Dataset Information

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

ABSTRACT: A principal challenge in the analysis of tissue imaging data is cell segmentation-the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets. We used TissueNet to train Mesmer, a deep-learning-enabled segmentation algorithm. We demonstrated that Mesmer is more accurate than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We then adapted Mesmer to harness cell lineage information in highly multiplexed datasets and used this enhanced version to quantify cell morphology changes during human gestation. All code, data and models are released as a community resource.

SUBMITTER: Greenwald NF

PROVIDER: S-EPMC9010346 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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

Greenwald Noah F NF Miller Geneva G Moen Erick E Kong Alex A Kagel Adam A Dougherty Thomas T Fullaway Christine Camacho CC McIntosh Brianna J BJ Leow Ke Xuan KX Schwartz Morgan Sarah MS Pavelchek Cole C Cui Sunny S Camplisson Isabella I Bar-Tal Omer O Singh Jaiveer J Fong Mara M Chaudhry Gautam G Abraham Zion Z Moseley Jackson J Warshawsky Shiri S Soon Erin E Greenbaum Shirley S Risom Tyler T Hollmann Travis T Bendall Sean C SC Keren Leeat L Graf William W Angelo Michael M Van Valen David D

Nature biotechnology 20211118 4

A principal challenge in the analysis of tissue imaging data is cell segmentation-the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets. We used TissueNet to train Mesmer, a deep-learning-enabled segmentation algorithm. We demonstrated that Me ...[more]

PMID: 34795433

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Project description:PurposeTraining deep neural networks usually require a large number of human-annotated data. For organ segmentation from volumetric medical images, human annotation is tedious and inefficient. To save human labour and to accelerate the training process, the strategy of annotation by iterative deep learning recently becomes popular in the research community. However, due to the lack of domain knowledge or efficient human-interaction tools, the current AID methods still suffer from long training time and high annotation burden.MethodsWe develop a contour-based annotation by iterative deep learning (AID) algorithm which uses boundary representation instead of voxel labels to incorporate high-level organ shape knowledge. We propose a contour segmentation network with a multi-scale feature extraction backbone to improve the boundary detection accuracy. We also developed a contour-based human-intervention method to facilitate easy adjustments of organ boundaries. By combining the contour-based segmentation network and the contour-adjustment intervention method, our algorithm achieves fast few-shot learning and efficient human proofreading.ResultsFor validation, two human operators independently annotated four abdominal organs in computed tomography (CT) images using our method and two compared methods, i.e. a traditional contour-interpolation method and a state-of-the-art (SOTA) convolutional network (CNN) method based on voxel label representation. Compared to these methods, our approach considerably saved annotation time and reduced inter-rater variabilities. Our contour detection network also outperforms the SOTA nnU-Net in producing anatomically plausible organ shape with only a small training set.ConclusionTaking advantage of the boundary shape prior and the contour representation, our method is more efficient, more accurate and less prone to inter-operator variability than the SOTA AID methods for organ segmentation from volumetric medical images. The good shape learning ability and flexible boundary adjustment function make it suitable for fast annotation of organ structures with regular shape.

Dataset Information

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

Publications

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

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