BIRL: Benchmark on Image Registration methods with Landmark validation
This report presents a generic image registration benchmark with automatic evaluation using landmark annotations. The key features of the BIRL framework are: easily extendable, performance evaluation, parallel experimentation, simple visualisations, experiment's time-out limit, resuming unfinis...
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creator | Borovec, Jiri |
description | This report presents a generic image registration benchmark with automatic
evaluation using landmark annotations. The key features of the BIRL framework
are: easily extendable, performance evaluation, parallel experimentation,
simple visualisations, experiment's time-out limit, resuming unfinished
experiments. From the research practice, we identified and focused on these two
main use-cases: (a) comparison of user's (newly developed) method with some
State-of-the-Art (SOTA) methods on a common dataset and (b) experimenting SOTA
methods on user's custom dataset (which should contain landmark annotation).
Moreover, we present an integration of several standard image registration
methods aiming at biomedical imaging into the BIRL framework. This report also
contains experimental results of these SOTA methods on the CIMA dataset, which
is a dataset of Whole Slice Imaging (WSI) from histology/pathology containing
several multi-stain tissue samples from three tissue kinds. Source and results:
https://borda.github.io/BIRL |
doi_str_mv | 10.48550/arxiv.1912.13452 |
format | Article |
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evaluation using landmark annotations. The key features of the BIRL framework
are: easily extendable, performance evaluation, parallel experimentation,
simple visualisations, experiment's time-out limit, resuming unfinished
experiments. From the research practice, we identified and focused on these two
main use-cases: (a) comparison of user's (newly developed) method with some
State-of-the-Art (SOTA) methods on a common dataset and (b) experimenting SOTA
methods on user's custom dataset (which should contain landmark annotation).
Moreover, we present an integration of several standard image registration
methods aiming at biomedical imaging into the BIRL framework. This report also
contains experimental results of these SOTA methods on the CIMA dataset, which
is a dataset of Whole Slice Imaging (WSI) from histology/pathology containing
several multi-stain tissue samples from three tissue kinds. Source and results:
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evaluation using landmark annotations. The key features of the BIRL framework
are: easily extendable, performance evaluation, parallel experimentation,
simple visualisations, experiment's time-out limit, resuming unfinished
experiments. From the research practice, we identified and focused on these two
main use-cases: (a) comparison of user's (newly developed) method with some
State-of-the-Art (SOTA) methods on a common dataset and (b) experimenting SOTA
methods on user's custom dataset (which should contain landmark annotation).
Moreover, we present an integration of several standard image registration
methods aiming at biomedical imaging into the BIRL framework. This report also
contains experimental results of these SOTA methods on the CIMA dataset, which
is a dataset of Whole Slice Imaging (WSI) from histology/pathology containing
several multi-stain tissue samples from three tissue kinds. Source and results:
https://borda.github.io/BIRL</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><subject>Computer Science - Performance</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj81qwkAUhWfjomgfoKvOCySdn8w4ulOpbSBQEPfhztwbM2hiSYJt375p6urA4eNwPsaepEgzZ4x4ge473lK5kiqVOjPqge23-aFY8y21oW6gO_Nry_MGTsQPdIr90MEQx6qhob5iz7_iUPMCWpzYG1wiTsCCzSq49PR4zzk77l-Pu_ek-HjLd5siAbtUCbmgKkc-mBWKrKIAIqDOgCpvvVcQDFonlECHfgQkqYAWHEqnQQFaPWfP_7OTSPnZxfHHT_knVE5C-hcQPkc5</recordid><startdate>20191231</startdate><enddate>20191231</enddate><creator>Borovec, Jiri</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20191231</creationdate><title>BIRL: Benchmark on Image Registration methods with Landmark validation</title><author>Borovec, Jiri</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a672-e8c2f8ebc59d04feca0cd34aefb6bb2ac5d68020d8dbd041e2cd6a8d183a2ad63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><topic>Computer Science - Performance</topic><toplevel>online_resources</toplevel><creatorcontrib>Borovec, Jiri</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Borovec, Jiri</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>BIRL: Benchmark on Image Registration methods with Landmark validation</atitle><date>2019-12-31</date><risdate>2019</risdate><abstract>This report presents a generic image registration benchmark with automatic
evaluation using landmark annotations. The key features of the BIRL framework
are: easily extendable, performance evaluation, parallel experimentation,
simple visualisations, experiment's time-out limit, resuming unfinished
experiments. From the research practice, we identified and focused on these two
main use-cases: (a) comparison of user's (newly developed) method with some
State-of-the-Art (SOTA) methods on a common dataset and (b) experimenting SOTA
methods on user's custom dataset (which should contain landmark annotation).
Moreover, we present an integration of several standard image registration
methods aiming at biomedical imaging into the BIRL framework. This report also
contains experimental results of these SOTA methods on the CIMA dataset, which
is a dataset of Whole Slice Imaging (WSI) from histology/pathology containing
several multi-stain tissue samples from three tissue kinds. Source and results:
https://borda.github.io/BIRL</abstract><doi>10.48550/arxiv.1912.13452</doi><oa>free_for_read</oa></addata></record> |
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language | eng |
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source | arXiv.org |
subjects | Computer Science - Computer Vision and Pattern Recognition Computer Science - Performance |
title | BIRL: Benchmark on Image Registration methods with Landmark validation |
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