shape Priors by Pairwise Comparison for Robust Semantic Segmentation |
| URI | https://ieeexplore.ieee.org/document/9433936 |
| WOSCitedRecordID | wos000786144100009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NS8MwFH9sw4Ne_NjEb3LwaLe2yZrk6nA40DGcwm4jX5092I62U_zvTbpSFbx4CyEh8EJ4v_defr8HcB3rofVbFHsh84lHuB97Atv3KBUTKqgEzyt1_Qc6nbLFgs9acNNwYYwx1ecz03fDqpavM7VxqbIBJxhzHLWhTSndcrWafIp1hcSG5jUJOPD5YDK_nRBmAxwbBYZBv978q4tK5UTG-_87_gB632w8NGv8zCG0THoEez-EBLvwWMukrtD8Vazd8iTLCyQ_0Uwk-UdSGDRqGg4ii1PRUyY3RYnm5s2aNlF2sHqraUhpD17Gd8-je69ulOAloY9LT0dDHXHiKwtuhDIEx4JFygxjQimnwkm6iVBh6pKekjnEZ7hWUWCwFKHWFB9DJ81ScwKIxBYvMSwCIyQxhDIlsFZ-ZGGMDCSPT6HrDLNcb7UwlrVNzv6ePoddZ3tXfQnwBXTKfGMuYUe9l0mRX1UX-AWHuJwJ |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PT8IwFG4QTdSLP8D42x48OljXbm2vEglEIEQw4UbarsMd2AgbGv9727FMTbx4a5o2aV7TvO-91-97ANxHoW_8FsWOx1ziEO5GjsDmPUrFhEKF4Hmhrj-goxGbzfi4Bh4qLozWuvh8plt2WNTyw1RtbKqszQnGHAc7YNcnxENbtlaVUTHOkJjgvKQBI5e3-5PHPmEmxDFxoIda5fZffVQKN9I9-t8BjkHzm48Hx5WnOQE1nZyCwx9Sgg0wLIVSF3DyJlZ2eZyuMyg_4VjE648407BTtRyEBqnCl1RushxO9NIYN1ZmsFiWRKSkCV67T9NOzylbJTix5-LcCQM_DDhxlYE3QmmCI8ECpf2IUMqpsKJuwlOY2rSnZBbzaR6qAGkshReGFJ-BepIm-hxAEhnExLBAWkiiCWVK4FC5gQEyEkkeXYCGNcx8tVXDmJc2ufx7-g7s96bDwXzQHz1fgQN7D7YWg_A1qOfrjb4Be-o9j7P1bXGZX9Sdn1A |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=proceeding&rft.title=Proceedings+%28International+Symposium+on+Biomedical+Imaging%29&rft.atitle=Learning+Shape+Priors+by+Pairwise+Comparison+for+Robust+Semantic+Segmentation&rft.au=Xie%2C+Cong&rft.au=Liu%2C+Hualuo&rft.au=Cao%2C+Shilei&rft.au=Wei%2C+Dong&rft.date=2021-04-13&rft.pub=IEEE&rft.eissn=1945-8452&rft.spage=38&rft.epage=41&rft_id=info:doi/10.1109%2FISBI48211.2021.9433936&rft.externalDocID=9433936 |