FSE-Net: Rethinking the up-sampling operation in encoder-decoder structure for retinal vessel segmentation
•We present an efficient medical image segmentation framework called FSE-Net for retinal vessel segmentation, which eliminates the up-sampling operation.•To enhance the feature extraction capability in the encoder stage, we introduce the Residual Feature Separable Block (RFSB) module, which effectiv...
Uloženo v:
| Vydáno v: | Biomedical signal processing and control Ročník 90; s. 105861 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.04.2024
|
| Témata: | |
| ISSN: | 1746-8094, 1746-8108 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | •We present an efficient medical image segmentation framework called FSE-Net for retinal vessel segmentation, which eliminates the up-sampling operation.•To enhance the feature extraction capability in the encoder stage, we introduce the Residual Feature Separable Block (RFSB) module, which effectively extracts and refines low-level features, increasing the receptive field of the network.•We introduce the Multi-Head Feature Fusion (MFF) module, which effectively merges low-level and high-level features, streamlining encoder-decoder structure.
Automatic retinal vessel segmentation plays a crucial role in the diagnosis and assessment of various ophthalmologic diseases. Currently, the primary retinal vessel segmentation algorithms are based on the encoder-decoder structure. However, these U-Net analogs suffer from the loss of both spatial and semantic information, caused by continuous up-sampling operations in the decoder structure. In this paper, we rethink the above problem and build a novel deep neural network for retinal vessel segmentation, called FSE-Net. Specifically, to address the issue of feature information loss and enhance the performance of retinal vessel segmentation, we eliminate the decoder structure. In particular, we introduced a multi-head feature fusion block (MFF) as a substitute for the continuous up-sampling operation. Additionally, the encoder stage of FSE-Net incorporates a residual feature separable block (RFSB) to further refine and distill features, thereby enhancing the capability of feature extraction. Subsequently, we employ a residual atrous spatial feature aggregate module (RASF) to expand the network's receptive field by incorporating multi-scale feature information. We conducted experiments on five widely recognized databases for retinal vessel segmentation, namely DRIVE, CHASEDB1, STARE, IOSTAR, and LES-AV. The results demonstrate that our proposed FSE-Net outperforms state-of-the-art approaches in terms of segmentation performance. Moreover, we demonstrate the feasibility of achieving superior segmentation performance without employing the traditional U-Net analog network structure. |
|---|---|
| AbstractList | •We present an efficient medical image segmentation framework called FSE-Net for retinal vessel segmentation, which eliminates the up-sampling operation.•To enhance the feature extraction capability in the encoder stage, we introduce the Residual Feature Separable Block (RFSB) module, which effectively extracts and refines low-level features, increasing the receptive field of the network.•We introduce the Multi-Head Feature Fusion (MFF) module, which effectively merges low-level and high-level features, streamlining encoder-decoder structure.
Automatic retinal vessel segmentation plays a crucial role in the diagnosis and assessment of various ophthalmologic diseases. Currently, the primary retinal vessel segmentation algorithms are based on the encoder-decoder structure. However, these U-Net analogs suffer from the loss of both spatial and semantic information, caused by continuous up-sampling operations in the decoder structure. In this paper, we rethink the above problem and build a novel deep neural network for retinal vessel segmentation, called FSE-Net. Specifically, to address the issue of feature information loss and enhance the performance of retinal vessel segmentation, we eliminate the decoder structure. In particular, we introduced a multi-head feature fusion block (MFF) as a substitute for the continuous up-sampling operation. Additionally, the encoder stage of FSE-Net incorporates a residual feature separable block (RFSB) to further refine and distill features, thereby enhancing the capability of feature extraction. Subsequently, we employ a residual atrous spatial feature aggregate module (RASF) to expand the network's receptive field by incorporating multi-scale feature information. We conducted experiments on five widely recognized databases for retinal vessel segmentation, namely DRIVE, CHASEDB1, STARE, IOSTAR, and LES-AV. The results demonstrate that our proposed FSE-Net outperforms state-of-the-art approaches in terms of segmentation performance. Moreover, we demonstrate the feasibility of achieving superior segmentation performance without employing the traditional U-Net analog network structure. |
| ArticleNumber | 105861 |
| Author | Pan, An Mu, Wei Ni, Jiajia Chen, Zhengming |
| Author_xml | – sequence: 1 givenname: Jiajia orcidid: 0000-0002-0158-0890 surname: Ni fullname: Ni, Jiajia organization: School of Artificial Intelligence, Anhui Polytechnic University, Wuhu, China – sequence: 2 givenname: Wei surname: Mu fullname: Mu, Wei organization: College of Internet of Things Engineering, HoHai University, Changzhou, China – sequence: 3 givenname: An surname: Pan fullname: Pan, An organization: College of Internet of Things Engineering, HoHai University, Changzhou, China – sequence: 4 givenname: Zhengming surname: Chen fullname: Chen, Zhengming organization: College of Internet of Things Engineering, HoHai University, Changzhou, China |
| BookMark | eNp9kE1OwzAQRi1UJNrCBVj5Ail2nDg2YoOqFpAQSPysLdeZtC6pE9luJW5P0sCGRVffzEhvNPMmaOQaBwhdUzKjhPKb7WwVWjNLScq6QS44PUNjWmQ8EZSI0V9NZHaBJiFsCclEQbMx2i7fF8kLxFv8BnFj3Zd1axw3gPdtEvSurfu-acHraBuHrcPgTFOCT0o4Jg7R703ce8BV47GHaJ2u8QFCgBoHWO_AxSN8ic4rXQe4-s0p-lwuPuaPyfPrw9P8_jkxjJCYVDLXkhgJmmgBRHJTVDKDHErDM8NEylc6zcqKlaIUNGeyygWrCq45lxQkY1Mkhr3GNyF4qJSxwwXRa1srSlTvTG1V70z1ztTgrEPTf2jr7U7779PQ3QBB99TBglfB2M4SlNaDiaps7Cn8BybNic8 |
| CitedBy_id | crossref_primary_10_1016_j_bspc_2025_108598 crossref_primary_10_3390_s24165372 crossref_primary_10_1007_s00530_025_01789_3 crossref_primary_10_1007_s10044_025_01424_x crossref_primary_10_1007_s11760_025_04415_x crossref_primary_10_1016_j_bspc_2024_107175 crossref_primary_10_1016_j_bspc_2025_108463 crossref_primary_10_1016_j_bspc_2024_107150 crossref_primary_10_1007_s11517_024_03223_8 crossref_primary_10_1002_ima_70213 crossref_primary_10_1016_j_measurement_2025_118695 crossref_primary_10_1016_j_compmedimag_2025_102548 crossref_primary_10_1016_j_inffus_2024_102777 crossref_primary_10_1016_j_jksuci_2024_102123 |
| Cites_doi | 10.1016/j.media.2021.102025 10.5772/60581 10.1109/TVCG.2020.3030374 10.1016/j.bspc.2023.104829 10.1155/2022/4695136 10.1016/j.media.2017.07.005 10.1117/1.JBO.18.12.126011 10.1167/iovs.08-3018 10.1109/TMI.2019.2903562 10.1016/j.neunet.2023.05.029 10.1016/j.compbiomed.2021.104449 10.1016/j.bspc.2021.102977 10.1109/42.845178 10.1016/j.cmpb.2019.105121 10.1109/TMI.2016.2587062 10.1109/TMI.2018.2867837 10.1109/TMI.2018.2791721 10.1109/ICCV.2019.00069 10.1016/j.compbiomed.2022.106341 10.1109/TMI.2004.825627 10.1016/j.measurement.2022.112316 10.1109/TPAMI.2019.2938758 10.1109/JBHI.2022.3182471 10.1016/j.cmpb.2021.106070 10.1109/JBHI.2020.3028180 10.1007/978-3-030-00937-3_48 10.1016/j.bspc.2022.104087 10.1186/s12880-022-00836-z |
| ContentType | Journal Article |
| Copyright | 2023 Elsevier Ltd |
| Copyright_xml | – notice: 2023 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.bspc.2023.105861 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1746-8108 |
| ExternalDocumentID | 10_1016_j_bspc_2023_105861 S1746809423012946 |
| GroupedDBID | --- --K --M .~1 0R~ 1B1 1~. 1~5 23N 4.4 457 4G. 5GY 5VS 6J9 7-5 71M 8P~ AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXUO AAYFN ABBOA ABFNM ABFRF ABJNI ABMAC ABXDB ABYKQ ACDAQ ACGFO ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADMUD ADTZH AEBSH AECPX AEFWE AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD AXJTR BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HZ~ IHE J1W JJJVA KOM M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RIG ROL RPZ SDF SDG SES SPC SPCBC SST SSV SSZ T5K UNMZH ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c300t-f95a90c9ea0a8e096c7f94e5edc64c3826ba24df3d8d81539f583f76a6691e933 |
| ISICitedReferencesCount | 19 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001143296300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1746-8094 |
| IngestDate | Tue Nov 18 22:15:55 EST 2025 Sat Nov 29 07:01:35 EST 2025 Sat Feb 24 15:48:58 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Retinal vessel segmentation Feature fusion Convolutional neural network U-Net |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c300t-f95a90c9ea0a8e096c7f94e5edc64c3826ba24df3d8d81539f583f76a6691e933 |
| ORCID | 0000-0002-0158-0890 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_bspc_2023_105861 crossref_primary_10_1016_j_bspc_2023_105861 elsevier_sciencedirect_doi_10_1016_j_bspc_2023_105861 |
| PublicationCentury | 2000 |
| PublicationDate | April 2024 2024-04-00 |
| PublicationDateYYYYMMDD | 2024-04-01 |
| PublicationDate_xml | – month: 04 year: 2024 text: April 2024 |
| PublicationDecade | 2020 |
| PublicationTitle | Biomedical signal processing and control |
| PublicationYear | 2024 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Chen, Liu, Zhang, Lu, Kong (b0110) 2023 Ni, Wu, Elazab, Tong, Chen (b0145) 2022; 22 K. Li, X. Qi, Y. Luo, Z. Yao, X. Zhou, M. Sun, Informatics h: Accurate Retinal Vessel Segmentation in Color Fundus Images via Fully Attention-Based Networks 25(6) (2020) 2071–2081. 1-8. Wang, Li, Zuluaga, Pratt, Patel, Aertsen, Doel, David, Deprest, Ourselin (b0010) 2018; 37 Xu, Yang, Wang, Xiao, Xing, Zhang, Wang, Xu, Zhang, Lei (b0125) 2022 Zhou, Siddiquee, Tajbakhsh, Liang (b0070) 2018 (2) (2018) 585-595. Owen, Rudnicka, Mullen, Barman, Monekosso, Whincup, Ng, Paterson (b0185) 2009; 50 Ni, Wu, Tong, Chen, Zhao (b0045) 2020; 190 Li, Verma, Nakashima, Nagahara, Kawasaki (b0050) 2020 Hoover, Kouznetsova, Goldbaum (b0190) 2000; 19 Li, Gao, Liu, Yang (b0060) 2023; 206 Ni, Sun, Xu, Liu, Chen (b0115) 2023; 85 Wu, Wang, Zhong, Lei, Wen, Qin (b0015) 2021; 70 Khan, Naqvi, Robles-Kelly, Razzak (b0055) 2023 Z. Huang, X. Wang, L. Huang, C. Huang, Y. Wei, W. Wei Ni, Liu, Li, Chen (b0080) 2022; 2022 2017. Gu, Cheng, Fu, Zhou, Hao, Zhao, Zhang, Gao, Liu (b0040) 2019; 38 (2021) 104449. H.R. Roth, H. Oda, Y. Hayashi, M. Oda, N. Shimizu, M. Fujiwara, K. Misawa, K. Misawa Staal, Abràmoff, Niemeijer, Viergever, Van Ginneken (b0180) 2004; 23 Huang, Lin, Tong, Hu, Zhang, Iwamoto, Han, Chen, Wu (b0120) 2020 Wang, Lin, Li, Li, Shen, Gao, Ma (b0105) 2023 F. Isensee, J. Petersen, S.A. Kohl, P.F. Jäger, K.H. Maier-Hein J. Zhang, B. Dashtbozorg, E. Bekkers, J.P. Pluim, R. Duits, B. Ter Haar Romeny, Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores 35(12) (2016) 2631–2644. Zhang, Zhang, Xu (b0135) 2021 Litjens, Kooi, Bejnordi, Setio, Ciompi, Ghafoorian, Van Der Laak, Van Ginneken, Sánchez (b0005) 2017; 42 Mapayi, Tapamo, Viriri (b0020) 2015; 12 P. Rodrigues, P. Guimaraes, T. Santos, S. Simao, T. Miranda, P. Serranho, R. Bernardes . Ye, Pan, Wu, Wang, Xia (b0155) 2022; 26 Gao, Cheng, Zhao, Zhang, Yang, Torr (b0170) 2019; 43 2019 (12) (2013) 126011. Li, Liu, Chen, Cai, Gu, Qiao, Dong (b0175) 2022, 2022 Wang, Yan, Zhu, Buch, Wang, Haacke, Hua, Zhong (b0085) 2020; 27 Long, Shelhamer, Darrell (b0090) 2015, 2015 Liu, Shen, Yang, Bian, Yu (b0160) 2023; 79 Ronneberger, Fischer, Brox (b0030) 2015 Z. Shi, T. Wang, Z. Huang, F. Xie, Z. Liu, B. Wang, J. Xu, Control: MD-Net: A Multi-Scale Dense Network for Retinal Vessel Segmentation 70 (2021) 102977. Orlando, Barbosa Breda, Kv, Blaschko, Blanco, Bulant (b0200) 2018 K. He, X. Cao, Y. Shi, D. Nie, Y. Gao, D. Shen Liu, Shen, Yang, Yu, Bian (b0165) 2023; 152 Z. Wang, Y. Zou, P. Liu, Medicine 2018. Li, Rahardja (b0140) 2021; 205 Li (10.1016/j.bspc.2023.105861_b0140) 2021; 205 Xu (10.1016/j.bspc.2023.105861_b0125) 2022 Khan (10.1016/j.bspc.2023.105861_b0055) 2023 Ye (10.1016/j.bspc.2023.105861_b0155) 2022; 26 Staal (10.1016/j.bspc.2023.105861_b0180) 2004; 23 10.1016/j.bspc.2023.105861_b0095 10.1016/j.bspc.2023.105861_b0150 10.1016/j.bspc.2023.105861_b0195 10.1016/j.bspc.2023.105861_b0075 Huang (10.1016/j.bspc.2023.105861_b0120) 2020 10.1016/j.bspc.2023.105861_b0130 Wang (10.1016/j.bspc.2023.105861_b0085) 2020; 27 Wu (10.1016/j.bspc.2023.105861_b0015) 2021; 70 10.1016/j.bspc.2023.105861_b0035 Liu (10.1016/j.bspc.2023.105861_b0165) 2023; 152 Long (10.1016/j.bspc.2023.105861_b0090) 2015 Ni (10.1016/j.bspc.2023.105861_b0145) 2022; 22 Wang (10.1016/j.bspc.2023.105861_b0105) 2023 Ronneberger (10.1016/j.bspc.2023.105861_b0030) 2015 Li (10.1016/j.bspc.2023.105861_b0060) 2023; 206 Chen (10.1016/j.bspc.2023.105861_b0110) 2023 Ni (10.1016/j.bspc.2023.105861_b0045) 2020; 190 Gu (10.1016/j.bspc.2023.105861_b0040) 2019; 38 Owen (10.1016/j.bspc.2023.105861_b0185) 2009; 50 Hoover (10.1016/j.bspc.2023.105861_b0190) 2000; 19 Wang (10.1016/j.bspc.2023.105861_b0010) 2018; 37 Gao (10.1016/j.bspc.2023.105861_b0170) 2019; 43 Litjens (10.1016/j.bspc.2023.105861_b0005) 2017; 42 10.1016/j.bspc.2023.105861_b0065 Ni (10.1016/j.bspc.2023.105861_b0080) 2022; 2022 10.1016/j.bspc.2023.105861_b0100 10.1016/j.bspc.2023.105861_b0025 Li (10.1016/j.bspc.2023.105861_b0050) 2020 Li (10.1016/j.bspc.2023.105861_b0175) 2022 Mapayi (10.1016/j.bspc.2023.105861_b0020) 2015; 12 Zhou (10.1016/j.bspc.2023.105861_b0070) 2018 Zhang (10.1016/j.bspc.2023.105861_b0135) 2021 Ni (10.1016/j.bspc.2023.105861_b0115) 2023; 85 Liu (10.1016/j.bspc.2023.105861_b0160) 2023; 79 Orlando (10.1016/j.bspc.2023.105861_b0200) 2018 |
| References_xml | – reference: Z. Shi, T. Wang, Z. Huang, F. Xie, Z. Liu, B. Wang, J. Xu, Control: MD-Net: A Multi-Scale Dense Network for Retinal Vessel Segmentation 70 (2021) 102977. – year: 2018 ident: b0200 article-title: Towards a glaucoma risk index based on simulated hemodynamics from fundus images publication-title: In: – reference: (2) (2018) 585-595. – volume: 2022 start-page: 4695136 year: 2022 ident: b0080 article-title: SFA-Net: Scale and Feature Aggregate Network for Retinal Vessel Segmentation publication-title: J Healthc Eng – volume: 12 start-page: 133 year: 2015 ident: b0020 article-title: Retinal vessel segmentation: a comparative study of fuzzy C-means and sum entropy information on phase congruency publication-title: Int. J. Adv. Rob. Syst. – reference: Z. Huang, X. Wang, L. Huang, C. Huang, Y. Wei, W. Wei, – start-page: 3656 year: 2020 end-page: 3665 ident: b0050 article-title: Iternet: Retinal image segmentation utilizing structural redundancy in vessel networks – volume: 19 start-page: 203 year: 2000 end-page: 210 ident: b0190 article-title: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response publication-title: IEEE Trans. Med. Imaging – start-page: 1055 year: 2020 end-page: 1059 ident: b0120 article-title: Unet 3+: A full-scale connected unet for medical