FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W61915046> ?p ?o ?g. }

• W61915046 endingPage "322" @default.
• W61915046 startingPage "301" @default.
• W61915046 abstract "Spine is one of the major organs in human body. It consists of multiple vertebrae and inter-vertebral discs. As the locations and labels of vertebrae provide a vertical reference framework to different organs in the torso, they play an important role in various neurological, orthopaedic and oncological studies. On the other hand, however, manual localization and labeling of vertebrae is often time consuming. Therefore, automatic vertebrae localization and labeling has drawn significant attentions in the community of medical image analysis. While some pioneer studies aim to localize and label vertebrae using domain knowledge, more recent studies tackle this problem via machine learning technologies. With the spirit of “data-driven”, learning-based approaches are able to extract the appearance and geometric characteristics of vertebrae more efficient and effective than hand-crafted algorithms. More importantly, it facilitates cross-modality vertebrae localization, i.e., a generic algorithm working on different imaging modalities. In this chapter, we start with a review of several representative learning-based vertebrae localization and labeling methods. The key ideas of these methods are re-visited. In order to achieve a solution that is robust to severe diseases (e.g., scoliosis) and imaging artifacts (e.g., metal artifacts), we propose a learning-based method with two novel components. First, instead of treating vertebrae/discs as either repetitive components or completely independent entities, we emulate a radiologist and use a hierarchial strategy to learn detectors dedicated to anchor (distinctive) vertebrae, bundle (non-distinctive) vertebrae and inter-vertebral discs, respectively. At run-time, anchor vertebrae are detected concurrently to provide redundant and distributed appearance cues robust to local imaging artifacts. Bundle vertebrae detectors provide candidates of vertebrae with subtle appearance differences, whose labels are mutually determined by anchor vertebrae to gain additional robustness. Disc locations are derived from a cloud of responses from disc detectors, which is robust to sporadic voxel-level errors. Second, owing to the non-rigidness of spine anatomies, we employ a local articulated model to effectively model the spatial relations across vertebrae and discs. The local articulated model fuses appearance cues from different detectors in a way that is robust to abnormal spine geometry caused by severe diseases. Our method is validated on a large scale of CT (189) and MR (300) spine scans. It exhibits robust performance, especially to cases with severe diseases and imaging artifacts." @default.
• W61915046 created "2016-06-24" @default.
• W61915046 creator A5022927618 @default.
• W61915046 creator A5047988362 @default.
• W61915046 creator A5062819602 @default.
• W61915046 creator A5089476507 @default.
• W61915046 date "2014-12-18" @default.
• W61915046 modified "2023-09-23" @default.
• W61915046 title "Cross-Modality Vertebrae Localization and Labeling Using Learning-Based Approaches" @default.
• W61915046 cites W1530395742 @default.
• W61915046 cites W1542060247 @default.
• W61915046 cites W1604154211 @default.
• W61915046 cites W184565980 @default.
• W61915046 cites W1858236522 @default.
• W61915046 cites W188688 @default.
• W61915046 cites W1950915640 @default.
• W61915046 cites W1979064019 @default.
• W61915046 cites W1988790447 @default.
• W61915046 cites W2014090484 @default.
• W61915046 cites W2038952578 @default.
• W61915046 cites W2048735994 @default.
• W61915046 cites W2068854578 @default.
• W61915046 cites W2080362539 @default.
• W61915046 cites W2091812846 @default.
• W61915046 cites W2092416145 @default.
• W61915046 cites W2093861573 @default.
• W61915046 cites W2097583462 @default.
• W61915046 cites W2101636005 @default.
• W61915046 cites W2110872054 @default.
• W61915046 cites W2137032657 @default.
• W61915046 cites W2143425433 @default.
• W61915046 cites W2163801774 @default.
• W61915046 cites W3097096317 @default.
• W61915046 doi "https://doi.org/10.1007/978-3-319-12508-4_9" @default.
• W61915046 hasPublicationYear "2014" @default.
• W61915046 type Work @default.
• W61915046 sameAs 61915046 @default.
• W61915046 citedByCount "7" @default.
• W61915046 countsByYear W619150462017 @default.
• W61915046 countsByYear W619150462018 @default.
• W61915046 countsByYear W619150462019 @default.
• W61915046 countsByYear W619150462020 @default.
• W61915046 countsByYear W619150462022 @default.
• W61915046 crossrefType "book-chapter" @default.
• W61915046 hasAuthorship W61915046A5022927618 @default.
• W61915046 hasAuthorship W61915046A5047988362 @default.
• W61915046 hasAuthorship W61915046A5062819602 @default.
• W61915046 hasAuthorship W61915046A5089476507 @default.
• W61915046 hasConcept C154945302 @default.
• W61915046 hasConcept C2780226545 @default.
• W61915046 hasConcept C31972630 @default.
• W61915046 hasConcept C41008148 @default.
• W61915046 hasConceptScore W61915046C154945302 @default.
• W61915046 hasConceptScore W61915046C2780226545 @default.
• W61915046 hasConceptScore W61915046C31972630 @default.
• W61915046 hasConceptScore W61915046C41008148 @default.
• W61915046 hasLocation W619150461 @default.
• W61915046 hasOpenAccess W61915046 @default.
• W61915046 hasPrimaryLocation W619150461 @default.
• W61915046 hasRelatedWork W1891287906 @default.
• W61915046 hasRelatedWork W1969923398 @default.
• W61915046 hasRelatedWork W2036807459 @default.
• W61915046 hasRelatedWork W2058170566 @default.
• W61915046 hasRelatedWork W2170022336 @default.
• W61915046 hasRelatedWork W2229312674 @default.
• W61915046 hasRelatedWork W258625772 @default.
• W61915046 hasRelatedWork W2755342338 @default.
• W61915046 hasRelatedWork W2772917594 @default.
• W61915046 hasRelatedWork W3116076068 @default.
• W61915046 isParatext "false" @default.
• W61915046 isRetracted "false" @default.
• W61915046 magId "61915046" @default.
• W61915046 workType "book-chapter" @default.