ArticleIEEE transactions on medical imaging2020
A Multi-Organ Nucleus Segmentation Challenge.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 papers, 1 of them a synthesis that pooled it.
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Who cites it
116 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancements in automated nuclei segmentation for histopathology using you only look once-driven approaches: A systematic review.Computers in biology and medicine · 2025Pooled it
- Spinal morphology-based multimodal AI for predicting pulmonary dysfunction in adolescent idiopathic scoliosis.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- TiLCA-Net: nuclei segmentation and classification in pathological images via task-specialized attention and efficient decoding.Quantitative imaging in medicine and surgery · 2026Article
- A Meta-Analysis of Deep-Learning-Based Fusion of Positron Emission Tomography and Computed Tomography in Oncologic Imaging.Journal of imaging informatics in medicine · 2026Review
- Neural Network-Based System for Rapid Diagnostic Decision Support Using FLIM Data: Segmentation and Classification in Liver, Skin, and Pancreatic Tissues.International journal of molecular sciences · 2026Article
- BasNet: Attention U-Net-Based Automated Axon Segmentation in Bielschowsky Silver-Stained Histology.Neuroinformatics · 2026Article
- AI-based tumor cellularity assessment in digital pathology: A review of methods, datasets, and clinical translation.Journal of pathology informatics · 2026Review
- Toward comprehensive cellular characterization of H&E slides.Journal of pathology informatics · 2026Article
- SiNuS: A comprehensive dataset for singular nuclei segmentation forData in brief · 2026Article
- NucFuseRank: Dataset Fusion and Performance Ranking for Nuclei Instance Segmentation.Bioengineering (Basel, Switzerland) · 2026Article
- Microscopy Cell Segmentation: Review and Benchmarking of Task-Specific and Foundation Models.Journal of imaging · 2026Review
- Interpretable multitask model for clinical pathology image prediction and interpretation.NPJ systems biology and applications · 2026Article
- Development of Human Conjunctival Goblet Cell Segmentation Datasets to Improve Quantitation.Scientific data · 2026Article
- Decoding (digital) histopathology: The building blocks for computational researchers.PLOS digital health · 2026Review
- Synthetic data enables human-grade microtubule analysis with foundation models for segmentation.PLoS computational biology · 2026Article
- Self-Refining Segment Anything Model for Nuclei Segmentation as Contrastive Learning Approach to Label-Efficient Pathological Imaging.Diagnostics (Basel, Switzerland) · 2026Article
- Chidamide synergizes with cisplatin-etoposide to trigger pyroptosis and anti-tumor immunity in diffuse large B-cell lymphoma.Communications medicine · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Automated Assessment of Ki-67 Labeling Index Using Cell-Level Detection and Classification in Whole-Slide Images.Diagnostics (Basel, Switzerland) · 2026Article
- 3SGAN: Semi-Supervised and Multi-Task GAN for Stain Normalization and Nuclei Segmentation of Histopathological Images.Cancers · 2026Article
56 more citing papers are in PubMed but not listed here.
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87 authors.
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Abstract
Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.
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