image segmentation publication-title: In: – volume: 42 start-page: 60 year: 2017 end-page: 88 ident: b0005 article-title: A survey on deep learning in medical image analysis publication-title: Med. Image Anal. – start-page: 3 year: 2018 end-page: 11 ident: b0070 article-title: Unet++: A nested u-net architecture for medical image segmentation publication-title: In: – reference: K. Li, X. Qi, Y. Luo, Z. Yao, X. Zhou, M. Sun, Informatics h: Accurate Retinal Vessel Segmentation in Color Fundus Images via Fully Attention-Based Networks 25(6) (2020) 2071–2081. – reference: Z. Wang, Y. Zou, P. Liu, Medicine: – year: 2023 ident: b0105 article-title: MISSU: 3D medical image segmentation via self-distilling TransUNet publication-title: IEEE Trans. Med. Imaging – volume: 23 start-page: 501 year: 2004 end-page: 509 ident: b0180 article-title: Ridge-based vessel segmentation in color images of the retina publication-title: IEEE Trans. Med. Imaging – reference: , 2017. – volume: 79 year: 2023 ident: b0160 article-title: ResDO-UNet: A deep residual network for accurate retinal vessel segmentation from fundus images publication-title: Biomed. Signal Process. Control – volume: 70 year: 2021 ident: b0015 article-title: Scs-net: A scale and context sensitive network for retinal vessel segmentation publication-title: Med. Image Anal. – reference: F. Isensee, J. Petersen, S.A. Kohl, P.F. Jäger, K.H. Maier-Hein, – start-page: 234 year: 2015 end-page: 241 ident: b0030 article-title: U-net: Convolutional networks for biomedical image segmentation – reference: (2021) 104449. – reference: K. He, X. Cao, Y. Shi, D. Nie, Y. Gao, D. Shen, – volume: 190 year: 2020 ident: b0045 article-title: GC-Net: Global context network for medical image segmentation publication-title: Comput Methods Programs Biomed – volume: 38 start-page: 2281 year: 2019 end-page: 2292 ident: b0040 article-title: CE-Net: Context Encoder Network for 2D Medical Image Segmentation publication-title: IEEE Trans Med Imaging – reference: P. Rodrigues, P. Guimaraes, T. Santos, S. Simao, T. Miranda, P. Serranho, R. Bernardes, – reference: :1-8. – reference: H.R. Roth, H. Oda, Y. Hayashi, M. Oda, N. Shimizu, M. Fujiwara, K. Misawa, K. Misawa, – year: 2022 ident: b0125 article-title: AV-casNet: Fully Automatic Arteriole-Venule Segmentation and Differentiation in OCT Angiography publication-title: IEEE Trans Med Imaging – volume: 22 start-page: 1 year: 2022 end-page: 14 ident: b0145 article-title: DNL-Net: deformed non-local neural network for blood vessel segmentation publication-title: BMC Med Imaging – volume: 85 year: 2023 ident: b0115 article-title: A feature aggregation and feature fusion network for retinal vessel segmentation publication-title: Biomed. Signal Process. Control – reference: (12) (2013) 126011. – volume: 50 start-page: 2004 year: 2009 end-page: 2010 ident: b0185 article-title: Measuring retinal vessel tortuosity in 10-year-old children: validation of the computer-assisted image analysis of the retina (CAIAR) program publication-title: Invest Ophthalmol vis Sci – volume: 37 start-page: 1562 year: 2018 end-page: 1573 ident: b0010 article-title: Interactive medical image segmentation using deep learning with image-specific fine tuning publication-title: IEEE Trans. Med. Imaging – reference: 2018. – year: 2023 ident: b0055 article-title: Retinal vessel segmentation via a Multi-resolution Contextual Network and adversarial learning publication-title: Neural Netw – start-page: 1125 year: 2021 end-page: 1129 ident: b0135 article-title: Pyramid u-net for retinal vessel segmentation publication-title: In: – start-page: 3431 year: 2015, 2015, end-page: 3440 ident: b0090 article-title: Fully convolutional networks for semantic segmentation publication-title: In: – reference: