FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4309747377> ?p ?o ?g. }

• W4309747377 endingPage "886" @default.
• W4309747377 startingPage "871" @default.
• W4309747377 abstract "Segmenting the pancreas from abdominal CT scans is challenging since it often takes up a relatively small region. Researchers suggested leveraging coarse-to-fine approaches to cope with this challenge. However, the coarse-scaled segmentation and the fine-scaled segmentation are either trained separately utilizing the coordinates located by the coarse-scaled segmentation mask to crop the fine-scaled segmentation input, or trained jointly utilizing the coarse-scaled segmentation mask to enhance the fine-scaled segmentation input. We argued that these two solutions are complementary to some extent and can promote each other to improve the performance of pancreas segmentation. In addition, the backbone in the coarse-scaled segmentation and fine-scaled segmentation is mostly based on UNet or UNet-like networks, where the multi-scale features transmitted from the encoder to the decoder have not been explored for vertical calibration before. In this paper, we propose a cascaded multi-scale feature calibration UNet (CMFCUNet) for pancreas segmentation where the multi-scale features in the backbone of each scaled segmentation are calibrated vertically in a pixel-wise fashion. Besides, the coarse-scaled segmentation and the fine-scaled segmentation are connected by leveraging a designed dual enhancement module (DEM). Experiments are first conducted on the public NIH pancreas dataset. First, when leveraging CMFCUNet, our method increased by over 3% on the Jaccard index (JI) and nearly 1% on dice similarity coefficient (DSC) which surpasses all existing pancreas segmentation approaches. In addition, our experiments demonstrate that CMFCUNet improved the coarse-to-fine segmentation framework and outperformed the mainstream coarse-to-fine pancreas segmentation approaches. Furthermore, we also conducted ablation studies to analyze the effectiveness of the backbone (MFCUNet) and the DEM. In addition to the experiments on the NIH dataset, we also experimentally demonstrate the excellent generalization of our method on the MSD pancreas dataset." @default.
• W4309747377 created "2022-11-29" @default.
• W4309747377 creator A5008659449 @default.
• W4309747377 creator A5010229913 @default.
• W4309747377 creator A5011656910 @default.
• W4309747377 creator A5019445464 @default.
• W4309747377 creator A5056485902 @default.
• W4309747377 creator A5091161457 @default.
• W4309747377 date "2022-11-22" @default.
• W4309747377 modified "2023-10-17" @default.
• W4309747377 title "CMFCUNet: cascaded multi-scale feature calibration UNet for pancreas segmentation" @default.
• W4309747377 cites W1494621122 @default.
• W4309747377 cites W1901129140 @default.
• W4309747377 cites W1903029394 @default.
• W4309747377 cites W191916141 @default.
• W4309747377 cites W2000585255 @default.
• W4309747377 cites W2009467975 @default.
• W4309747377 cites W2016991777 @default.
• W4309747377 cites W2057332541 @default.
• W4309747377 cites W2255466643 @default.
• W4309747377 cites W2493418695 @default.
• W4309747377 cites W2546807699 @default.
• W4309747377 cites W2585890928 @default.
• W4309747377 cites W2601258016 @default.
• W4309747377 cites W2618237340 @default.
• W4309747377 cites W2700917049 @default.
• W4309747377 cites W2792124446 @default.
• W4309747377 cites W2886667086 @default.
• W4309747377 cites W2913637767 @default.
• W4309747377 cites W2946217929 @default.
• W4309747377 cites W2963163009 @default.
• W4309747377 cites W2963874206 @default.
• W4309747377 cites W2964065884 @default.
• W4309747377 cites W2966708817 @default.
• W4309747377 cites W2980204380 @default.
• W4309747377 cites W2996290406 @default.
• W4309747377 cites W3002095852 @default.
• W4309747377 cites W3004890872 @default.
• W4309747377 cites W3008652188 @default.
• W4309747377 cites W3015788359 @default.
• W4309747377 cites W3026449616 @default.
• W4309747377 cites W3080280715 @default.
• W4309747377 cites W3104007979 @default.
• W4309747377 cites W3108860939 @default.
• W4309747377 cites W3136424010 @default.
• W4309747377 cites W3158290426 @default.
• W4309747377 cites W3166758207 @default.
• W4309747377 cites W3176616269 @default.
• W4309747377 cites W3187492106 @default.
• W4309747377 cites W3199298080 @default.
• W4309747377 cites W3200869553 @default.
• W4309747377 cites W4200181481 @default.
• W4309747377 cites W4200421860 @default.
• W4309747377 cites W855272188 @default.
• W4309747377 doi "https://doi.org/10.1007/s00530-022-01020-7" @default.
• W4309747377 hasPublicationYear "2022" @default.
• W4309747377 type Work @default.
• W4309747377 citedByCount "4" @default.
• W4309747377 countsByYear W43097473772023 @default.
• W4309747377 crossrefType "journal-article" @default.
• W4309747377 hasAuthorship W4309747377A5008659449 @default.
• W4309747377 hasAuthorship W4309747377A5010229913 @default.
• W4309747377 hasAuthorship W4309747377A5011656910 @default.
• W4309747377 hasAuthorship W4309747377A5019445464 @default.
• W4309747377 hasAuthorship W4309747377A5056485902 @default.
• W4309747377 hasAuthorship W4309747377A5091161457 @default.
• W4309747377 hasBestOaLocation W43097473772 @default.
• W4309747377 hasConcept C103278499 @default.
• W4309747377 hasConcept C115961682 @default.
• W4309747377 hasConcept C124504099 @default.
• W4309747377 hasConcept C138885662 @default.
• W4309747377 hasConcept C153180895 @default.
• W4309747377 hasConcept C154945302 @default.
• W4309747377 hasConcept C160633673 @default.
• W4309747377 hasConcept C203519979 @default.
• W4309747377 hasConcept C205649164 @default.
• W4309747377 hasConcept C25694479 @default.
• W4309747377 hasConcept C2776401178 @default.
• W4309747377 hasConcept C2778755073 @default.
• W4309747377 hasConcept C31972630 @default.
• W4309747377 hasConcept C41008148 @default.
• W4309747377 hasConcept C41895202 @default.
• W4309747377 hasConcept C58640448 @default.
• W4309747377 hasConcept C65885262 @default.
• W4309747377 hasConcept C89600930 @default.
• W4309747377 hasConceptScore W4309747377C103278499 @default.
• W4309747377 hasConceptScore W4309747377C115961682 @default.
• W4309747377 hasConceptScore W4309747377C124504099 @default.
• W4309747377 hasConceptScore W4309747377C138885662 @default.
• W4309747377 hasConceptScore W4309747377C153180895 @default.
• W4309747377 hasConceptScore W4309747377C154945302 @default.
• W4309747377 hasConceptScore W4309747377C160633673 @default.
• W4309747377 hasConceptScore W4309747377C203519979 @default.
• W4309747377 hasConceptScore W4309747377C205649164 @default.
• W4309747377 hasConceptScore W4309747377C25694479 @default.
• W4309747377 hasConceptScore W4309747377C2776401178 @default.
• W4309747377 hasConceptScore W4309747377C2778755073 @default.
• W4309747377 hasConceptScore W4309747377C31972630 @default.

• next