XLSTM-HVED: Cross-Modal Brain Tumor Segmentation and MRI Reconstruction Method Using Vision Xlstm and Heteromodal Variational Encoder-Decoder

Neurogliomas are among the most aggressive forms of can-cer, presenting considerable challenges in both treatment and monitoring due to their unpredictable biological behav-ior. Magnetic resonance imaging (MRI) is currently the preferred method for diagnosing and monitoring gliomas. However, the lac...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Proceedings (International Symposium on Biomedical Imaging) s. 1 - 5
Hlavní autori: Zhu, Shenghao, Chen, Yifei, Jiang, Shuo, Chen, Weihong, Liu, Chang, Wang, Yuanhan, Chen, Xu, Ke, Yifan, Qin, Feiwei, Wang, Changmiao, Zhu, Zhu
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 14.04.2025
Predmet:
Accuracy
Biological system modeling
Brain modeling
Brain Tumor Segmentation
Brain tumors
Image reconstruction
Image segmentation
Magnetic resonance imaging
Missing Modality
Monitoring
Multi-task Learning
Multimodal MRI
Multitasking
Neuroglioma
Technological innovation
ISSN:1945-8452
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Abstract Neurogliomas are among the most aggressive forms of can-cer, presenting considerable challenges in both treatment and monitoring due to their unpredictable biological behav-ior. Magnetic resonance imaging (MRI) is currently the preferred method for diagnosing and monitoring gliomas. However, the lack of specific imaging techniques often com-promises the accuracy of tumor segmentation during the imaging process. To address this issue, we introduce the XLSTM-HVED model. This model integrates a hetero-modal encoder-decoder framework with the Vision XLSTM module to reconstruct missing MRI modalities. By deeply fusing spa-tial and temporal features, it enhances tumor segmentation performance. The key innovation of our approach is the Self-Attention Variational Encoder (SAVE) module, which im-proves the integration of modal features. Additionally, it op-timizes the interaction of features between segmentation and reconstruction tasks through the Squeeze-Fusion-Excitation Cross Awareness (SFECA) module. Our experiments using the BraTS 2024 dataset demonstrate that our model signif-icantly outperforms existing advanced methods in handling cases where modalities are missing. Our source code is available at https://github.com/Quanat0607/XLSTM-HVED.
AbstractList Neurogliomas are among the most aggressive forms of can-cer, presenting considerable challenges in both treatment and monitoring due to their unpredictable biological behav-ior. Magnetic resonance imaging (MRI) is currently the preferred method for diagnosing and monitoring gliomas. However, the lack of specific imaging techniques often com-promises the accuracy of tumor segmentation during the imaging process. To address this issue, we introduce the XLSTM-HVED model. This model integrates a hetero-modal encoder-decoder framework with the Vision XLSTM module to reconstruct missing MRI modalities. By deeply fusing spa-tial and temporal features, it enhances tumor segmentation performance. The key innovation of our approach is the Self-Attention Variational Encoder (SAVE) module, which im-proves the integration of modal features. Additionally, it op-timizes the interaction of features between segmentation and reconstruction tasks through the Squeeze-Fusion-Excitation Cross Awareness (SFECA) module. Our experiments using the BraTS 2024 dataset demonstrate that our model signif-icantly outperforms existing advanced methods in handling cases where modalities are missing. Our source code is available at https://github.com/Quanat0607/XLSTM-HVED.
Author Ke, Yifan
Jiang, Shuo
Zhu, Shenghao
Zhu, Zhu
Wang, Yuanhan
Qin, Feiwei
Wang, Changmiao
Chen, Xu
Chen, Yifei
Chen, Weihong
Liu, Chang
Author_xml – sequence: 1
  givenname: Shenghao
  surname: Zhu
  fullname: Zhu, Shenghao
  organization: Hangzhou Dianzi University,Hangzhou,China
– sequence: 2
  givenname: Yifei
  surname: Chen
  fullname: Chen, Yifei
  organization: Hangzhou Dianzi University,Hangzhou,China
– sequence: 3
  givenname: Shuo
  surname: Jiang
  fullname: Jiang, Shuo
  organization: Hangzhou Dianzi University,Hangzhou,China
– sequence: 4
  givenname: Weihong
  surname: Chen
  fullname: Chen, Weihong
  organization: Hangzhou Dianzi University,Hangzhou,China
– sequence: 5
  givenname: Chang
  surname: Liu
  fullname: Liu, Chang
  organization: Hangzhou Dianzi University,Hangzhou,China
– sequence: 6
  givenname: Yuanhan
  surname: Wang
  fullname: Wang, Yuanhan
  organization: Hangzhou Dianzi University,Hangzhou,China
– sequence: 7
  givenname: Xu
  surname: Chen
  fullname: Chen, Xu
  organization: Hangzhou Dianzi University,Hangzhou,China
– sequence: 8
  givenname: Yifan
  surname: Ke
  fullname: Ke, Yifan
  email: qinfeiwei@hdu.edu.cn
  organization: Hangzhou Dianzi University,Hangzhou,China
– sequence: 9
  givenname: Feiwei
  surname: Qin
  fullname: Qin, Feiwei
  organization: Hangzhou Dianzi University,Hangzhou,China
– sequence: 10
  givenname: Changmiao
  surname: Wang
  fullname: Wang, Changmiao
  organization: Shenzhen Research Institute of Big Data,Shenzhen,China
– sequence: 11
  givenname: Zhu
  surname: Zhu
  fullname: Zhu, Zhu
  email: zhuzhu_cs@zju.edu.cn
  organization: Zhejiang University, School of Medicine,Children's Hospital,Hangzhou,China
BookMark eNo1kMtOwzAQRQ0CiVL6B0j4B1LsOI5tdvQBjdQIqS91VznJtBg1NrLTRT-CfyYEmM0dHekeaeYWXVlnAaEHSoaUEvWYLUdZSrikw5jEfNgiSWPBL9BACSUZozwmPE4vUY-qhEcy4fENGoTwQdoRScJI0kNf2_lylUezzXTyhMfehRDlrtJHPPLaWLw61c7jJRxqsI1ujLNY2wrniwwvoHQ2NP5UdjiH5t1VeB2MPeCNCT9sewxN3RVm0IB3dWfeaG86VbtPbekq8NEEurxD13t9DDD4yz5av0xX41k0f3vNxs_zyFAhmwgkiYkqSlmkxb4UKYP2bkkStZepACU4SGAi4YXSgtIiLStN2g4jkDJWScX66P7XawBg9-lNrf159_9B9g0TNGfq
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/ISBI60581.2025.10981275
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Xplore / Electronic Library Online (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Xplore / Electronic Library Online (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISBN 9798331520526
EISSN 1945-8452
EndPage 5
ExternalDocumentID 10981275
Genre orig-research
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-i178t-e80209bc8b6bfc763e2758049f867e975e8e3745b9a711b6cda0e8030e633d893
IEDL.DBID RIE
ISICitedReferencesCount 1
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001546451000601&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 01:53:17 EDT 2025
IsPeerReviewed false
IsScholarly true
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i178t-e80209bc8b6bfc763e2758049f867e975e8e3745b9a711b6cda0e8030e633d893
PageCount 5
ParticipantIDs ieee_primary_10981275
PublicationCentury 2000
PublicationDate 2025-April-14
PublicationDateYYYYMMDD 2025-04-14
PublicationDate_xml – month: 04
  year: 2025
  text: 2025-April-14
  day: 14
PublicationDecade 2020
PublicationTitle Proceedings (International Symposium on Biomedical Imaging)
PublicationTitleAbbrev ISBI
PublicationYear 2025
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0000744304
Score 2.2952774
Snippet Neurogliomas are among the most aggressive forms of can-cer, presenting considerable challenges in both treatment and monitoring due to their unpredictable...
