Structured Learning for 3-D Perivascular Space Segmentation Using Vascular Features
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhang, Jun | - |
dc.contributor.author | Gao, Yaozong | - |
dc.contributor.author | Park, Sang Hyun | - |
dc.contributor.author | Zong, Xiaopeng | - |
dc.contributor.author | Lin, Weili | - |
dc.contributor.author | Shen, Dinggang | - |
dc.date.accessioned | 2021-09-02T22:25:41Z | - |
dc.date.available | 2021-09-02T22:25:41Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2017-12 | - |
dc.identifier.issn | 0018-9294 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/81374 | - |
dc.description.abstract | Objective: The goal of this paper is to automatically segment perivascular spaces (PVSs) in brain from high-resolution 7T magnetic resonance (MR) images. Methods: We propose a structured-learning-based segmentation framework to extract the PVSs from high-resolution 7T MR images. Specifically, we integrate three types of vascular filter responses into a structured random forest for classifying voxels into two categories, i.e., PVS and background. In addition, we propose a novel entropy-based sampling strategy to extract informative samples in the background for training an explicit classification model. Since the vascular filters can extract various vascular features, even thin and low-contrast structures can be effectively extracted from noisy backgrounds. Moreover, continuous and smooth segmentation results can be obtained by utilizing patch-based structured labels. Results: The performance of our proposed method is evaluated on 19 subjects with 7T MR images, with the Dice similarity coefficient reaching 66%. Conclusion: The joint use of entropy-based sampling strategy, vascular features, and structured learning can improve the segmentation accuracy. Significance: Instead of manual annotation, our method provides an automatic way for PVS segmentation. Moreover, our method can be potentially used for other vascular structure segmentation because of its data-driven property. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | VIRCHOW-ROBIN SPACES | - |
dc.subject | SMALL VESSEL DISEASE | - |
dc.subject | RETINAL IMAGES | - |
dc.subject | BRAIN | - |
dc.subject | OPTIMIZATION | - |
dc.subject | ROBUST | - |
dc.subject | CLASSIFICATION | - |
dc.subject | REGISTRATION | - |
dc.subject | MARKERS | - |
dc.subject | DESIGN | - |
dc.title | Structured Learning for 3-D Perivascular Space Segmentation Using Vascular Features | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shen, Dinggang | - |
dc.identifier.doi | 10.1109/TBME.2016.2638918 | - |
dc.identifier.scopusid | 2-s2.0-85040444235 | - |
dc.identifier.wosid | 000417722600006 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, v.64, no.12, pp.2803 - 2812 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | - |
dc.citation.title | IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | - |
dc.citation.volume | 64 | - |
dc.citation.number | 12 | - |
dc.citation.startPage | 2803 | - |
dc.citation.endPage | 2812 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Biomedical | - |
dc.subject.keywordPlus | VIRCHOW-ROBIN SPACES | - |
dc.subject.keywordPlus | SMALL VESSEL DISEASE | - |
dc.subject.keywordPlus | RETINAL IMAGES | - |
dc.subject.keywordPlus | BRAIN | - |
dc.subject.keywordPlus | OPTIMIZATION | - |
dc.subject.keywordPlus | ROBUST | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | REGISTRATION | - |
dc.subject.keywordPlus | MARKERS | - |
dc.subject.keywordPlus | DESIGN | - |
dc.subject.keywordAuthor | Perivascular spaces (PVSs) | - |
dc.subject.keywordAuthor | segmentation | - |
dc.subject.keywordAuthor | structured random forest (SRF) | - |
dc.subject.keywordAuthor | vascular features | - |
dc.subject.keywordAuthor | 7T magnetic resonance (MR) images | - |
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