J. Zhang, B. Dashtbozorg, E. Bekkers, J.P. Pluim, R. Duits, B. Ter Haar Romeny, Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores 35(12) (2016) 2631–2644. – year: 2023 ident: b0110 article-title: Transattunet: publication-title: IEEE Trans. Emerging Topics Comput. Intell. – reference: 2019, – reference: . – volume: 43 start-page: 652 year: 2019 end-page: 662 ident: b0170 article-title: Res2net: A new multi-scale backbone architecture publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – start-page: 833 year: 2022, 2022, end-page: 843 ident: b0175 article-title: Blueprint Separable Residual Network for Efficient Image Super-Resolution publication-title: In: – volume: 205 year: 2021 ident: b0140 article-title: BSEResU-Net: An attention-based before-activation residual U-Net for retinal vessel segmentation publication-title: Comput Methods Programs Biomed – volume: 26 start-page: 4551 year: 2022 end-page: 4562 ident: b0155 article-title: MFI-Net: Multiscale Feature Interaction Network for Retinal Vessel Segmentation publication-title: IEEE J Biomed Health Inform – volume: 152 year: 2023 ident: b0165 article-title: Wave-Net: A lightweight deep network for retinal vessel segmentation from fundus images publication-title: Comput. Biol. Med. – volume: 206 year: 2023 ident: b0060 article-title: MAGF-Net: A multiscale attention-guided fusion network for retinal vessel segmentation publication-title: Measurement – volume: 27 start-page: 1301 year: 2020 end-page: 1311 ident: b0085 article-title: VC-Net: deep volume-composition networks for segmentation and visualization of highly sparse and noisy image data publication-title: IEEE Trans. Vis. Comput. Graph. – volume: 70 year: 2021 ident: 10.1016/j.bspc.2023.105861_b0015 article-title: Scs-net: A scale and context sensitive network for retinal vessel segmentation publication-title: Med. Image Anal. doi: 10.1016/j.media.2021.102025 – volume: 12 start-page: 133 issue: 9 year: 2015 ident: 10.1016/j.bspc.2023.105861_b0020 article-title: Retinal vessel segmentation: a comparative study of fuzzy C-means and sum entropy information on phase congruency publication-title: Int. J. Adv. Rob. Syst. doi: 10.5772/60581 – start-page: 3656 year: 2020 ident: 10.1016/j.bspc.2023.105861_b0050 article-title: Iternet: Retinal image segmentation utilizing structural redundancy in vessel networks – start-page: 1125 year: 2021 ident: 10.1016/j.bspc.2023.105861_b0135 article-title: Pyramid u-net for retinal vessel segmentation – year: 2023 ident: 10.1016/j.bspc.2023.105861_b0105 article-title: MISSU: 3D medical image segmentation via self-distilling TransUNet publication-title: IEEE Trans. Med. Imaging – volume: 27 start-page: 1301 issue: 2 year: 2020 ident: 10.1016/j.bspc.2023.105861_b0085 article-title: VC-Net: deep volume-composition networks for segmentation and visualization of highly sparse and noisy image data publication-title: IEEE Trans. Vis. Comput. Graph. doi: 10.1109/TVCG.2020.3030374 – volume: 85 year: 2023 ident: 10.1016/j.bspc.2023.105861_b0115 article-title: A feature aggregation and feature fusion network for retinal vessel segmentation publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2023.104829 – start-page: 234 year: 2015 ident: 10.1016/j.bspc.2023.105861_b0030 article-title: U-net: Convolutional networks for biomedical image segmentation – volume: 2022 start-page: 4695136 year: 2022 ident: 10.1016/j.bspc.2023.105861_b0080 article-title: SFA-Net: Scale and Feature Aggregate Network for Retinal Vessel Segmentation publication-title: J Healthc Eng doi: 10.1155/2022/4695136 – volume: 42 start-page: 60 year: 2017 ident: 10.1016/j.bspc.2023.105861_b0005 article-title: A survey on deep learning in medical image analysis publication-title: Med. Image Anal. doi: 10.1016/j.media.2017.07.005 – ident: 10.1016/j.bspc.2023.105861_b0075 – ident: 10.1016/j.bspc.2023.105861_b0025 doi: 10.1117/1.JBO.18.12.126011 – start-page: 833 year: 2022 ident: 10.1016/j.bspc.2023.105861_b0175 article-title: Blueprint Separable Residual Network for Efficient Image Super-Resolution – volume: 50 start-page: 2004 issue: 5 year: 2009 ident: 10.1016/j.bspc.2023.105861_b0185 article-title: Measuring retinal vessel tortuosity in 10-year-old children: validation of the computer-assisted image analysis of the retina (CAIAR) program publication-title: Invest Ophthalmol vis Sci doi: 10.1167/iovs.08-3018 – volume: 38 start-page: 2281 issue: 10 year: 2019 ident: 10.1016/j.bspc.2023.105861_b0040 article-title: CE-Net: Context Encoder Network for 2D Medical Image Segmentation publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2019.2903562 – year: 2023 ident: 10.1016/j.bspc.2023.105861_b0055 article-title: Retinal vessel segmentation via a Multi-resolution Contextual Network and adversarial learning publication-title: Neural Netw doi: 10.1016/j.neunet.2023.05.029 – ident: 10.1016/j.bspc.2023.105861_b0065 doi: 10.1016/j.compbiomed.2021.104449 – start-page: 3431 year: 2015 ident: 10.1016/j.bspc.2023.105861_b0090 article-title: Fully convolutional networks for semantic segmentation – year: 2022 ident: 10.1016/j.bspc.2023.105861_b0125 article-title: AV-casNet: Fully Automatic Arteriole-Venule Segmentation and Differentiation in OCT Angiography publication-title: IEEE Trans Med Imaging – ident: 10.1016/j.bspc.2023.105861_b0130 doi: 10.1016/j.bspc.2021.102977 – volume: 19 start-page: 203 issue: 3 year: 2000 ident: 10.1016/j.bspc.2023.105861_b0190 article-title: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response publication-title: IEEE Trans. Med. Imaging doi: 10.1109/42.845178 – year: 2018 ident: 10.1016/j.bspc.2023.105861_b0200 article-title: Towards a glaucoma risk index based on simulated hemodynamics from fundus images – volume: 190 year: 2020 ident: 10.1016/j.bspc.2023.105861_b0045 article-title: GC-Net: Global context network for medical image segmentation publication-title: Comput Methods Programs Biomed doi: 10.1016/j.cmpb.2019.105121 – year: 2023 ident: 10.1016/j.bspc.2023.105861_b0110 article-title: Transattunet: Multi-level attention-guided u-net with transformer for medical image segmentation publication-title: IEEE Trans. Emerging Topics Comput. Intell. – ident: 10.1016/j.bspc.2023.105861_b0195 doi: 10.1109/TMI.2016.2587062 – ident: 10.1016/j.bspc.2023.105861_b0100 doi: 10.1109/TMI.2018.2867837 – volume: 37 start-page: 1562 issue: 7 year: 2018 ident: 10.1016/j.bspc.2023.105861_b0010 article-title: Interactive medical image segmentation using deep learning with image-specific fine tuning publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2018.2791721 – start-page: 3 year: 2018 ident: 10.1016/j.bspc.2023.105861_b0070 article-title: Unet++: A nested u-net architecture for medical image segmentation – ident: 10.1016/j.bspc.2023.105861_b0035 doi: 10.1109/ICCV.2019.00069 – volume: 152 year: 2023 ident: 10.1016/j.bspc.2023.105861_b0165 article-title: Wave-Net: A lightweight deep network for retinal vessel segmentation from fundus images publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2022.106341 – volume: 23 start-page: 501 issue: 4 year: 2004 ident: 10.1016/j.bspc.2023.105861_b0180 article-title: Ridge-based vessel segmentation in color images of the retina publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2004.825627 – volume: 206 year: 2023 ident: 10.1016/j.bspc.2023.105861_b0060 article-title: MAGF-Net: A multiscale attention-guided fusion network for retinal vessel segmentation publication-title: Measurement doi: 10.1016/j.measurement.2022.112316 – volume: 43 start-page: 652 issue: 2 year: 2019 ident: 10.1016/j.bspc.2023.105861_b0170 article-title: Res2net: