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- Publisher Website: 10.1109/BIBM49941.2020.9313458
- Scopus: eid_2-s2.0-85100331483
- WOS: WOS:000659487102062
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Conference Paper: ChromSeg: Two-Stage Framework for Overlapping Chromosome Segmentation and Reconstruction
Title | ChromSeg: Two-Stage Framework for Overlapping Chromosome Segmentation and Reconstruction |
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Authors | |
Issue Date | 2020 |
Publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001586 |
Citation | Proceedings of IEEE International Conference on Bioinformatics and Biomedicine 2020 (IEEE BIBM 2020), Virtual Conference, Seoul, Korea, 16-19 December 2020, p. 2335-2342 How to Cite? |
Abstract | Karyotyping is the most commonly used genetic tool for diagnosing diseases associated with chromosomal abnormalities. It generates images of the chromosomes of a patient in which quantity or shape discrepancies against normal chromosomes might suggest chromosomal abnormalities. However, the current methods are cumbersome and require manual or half-automatic separation of overlapping chromosomes, significantly limiting the productivity of clinical geneticists and cytologists. In this project, we implemented a fully automatic method, called ChromSeg, which efficiently separates crossing-overlap chromosomes. It uses a new neural network architecture called “region-guided UNet++” to accurately detect crossing-overlap chromosomes from metaphase cell images. A new heuristic algorithm, called “crossing-partition”, is then applied to splice and reconstruct the crossing-overlap chromosomes into single chromosomes. While there are a very limited number of publicly accessible annotations on overlapping chromosomes, we manually annotated 345 images for our model training and performance testing. Benchmarking results showed that our method achieved 99.1% overlap detection on crossing-overlap chromosomes and outperformed the second best method by 3.1%. Notably, this is the first tool to provide an image of the reconstructed chromosomes; other tools provide only segmentation suggestions, which are of less value to end-users. The source code of ChromSeg is available at https://github.com/HKU-BAL/ChromSeg, and the 345 anno |
Persistent Identifier | http://hdl.handle.net/10722/301147 |
ISBN | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Cao, X | - |
dc.contributor.author | Lan, F | - |
dc.contributor.author | Liu, CM | - |
dc.contributor.author | Lam, TW | - |
dc.contributor.author | Luo, R | - |
dc.date.accessioned | 2021-07-27T08:06:49Z | - |
dc.date.available | 2021-07-27T08:06:49Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Proceedings of IEEE International Conference on Bioinformatics and Biomedicine 2020 (IEEE BIBM 2020), Virtual Conference, Seoul, Korea, 16-19 December 2020, p. 2335-2342 | - |
dc.identifier.isbn | 9781728162164 | - |
dc.identifier.uri | http://hdl.handle.net/10722/301147 | - |
dc.description.abstract | Karyotyping is the most commonly used genetic tool for diagnosing diseases associated with chromosomal abnormalities. It generates images of the chromosomes of a patient in which quantity or shape discrepancies against normal chromosomes might suggest chromosomal abnormalities. However, the current methods are cumbersome and require manual or half-automatic separation of overlapping chromosomes, significantly limiting the productivity of clinical geneticists and cytologists. In this project, we implemented a fully automatic method, called ChromSeg, which efficiently separates crossing-overlap chromosomes. It uses a new neural network architecture called “region-guided UNet++” to accurately detect crossing-overlap chromosomes from metaphase cell images. A new heuristic algorithm, called “crossing-partition”, is then applied to splice and reconstruct the crossing-overlap chromosomes into single chromosomes. While there are a very limited number of publicly accessible annotations on overlapping chromosomes, we manually annotated 345 images for our model training and performance testing. Benchmarking results showed that our method achieved 99.1% overlap detection on crossing-overlap chromosomes and outperformed the second best method by 3.1%. Notably, this is the first tool to provide an image of the reconstructed chromosomes; other tools provide only segmentation suggestions, which are of less value to end-users. The source code of ChromSeg is available at https://github.com/HKU-BAL/ChromSeg, and the 345 anno | - |
dc.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001586 | - |
dc.relation.ispartof | IEEE International Conference on Bioinformatics and Biomedicine Proceedings | - |
dc.rights | IEEE International Conference on Bioinformatics and Biomedicine Proceedings. Copyright © IEEE. | - |
dc.rights | ©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.title | ChromSeg: Two-Stage Framework for Overlapping Chromosome Segmentation and Reconstruction | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Liu, CM: imcx@HKUCC-COM.hku.hk | - |
dc.identifier.email | Lam, TW: twlam@cs.hku.hk | - |
dc.identifier.email | Luo, R: rbluo@cs.hku.hk | - |
dc.identifier.authority | Lam, TW=rp00135 | - |
dc.identifier.authority | Luo, R=rp02360 | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1109/BIBM49941.2020.9313458 | - |
dc.identifier.scopus | eid_2-s2.0-85100331483 | - |
dc.identifier.hkuros | 323499 | - |
dc.identifier.spage | 2335 | - |
dc.identifier.epage | 2342 | - |
dc.identifier.isi | WOS:000659487102062 | - |
dc.publisher.place | United States | - |