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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2408.15954 |
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| _version_ | 1866914927945449472 |
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| author | Goldsborough, Thibaut Philps, Ben O'Callaghan, Alan Inglis, Fiona Leplat, Leo Filby, Andrew Bilen, Hakan Bankhead, Peter |
| author_facet | Goldsborough, Thibaut Philps, Ben O'Callaghan, Alan Inglis, Fiona Leplat, Leo Filby, Andrew Bilen, Hakan Bankhead, Peter |
| contents | Cell and nucleus segmentation are fundamental tasks for quantitative bioimage analysis. Despite progress in recent years, biologists and other domain experts still require novel algorithms to handle increasingly large and complex real-world datasets. These algorithms must not only achieve state-of-the-art accuracy, but also be optimized for efficiency, portability and user-friendliness. Here, we introduce InstanSeg: a novel embedding-based instance segmentation pipeline designed to identify cells and nuclei in microscopy images. Using six public cell segmentation datasets, we demonstrate that InstanSeg can significantly improve accuracy when compared to the most widely used alternative methods, while reducing the processing time by at least 60%. Furthermore, InstanSeg is designed to be fully serializable as TorchScript and supports GPU acceleration on a range of hardware. We provide an open-source implementation of InstanSeg in Python, in addition to a user-friendly, interactive QuPath extension for inference written in Java. Our code and pre-trained models are available at https://github.com/instanseg/instanseg . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_15954 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | InstanSeg: an embedding-based instance segmentation algorithm optimized for accurate, efficient and portable cell segmentation Goldsborough, Thibaut Philps, Ben O'Callaghan, Alan Inglis, Fiona Leplat, Leo Filby, Andrew Bilen, Hakan Bankhead, Peter Computer Vision and Pattern Recognition Cell and nucleus segmentation are fundamental tasks for quantitative bioimage analysis. Despite progress in recent years, biologists and other domain experts still require novel algorithms to handle increasingly large and complex real-world datasets. These algorithms must not only achieve state-of-the-art accuracy, but also be optimized for efficiency, portability and user-friendliness. Here, we introduce InstanSeg: a novel embedding-based instance segmentation pipeline designed to identify cells and nuclei in microscopy images. Using six public cell segmentation datasets, we demonstrate that InstanSeg can significantly improve accuracy when compared to the most widely used alternative methods, while reducing the processing time by at least 60%. Furthermore, InstanSeg is designed to be fully serializable as TorchScript and supports GPU acceleration on a range of hardware. We provide an open-source implementation of InstanSeg in Python, in addition to a user-friendly, interactive QuPath extension for inference written in Java. Our code and pre-trained models are available at https://github.com/instanseg/instanseg . |
| title | InstanSeg: an embedding-based instance segmentation algorithm optimized for accurate, efficient and portable cell segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.15954 |