PanNuke: An Open Pan-Cancer Histology Dataset for Nuclei Instance Segmentation and Classification

Gamper, Jevgenij; Koohbanani, Navid Alemi; Benet, Ksenija; Khuram, Ali; Rajpoot, Nasir M.

doi:10.1007/978-3-030-23937-4_2

Cited by 161 publications

(117 citation statements)

References 16 publications

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“…To justify the effectiveness of CPP-Net, we conduct extensive experiments on three publicly available datasets, i.e., DSB2018 [1], BBBC006 [33], and PanNuke [34].…”

Section: Methodsmentioning

confidence: 99%

“…According to the metric, the average precision (AP) with IoU thresholds ranging from 0.5 to 0.9 with a step size of 0.05 are computed. For the PanNuke database, we adopt the Panoptic Quality (PQ) presented in [34] as the evaluation metric. PQ has been widely adopted in panoptic segmentation tasks and was introduced into nucleus segmentation in [4].…”

Section: Evaluation Metricsmentioning

confidence: 99%

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Boundary-Assisted Region Proposal Networks for Nucleus Segmentation

Chen

Ding

Tao

2020

Lecture Notes in Computer Science

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Nucleus segmentation is a challenging task due to the crowded distribution and blurry boundaries of nuclei. Recent approaches represent nuclei by means of polygons to differentiate between touching and overlapping nuclei and have accordingly achieved promising performance. Each polygon is represented by a set of centroid-toboundary distances, which are in turn predicted by features of the centroid pixel for a single nucleus. However, using the centroid pixel alone does not provide sufficient contextual information for robust prediction. To handle this problem, we propose a Context-aware Polygon Proposal Network (CPP-Net) for nucleus segmentation. First, we sample a point set rather than one single pixel within each cell for distance prediction. This strategy substantially enhances contextual information and thereby improves the robustness of the prediction. Second, we propose a Confidencebased Weighting Module, which adaptively fuses the predictions from the sampled point set. Third, we introduce a novel Shape-Aware Perceptual (SAP) loss that constrains the shape of the predicted polygons. Here, the SAP loss is based on an additional network that is pre-trained by means of mapping the centroid probability map and the pixel-toboundary distance maps to a different nucleus representation. Extensive experiments justify the effectiveness of each component in the proposed CPP-Net. Finally, CPP-Net is found to achieve state-of-the-art performance on three publicly available databases, namely DSB2018, BBBC06, and PanNuke. Code of this paper will be released.

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“…To justify the effectiveness of CPP-Net, we conduct extensive experiments on three publicly available datasets, i.e., DSB2018 [1], BBBC006 [33], and PanNuke [34].…”

Section: Methodsmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Boundary-Assisted Region Proposal Networks for Nucleus Segmentation

Chen

Ding

Tao

2020

Lecture Notes in Computer Science

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“…Existing annotated datasets for cell segmentation are limited in scope and scale ( Figure 1b) 26,27,[32][33][34][35][36][37][38] . This limitation is largely due to the linear, time-intensive approach used to construct them, which requires the border of every cell in an image to be manually demarcated.…”

Section: A Human-in-the-loop Approach Drives Scalable Construction Ofmentioning

confidence: 99%

“…Generating ground-truth data for cell segmentation is time intensive due to the need to generate pixel-level labels; as a result, existing datasets are of modest size (10 4 -10 5 annotations). Moreover, most public datasets 26,27,[32][33][34][35][36][37][38] annotate the location of cell nuclei rather than the whole cell. Deploying pre-trained models to the life science community is also difficult, and has been the focus of a number of recent works [39][40][41][42] .…”

Section: Introductionmentioning

confidence: 99%

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Greenwald

Miller

Moen

et al. 2021

Preprint

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Understanding the spatial organization of tissues is of critical importance for both basic and translational research. While recent advances in tissue imaging are opening an exciting new window into the biology of human tissues, interpreting the data that they create is a significant computational challenge. Cell segmentation, the task of uniquely identifying each cell in an image, remains a substantial barrier for tissue imaging, as existing approaches are inaccurate or require a substantial amount of manual curation to yield useful results. Here, we addressed the problem of cell segmentation in tissue imaging data through large-scale data annotation and deep learning. We constructed TissueNet, an image dataset containing >1 million paired whole-cell and nuclear annotations for tissue images from nine organs and six imaging platforms. We created Mesmer, a deep learning-enabled segmentation algorithm trained on TissueNet that performs nuclear and whole-cell segmentation in tissue imaging data. We demonstrated that Mesmer has better speed and accuracy than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance for whole-cell segmentation. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We further showed that Mesmer could be adapted to harness cell lineage information present in highly multiplexed datasets. We used this enhanced version to quantify cell morphology changes during human gestation. All underlying code and models are released with permissive licenses as a community resource.

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“…A subset of 209 WSIs have detailed hand-drawn contours for all metastases. The PanNuke dataset [https://jgamper.github.io/PanNukeDataset/, (17)] contains 216.4K labeled nuclei from more than 20K WSI at different magnifications. The ANHIR challenge (https://anhir.grand-challenge.org/) for Automatic Non-rigid Histological Image Registration presents hundreds of different types of histopathology tissue (lesions, lung lobes, mammary gland) stained with different dyes and where landmarks have been manually annotated to assess image registration performances.…”

Section: Open Practices and Resourcesmentioning

confidence: 99%

Open Practices and Resources for Collaborative Digital Pathology

Marée

2019

Front. Med.

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In this paper, we describe open practices and open resources in the field of digital pathology with a specific focus on approaches that ease collaboration in research and education settings. Our review includes open access journals and open peer review, open-source software (libraries, desktop tools, and web applications), and open access collections. We illustrate applications and discuss current limitations and perspectives.

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PanNuke: An Open Pan-Cancer Histology Dataset for Nuclei Instance Segmentation and Classification

Cited by 161 publications

References 16 publications

Boundary-Assisted Region Proposal Networks for Nucleus Segmentation

Boundary-Assisted Region Proposal Networks for Nucleus Segmentation

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Open Practices and Resources for Collaborative Digital Pathology

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