High Throughput Deep Model of 3D Nucleus Instance Segmentation by Stereo Stitching Contextual Gaps-Reference-Cited by-同舟云学术

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High Throughput Deep Model of 3D Nucleus Instance Segmentation by Stereo Stitching Contextual Gaps

Published:2023-04-18 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)
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Author:

Wei Ziquan¹,Dan Tingting¹,Ding Jiaqi²,McCormick Carolyn³,Kyere Felix A.³,Kim Minjeong⁴,Borland David⁵,Stein Jason L.³,Wu Guorong¹

Affiliation:

1. University of North Carolina at Chapel Hill,Departments of Psychiatry,Chapel Hill,NC,USA

2. University of North Carolina at Chapel Hill,Computer Science,Chapel Hill,NC,USA

3. University of North Carolina at Chapel Hill,Genetics,Chapel Hill,NC,USA

4. University of North Carolina at Greensboro,Department of Computer Science,Greensboro,NC,USA

5. University of North Carolina at Chapel Hill,RENCI,Chapel Hill,NC,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10230311/10230322/10230745.pdf?arnumber=10230745

Reference11 articles.

1. Segmentor: a tool for manual refinement of 3D microscopy annotations

2. Relational Message Passing for Knowledge Graph Completion

3. Image segmentation using deep learning: A survey;minaee;IEEE Transactions on Pattern Analysis and Machine Intelligence,2021

4. End-to-End Video Instance Segmentation via Spatial-Temporal Graph Neural Networks

5. Brain tumor segmentation using 3D Mask R-CNN for dynamic susceptibility contrast enhanced perfusion imaging

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