مورد إلكتروني
A robust approach to 3D neuron shape representation for quantification and classification.
| العنوان: | A robust approach to 3D neuron shape representation for quantification and classification. |
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| المؤلفون: | Jiang, Jiaxiang |
| المصدر: | BMC Bioinformatics; vol 24, iss 1 |
| بيانات النشر: | eScholarship, University of California 2023-09-28 |
| تفاصيل مُضافة: | Jiang, Jiaxiang Goebel, Michael Borba, Cezar Smith, William Manjunath, B |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on neuron shapes. In neuroscience research, the skeleton representation is often used as a compact and abstract representation of neuron shapes. However, existing methods are limited to getting and analyzing curve skeletons which can only be applied for tubular shapes. This paper presents a 3D neuron morphology analysis method for more general and complex neuron shapes. First, we introduce the concept of skeleton mesh to represent general neuron shapes and propose a novel method for computing mesh representations from 3D surface point clouds. A skeleton graph is then obtained from skeleton mesh and is used to extract sub-cellular features. Finally, an unsupervised learning method is used to embed the skeleton graph for neuron classification. Extensive experiment results are provided and demonstrate the robustness of our method to analyze neuron morphology. |
| مصطلحات الفهرس: | 3D neuron morphology, Classification, Embedding, Graph, Skeleton mesh, Sub-cellular features, article |
| URL: | |
| الإتاحة: | Open access content. Open access content public |
| ملاحظة: | application/pdf BMC Bioinformatics vol 24, iss 1 |
| أرقام أخرى: | CDLER oai:escholarship.org:ark:/13030/qt8dz2x3kk qt8dz2x3kk https://escholarship.org/uc/item/8dz2x3kkTest https://escholarship.orgTest/ 1410329301 |
| المصدر المساهم: | UC MASS DIGITIZATION From OAIster®, provided by the OCLC Cooperative. |
| رقم الانضمام: | edsoai.on1410329301 |
| قاعدة البيانات: | OAIster |
| الوصف غير متاح. |