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?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.15350
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.15350
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 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
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2306.15350
- https://arxiv.org/pdf/2306.15350
- OA Status
- green
- Cited By
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4382491502
All OpenAlex metadata
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https://openalex.org/W4382491502Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2306.15350Digital Object Identifier
- Title
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CellViT: Vision Transformers for Precise Cell Segmentation and ClassificationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-06-27Full 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
- Landing page
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https://arxiv.org/abs/2306.15350Publisher landing page
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https://arxiv.org/pdf/2306.15350Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2306.15350Direct OA link when available
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Segmentation, Computer science, Artificial intelligence, Convolutional neural network, Deep learning, Encoder, Pattern recognition (psychology), Cluster analysis, Computer vision, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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18Total citation count in OpenAlex
- Citations by year (recent)
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2025: 9, 2024: 8, 2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.publicly | 185 |
| abstract_inverted_index.recently | 141 |
| abstract_inverted_index.staining | 34 |
| abstract_inverted_index.(H&E) | 8 |
| abstract_inverted_index.achieving | 157 |
| abstract_inverted_index.annotated | 115 |
| abstract_inverted_index.automated | 70 |
| abstract_inverted_index.available | 186 |
| abstract_inverted_index.datasets, | 110 |
| abstract_inverted_index.detection | 1, 160 |
| abstract_inverted_index.digitized | 77 |
| abstract_inverted_index.evaluated | 95 |
| abstract_inverted_index.important | 12, 120 |
| abstract_inverted_index.in-domain | 132 |
| abstract_inverted_index.introduce | 65 |
| abstract_inverted_index.potential | 56 |
| abstract_inverted_index.published | 142 |
| abstract_inverted_index.variances | 32 |
| abstract_inverted_index.Therefore, | 63 |
| abstract_inverted_index.clinically | 119 |
| abstract_inverted_index.consisting | 111 |
| abstract_inverted_index.leveraging | 139 |
| abstract_inverted_index.Transformer | 88 |
| abstract_inverted_index.ViT-encoder | 148 |
| abstract_inverted_index.boundaries, | 38 |
| abstract_inverted_index.challenging | 27, 106 |
| abstract_inverted_index.clustering. | 41 |
| abstract_inverted_index.demonstrate | 127 |
| abstract_inverted_index.extensively | 48 |
| abstract_inverted_index.hematoxylin | 5 |
| abstract_inverted_index.large-scale | 131 |
| abstract_inverted_index.overlapping | 37 |
| abstract_inverted_index.performance | 164 |
| abstract_inverted_index.pre-trained | 135, 149 |
| abstract_inverted_index.superiority | 129 |
| abstract_inverted_index.F1-detection | 178 |
| abstract_inverted_index.Transformers | 137 |
| abstract_inverted_index.architecture | 84 |
| abstract_inverted_index.histological | 153 |
| abstract_inverted_index.segmentation | 3, 72, 109, 163 |
| abstract_inverted_index.applications. | 22 |
| abstract_inverted_index.convolutional | 43 |
| abstract_inverted_index.eosin-stained | 7 |
| abstract_inverted_index.out-of-domain | 134 |
| abstract_inverted_index.state-of-the-art | 158 |
| abstract_inverted_index.Transformer-based | 58 |
| abstract_inverted_index.https://github.com/TIO-IKIM/CellViT | 188 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 11 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.44999998807907104 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |