mlsanityData to 7 Oct 2026

Mask-RCNN and U-net Ensembled for Nuclei Segmentation

arXiv 1901.10170 ↗First posted 29 Jan 2019cs.CVcited by 23

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Abstract (arXiv)

Nuclei segmentation is both an important and in some ways ideal task for modern computer vision methods, e.g. convolutional neural networks. While recent developments in theory and open-source software have made these tools easier to implement, expert knowledge is still required to choose the right model architecture and training setup. We compare two popular segmentation frameworks, U-Net and Mask-RCNN in the nuclei segmentation task and find that they have different strengths and failures. To get the best of both worlds, we develop an ensemble model to combine their predictions that can outperform both models by a significant margin and should be considered when aiming for best nuclei segmentation performance.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jan 2019.

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Citing papers per year: 2021: 11; 2022: 4; 2023: 3; 2024: 2; 2025: 2; 2026: 1.

23 papers.

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  1. 2021CPP-Net: Context-aware Polygon Proposal Network for Nucleus Segmentationcited by 32
  2. 2021FEDZIP: A Compression Framework for Communication-Efficient Federated Learningcited by 27
  3. 2021Modality specific U-Net variants for biomedical image segmentation: A surveycited by 20
  4. 2021Cognitive Visual Commonsense Reasoning Using Dynamic Working Memorycited by 14
  5. 2021Interpretable Visual Understanding with Cognitive Attention Networkcited by 12
  6. 2021Multi-scale Regional Attention Deeplab3+: Multiple Myeloma Plasma Cells Segmentation in Microscopic Imagescited by 11
  7. 2023Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentationcited by 5
  8. 2022ConvNeXt-backbone HoVerNet for nuclei segmentation and classificationcited by 3
  9. 2022Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Reviewcited by 3
  10. 2021Machine Learning-based Automatic Graphene Detection with Color Correction for Optical Microscope Imagescited by 2
  11. 2023Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)cited by 2
  12. 2021Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopycited by 1
11 more
  1. 2022YOLO2U-Net: Detection-Guided 3D Instance Segmentation for Microscopycited by 1
  2. 2024ShapeICP: Iterative Category-level Object Pose and Shape Estimation from Depthcited by 1
  3. 2025AMA-SAM: Adversarial Multi-Domain Alignment of Segment Anything Model for High-Fidelity Histology Nuclei Segmentationcited by 1
  4. 2021Instance segmentation with the number of clusters incorporated in embedding learningcited by 0
  5. 2021COVID-19 Lung Lesion Segmentation Using a Sparsely Supervised Mask R-CNN on Chest X-rays Automatically Computed from Volumetric CTscited by 0
  6. 2021Semantic Dense Reconstruction with Consistent Scene Segmentscited by 0
  7. 2022Attention Mechanism based Cognition-level Scene Understandingcited by 0
  8. 2023License Plate Recognition Based On Multi-Angle View Modelcited by 0
  9. 2024Multimodal Laryngoscopic Video Analysis for Assisted Diagnosis of Vocal Fold Paralysiscited by 0
  10. 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
  11. 2026LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole-Slide Imagescited by 0