Adapting Mask-RCNN for Automatic Nucleus Segmentation
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Abstract (arXiv)
Automatic segmentation of microscopy images is an important task in medical image processing and analysis. Nucleus detection is an important example of this task. 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 we demonstrate 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.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 May 2018.
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Citing papers per year: 2021: 5; 2022: 1; 2023: 3; 2024: 2; 2025: 1.
12 papers.
- 2021Circle Representation for Medical Object Detectioncited by 15
- 2023ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for Cell Instance Segmentationcited by 11
- 2021PECNet: A Deep Multi-Label Segmentation Network for Eosinophilic Esophagitis Biopsy Diagnosticscited by 5
- 2021Automatic Volumetric Segmentation of Additive Manufacturing Defects with 3D U-Netcited by 4
- 2022Exploring Effective Knowledge Transfer for Few-shot Object Detectioncited by 3
- 2024Automatic characterization of boulders on planetary surfaces from high-resolution satellite imagescited by 2
- 2024Segmentation and Characterization of Macerated Fibers and Vessels Using Deep Learningcited by 1
- 2021MemX: An Attention-Aware Smart Eyewear System for Personalized Moment Auto-capturecited by 0
- 2021Transformer Assisted Convolutional Network for Cell Instance Segmentationcited by 0
- 2023Advanced Multi-Microscopic Views Cell Semi-supervised Segmentationcited by 0
- 2023Improving Generalization Capability of Deep Learning-Based Nuclei Instance Segmentation by Non-deterministic Train Time and Deterministic Test Time Stain Normalizationcited by 0
- 2025Weak-to-Strong Generalization Enables Fully Automated De Novo Training of Multi-head Mask-RCNN Model for Segmenting Densely Overlapping Cell Nuclei in Multiplex Whole-slice Brain Imagescited by 0
- 2025Weak-to-Strong Generalization Enables Fully Automated De Novo Training of Multi-head Mask-RCNN Model for Segmenting Densely Overlapping Cell Nuclei in Multiplex Whole-slice Brain Images12 Dec 2025
- 2024Segmentation and Characterization of Macerated Fibers and Vessels Using Deep Learning30 Jan 2024
- 2024Automatic characterization of boulders on planetary surfaces from high-resolution satellite images15 Jan 2024
- 2023ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for Cell Instance Segmentation11 Dec 2023
- 2023Improving Generalization Capability of Deep Learning-Based Nuclei Instance Segmentation by Non-deterministic Train Time and Deterministic Test Time Stain Normalization12 Sep 2023
- 2023Advanced Multi-Microscopic Views Cell Semi-supervised Segmentation21 Mar 2023
- 2022Exploring Effective Knowledge Transfer for Few-shot Object Detection5 Oct 2022
- 2021Circle Representation for Medical Object Detection22 Oct 2021
- 2021Transformer Assisted Convolutional Network for Cell Instance Segmentation5 Oct 2021
- 2021MemX: An Attention-Aware Smart Eyewear System for Personalized Moment Auto-capture3 May 2021
- 2021PECNet: A Deep Multi-Label Segmentation Network for Eosinophilic Esophagitis Biopsy Diagnostics2 Mar 2021
- 2021Automatic Volumetric Segmentation of Additive Manufacturing Defects with 3D U-Net22 Jan 2021