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Automatic Nucleus Segmentation with Mask-RCNN

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Advances in Computer Vision (CVC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 944))

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Abstract

Mask-RCNN is a recently proposed state-of-the-art algorithm for object detection, object localization, and object instance segmentation of natural images. In this paper, it is demonstrated that Mask-RCNN can be used to perform highly effective and efficient automatic segmentations of a wide range of microscopy images of cell nuclei, for a variety of cells acquired under a variety of conditions. In addition, it is shown that a cyclic learning rate regime allows effective training of a Mask-RCNN model without any need to finetune the learning rate, thereby eliminating a manual and time-consuming aspect of the training procedure. The results presented here will be of interest to those in the medical imaging field and to computer vision researchers more generally.

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Acknowledgments

The author would like to thank NVIDIA Corp. for GPU donation to support this research.

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Authors and Affiliations

  1. University of New Hampshire, Manchester, NH, 03101, USA

    Jeremiah W. Johnson

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  1. Jeremiah W. Johnson
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Correspondence to Jeremiah W. Johnson .

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Editors and Affiliations

  1. Saga University, Saga, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Johnson, J.W. (2020). Automatic Nucleus Segmentation with Mask-RCNN. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-17798-0_32

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