A new multi-scale backbone architecture publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2019.2938758 – volume: 26 start-page: 4551 issue: 9 year: 2022 ident: 10.1016/j.bspc.2023.105861_b0155 article-title: MFI-Net: Multiscale Feature Interaction Network for Retinal Vessel Segmentation publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2022.3182471 – volume: 205 year: 2021 ident: 10.1016/j.bspc.2023.105861_b0140 article-title: BSEResU-Net: An attention-based before-activation residual U-Net for retinal vessel segmentation publication-title: Comput Methods Programs Biomed doi: 10.1016/j.cmpb.2021.106070 – ident: 10.1016/j.bspc.2023.105861_b0150 doi: 10.1109/JBHI.2020.3028180 – ident: 10.1016/j.bspc.2023.105861_b0095 doi: 10.1007/978-3-030-00937-3_48 – volume: 79 year: 2023 ident: 10.1016/j.bspc.2023.105861_b0160 article-title: ResDO-UNet: A deep residual network for accurate retinal vessel segmentation from fundus images publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2022.104087 – start-page: 1055 year: 2020 ident: 10.1016/j.bspc.2023.105861_b0120 article-title: Unet 3+: A full-scale connected unet for medical image segmentation – volume: 22 start-page: 1 issue: 1 year: 2022 ident: 10.1016/j.bspc.2023.105861_b0145 article-title: DNL-Net: deformed non-local neural network for blood vessel segmentation publication-title: BMC Med Imaging doi: 10.1186/s12880-022-00836-z |
| SSID | ssj0048714 |
| Score | 2.433268 |
| Snippet | •We present an efficient medical image segmentation framework called FSE-Net for retinal vessel segmentation, which eliminates the up-sampling operation.•To... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 105861 |
| SubjectTerms | Convolutional neural network Feature fusion Retinal vessel segmentation U-Net |
| Title | FSE-Net: Rethinking the up-sampling operation in encoder-decoder structure for retinal vessel segmentation |
| URI | https://dx.doi.org/10.1016/j.bspc.2023.105861 |
| Volume | 90 |
| WOSCitedRecordID | wos001143296300001&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 | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1746-8108 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0048714 issn: 1746-8094 databaseCode: AIEXJ dateStart: 20060101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV07b9swECbcpEM7FH2i6SPg0M1gIEuiJHYLCgdNBqNAU8DoIlDk0ZXhKELsGvkN_dU9itQjbhskQxfJEERK8H043onffUfIB8m14RoMU5prFoOIWZEkwIpiIiAIhVEqbppNpLNZNp-LL6PRr7YWZrtKqyq7vhb1fzU1XkNj29LZe5i7mxQv4G80Oh7R7Hi8k-FPvk7ZDDaO6bb54XojNPHlz5qtpWWQW6JzDd72VjOkspXtV0xDcx47UVm7tWBJiLbO0casWyszvhqvYXHhC5aqG1vCTSG_q7IsF3ZA7YoQ2jJIz4rvd0IaBJVyWcre7A3nD8p-Z8tLHPQ0BOcnv-N5cdGuu_6zRThkuzhPm8ZWCdl1OG5dsesc6n0pRn6ZE2r_w827Lw7Lo2JdWxnKMDrqb76pqb2z1nUMxJbctsztHLmdI3dzPCD7YcoFesj949Pp_Kxd1zGza5Tiuxf3JViOLbj7Jn8Pcwahy_lT8sTnHPTYYeUZGUH1nDweKFG-IEuPmo-0xwxFzNABZmiHGVpWdAcztMMMRcxQjxnqMEOHmHlJvp1Mzz99Zr4NB1NREGyYEVyKQAmQgcwAU16VGhEDB62SWEWYnxYyjLWJdKYzXECF4Vlk0kQmiZiAiKJXZK-6rOA1oRKDIVGEBrN2qwrFpcHwvYhxPlVAJPkBmbT_Wq68Rr1tlbLK_22vAzLuxtROoeXWu3lrjNzHmC52zBFbt4x7c6-nvCWPesy_I3toAXhPHqrtplxfHXpg_QaxRqDc |
| linkProvider | Elsevier |
| 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=article&rft.atitle=FSE-Net%3A+Rethinking+the+up-sampling+operation+in+encoder-decoder+structure+for+retinal+vessel+segmentation&rft.jtitle=Biomedical+signal+processing+and+control&rft.au=Ni%2C+Jiajia&rft.au=Mu%2C+Wei&rft.au=Pan%2C+An&rft.au=Chen%2C+Zhengming&rft.date=2024-04-01&rft.issn=1746-8094&rft.volume=90&rft.spage=105861&rft_id=info:doi/10.1016%2Fj.bspc.2023.105861&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_bspc_2023_105861 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1746-8094&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1746-8094&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1746-8094&client=summon |