SourceID ieee
SourceType Publisher
StartPage 1
SubjectTerms Accuracy
Biological system modeling
Brain modeling
Brain Tumor Segmentation
Brain tumors
Image reconstruction
Image segmentation
Magnetic resonance imaging
Missing Modality
Monitoring
Multi-task Learning
Multimodal MRI
Multitasking
Neuroglioma
Technological innovation
Title XLSTM-HVED: Cross-Modal Brain Tumor Segmentation and MRI Reconstruction Method Using Vision Xlstm and Heteromodal Variational Encoder-Decoder
URI https://ieeexplore.ieee.org/document/10981275
WOSCitedRecordID wos001546451000601&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/eLvHCXMwlV07b8IwELZa1KFd-qLqWx66mhLi2E5HXgIJECo0yoZs51IhQVLx6L_of65tArRDh06xolwUX5ycv7v77hB6Aqq51FITFgAlVHqCCKUYkcKXKtRVlrhKTFGPDwYijsNhQVZ3XBgAcMlnULFDF8tPcr22rjLzhYfCFiQ_RIecsw1Za-dQMbaQGmxe5HCZS5-7o3rXRv0sDKwFla30rz4qzoy0T__5AGeovCfk4eHO1JyjA8gu0MmPWoKX6CvujcZ90olazRfcsLaP9PNEznDdNoHA4_U8X-ARvM8LslGGZZbg_msXWwS6ryOL-66nNHa5BDhy1HMcz5aruRPo2PSZfO7uHBmcXfgScSuz5PgFaYI7ltFbuzVudEjRa4FMPS5WBITZN4ZKC8VUqs1PB8w0hYEPqWAcQh6AAJ_TQIWSe55iOpFVI-NXgfl-YjY9V6iU5RlcI-wzFVJRS2s69WhKhayxlCoqlKd9MHjyBpWtZicfm3Iak61Sb_84f4eO7fuzIRyP3qOS0Qc8oCP9uZouF49uEXwDkkqzjA
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8MwDI5gIAEXXkO8yYFrxtpmacpxL21inSY2qt6mJHXRpK1Fe_Av-M8koWxw4MCpUVVXjZvW-Wx_NkL3QJUvlFCE1YASKhxOuJSMCO4JGagqS2wlpqjn9_s8joNBQVa3XBgAsMlnUDFDG8tPcrUyrjL9hQfcFCTfRjumdVZB11q7VLQ1pBqdF1lc-uKH7rDeNXE_AwTdWuVb_lcnFWtI2of_fIQjVN5Q8vBgbWyO0RZkJ-jgRzXBU_QR94ajkHSiVvMRN4z1I2GeiCmumzYQeLSa5XM8hNdZQTfKsMgSHD53scGgm0qyOLRdpbHNJsCRJZ_jeLpYzqxAxyTQ5DN750gj7cKbiFuZocfPSRPssYxe2q1Ro0OKbgtk4vh8SYDrnWMgFZdMpkr_dkBPk2sAkXLmQ-DXgIPn05oMhO84kqlEVLWMVwXmeYne9pyhUpZncI6wx2RAuZu6KnVoSrlwWUol5dJRHmhEeYHKRrPjt6-CGuNvpV7-cf4O7XVGYW_c6_afrtC-eZcmoOPQa1TSuoEbtKvel5PF_NYuiE8Y8LbV
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%3Abook&rft.genre=proceeding&rft.title=Proceedings+%28International+Symposium+on+Biomedical+Imaging%29&rft.atitle=XLSTM-HVED%3A+Cross-Modal+Brain+Tumor+Segmentation+and+MRI+Reconstruction+Method+Using+Vision+Xlstm+and+Heteromodal+Variational+Encoder-Decoder&rft.au=Zhu%2C+Shenghao&rft.au=Chen%2C+Yifei&rft.au=Jiang%2C+Shuo&rft.au=Chen%2C+Weihong&rft.date=2025-04-14&rft.pub=IEEE&rft.eissn=1945-8452&rft.spage=1&rft.epage=5&rft_id=info:doi/10.1109%2FISBI60581.2025.10981275&rft.externalDocID=10981275