CellViT: Vision Transformers for precise cell segmentation and classification Article Swipe
Fabian Hörst
,
Moritz Rempe
,
Lukas Heine
,
Constantin Seibold
,
Julius Keyl
,
Giulia Baldini
,
Selma Ugurel
,
Jens T. Siveke
,
Barbara T. Grünwald
,
Jan Egger
,
Jens Kleesiek
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.media.2024.103143
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.media.2024.103143
Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications. However, it is a challenging task due to nuclei variances in staining and size, overlapping boundaries, and nuclei clustering. While convolutional neural networks have been extensively used for this task, we explore the potential of Transformer-based networks in combination with large scale pre-training in this domain. Therefore, we introduce a new method for automated instance segmentation of cell nuclei in digitized tissue samples using a deep learning architecture based on Vision Transformer called CellViT. CellViT is trained and evaluated on the PanNuke dataset, which is one of the most challenging nuclei instance segmentation datasets, consisting of nearly 200,000 annotated nuclei into 5 clinically important classes in 19 tissue types. We demonstrate the superiority of large-scale in-domain and out-of-domain pre-trained Vision Transformers by leveraging the recently published Segment Anything Model and a ViT-encoder pre-trained on 104 million histological image patches - achieving state-of-the-art nuclei detection and instance segmentation performance on the PanNuke dataset with a mean panoptic quality of 0.50 and an F1-detection score of 0.83. The code is publicly available at https://github.com/TIO-IKIM/CellViT.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.media.2024.103143
- OA Status
- hybrid
- Cited By
- 172
- References
- 85
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4392883923Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.media.2024.103143Digital Object Identifier
- Title
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CellViT: Vision Transformers for precise cell segmentation and classificationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-03-16Full publication date if available
- Authors
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Fabian Hörst, Moritz Rempe, Lukas Heine, Constantin Seibold, Julius Keyl, Giulia Baldini, Selma Ugurel, Jens T. Siveke, Barbara T. Grünwald, Jan Egger, Jens KleesiekList of authors in order
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https://doi.org/10.1016/j.media.2024.103143Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.media.2024.103143Direct OA link when available
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Computer science, Segmentation, Artificial intelligence, Convolutional neural network, Deep learning, Encoder, Pattern recognition (psychology), Cluster analysis, Computer vision, Scale-space segmentation, Image segmentation, Operating systemTop concepts (fields/topics) attached by OpenAlex
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172Total citation count in OpenAlex
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2025: 125, 2024: 44, 2023: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.While | 42 |
| abstract_inverted_index.based | 91 |
| abstract_inverted_index.image | 160 |
| abstract_inverted_index.large | 63 |
| abstract_inverted_index.range | 20 |
| abstract_inverted_index.scale | 64 |
| abstract_inverted_index.score | 185 |
| abstract_inverted_index.size, | 36 |
| abstract_inverted_index.task, | 52 |
| abstract_inverted_index.tasks | 14 |
| abstract_inverted_index.using | 86 |
| abstract_inverted_index.which | 106 |
| abstract_inverted_index.Nuclei | 0 |
| abstract_inverted_index.Vision | 93, 142 |
| abstract_inverted_index.called | 95 |
| abstract_inverted_index.images | 10 |
| abstract_inverted_index.method | 74 |
| abstract_inverted_index.nearly | 119 |
| abstract_inverted_index.neural | 44 |
| abstract_inverted_index.nuclei | 31, 40, 81, 113, 122, 165 |
| abstract_inverted_index.tissue | 9, 84, 130 |
| abstract_inverted_index.types. | 131 |
| abstract_inverted_index.200,000 | 120 |
| abstract_inverted_index.CellViT | 97 |
| abstract_inverted_index.PanNuke | 104, 173 |
| abstract_inverted_index.Segment | 149 |
| abstract_inverted_index.classes | 127 |
| abstract_inverted_index.crucial | 16 |
| abstract_inverted_index.dataset | 174 |
| abstract_inverted_index.domain. | 68 |
| abstract_inverted_index.explore | 54 |
| abstract_inverted_index.million | 158 |
| abstract_inverted_index.patches | 161 |
| abstract_inverted_index.quality | 179 |
| abstract_inverted_index.samples | 85 |
| abstract_inverted_index.trained | 99 |
| abstract_inverted_index.Anything | 150 |
| abstract_inverted_index.CellViT. | 96 |
| abstract_inverted_index.However, | 23 |
| abstract_inverted_index.clinical | 13 |
| abstract_inverted_index.dataset, | 105 |
| abstract_inverted_index.instance | 77, 114, 168 |
| abstract_inverted_index.learning | 89 |
| abstract_inverted_index.networks | 45, 59 |
| abstract_inverted_index.panoptic | 178 |
| abstract_inverted_index.publicly | 191 |
| abstract_inverted_index.recently | 147 |
| abstract_inverted_index.staining | 34 |
| abstract_inverted_index.achieving | 163 |
| abstract_inverted_index.annotated | 121 |
| abstract_inverted_index.automated | 76 |
| abstract_inverted_index.available | 192 |
| abstract_inverted_index.datasets, | 116 |
| abstract_inverted_index.detection | 1, 166 |
| abstract_inverted_index.digitized | 83 |
| abstract_inverted_index.evaluated | 101 |
| abstract_inverted_index.important | 12, 126 |
| abstract_inverted_index.in-domain | 138 |
| abstract_inverted_index.introduce | 71 |
| abstract_inverted_index.potential | 56 |
| abstract_inverted_index.published | 148 |
| abstract_inverted_index.variances | 32 |
| abstract_inverted_index.Therefore, | 69 |
| abstract_inverted_index.clinically | 125 |
| abstract_inverted_index.consisting | 117 |
| abstract_inverted_index.leveraging | 145 |
| abstract_inverted_index.Transformer | 94 |
| abstract_inverted_index.ViT-encoder | 154 |
| abstract_inverted_index.boundaries, | 38 |
| abstract_inverted_index.challenging | 27, 112 |
| abstract_inverted_index.clustering. | 41 |
| abstract_inverted_index.combination | 61 |
| abstract_inverted_index.demonstrate | 133 |
| abstract_inverted_index.extensively | 48 |
| abstract_inverted_index.hematoxylin | 5 |
| abstract_inverted_index.large-scale | 137 |
| abstract_inverted_index.overlapping | 37 |
| abstract_inverted_index.performance | 170 |
| abstract_inverted_index.pre-trained | 141, 155 |
| abstract_inverted_index.superiority | 135 |
| abstract_inverted_index.Transformers | 143 |
| abstract_inverted_index.architecture | 90 |
| abstract_inverted_index.histological | 159 |
| abstract_inverted_index.pre-training | 65 |
| abstract_inverted_index.segmentation | 3, 78, 115, 169 |
| abstract_inverted_index.applications. | 22 |
| abstract_inverted_index.convolutional | 43 |
| abstract_inverted_index.eosin-stained | 7 |
| abstract_inverted_index.out-of-domain | 140 |
| abstract_inverted_index.state-of-the-art | 164 |
| abstract_inverted_index.Transformer-based | 58 |
| abstract_inverted_index.F<sub>1</sub>-detection | 184 |
| abstract_inverted_index.https://github.com/TIO-IKIM/CellViT. | 194 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5017950391 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 11 |
| corresponding_institution_ids | https://openalex.org/I4210119759 |
| citation_normalized_percentile.value | 0.99954102 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |