ArticleIEEE transactions on medical imaging2020
Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images.
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 97 papers, 1 of them a synthesis that pooled it.
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97 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation.Biomedical engineering online · 2024Pooled it
- Robust Cell Segmentation for Size Distribution Estimation via Synthetic-Data Training.Biotechnology journal · 2026Article
- Lightweight self supervised learning framework for domain generalization in histopathology.Scientific reports · 2025Article
- HistoChat: Instruction-tuning multimodal vision language assistant for colorectal histopathology on limited data.Patterns (New York, N.Y.) · 2025Article
- A deep learning derived prostate zonal volume-based biomarker from T2-weighted MRI to distinguish between prostate cancer and benign prostatic hyperplasia.Medical physics · 2025Article
- Scalable Nuclei Detection in HER2-SISH Whole Slide Images via Fine-Tuned Stardist with Expert-Annotated Regions of Interest.Diagnostics (Basel, Switzerland) · 2025Article
- Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases.NPJ digital medicine · 2025Article
- Efficient merging and validation of deep learning-based nuclei segmentations in H&E slides from multiple models.Journal of pathology informatics · 2025Article
- Instruction tuning for colorectal histopathology: a multimodal vision-language assistant on human-evaluated data.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- DUNE: a versatile neuroimaging encoder captures brain complexity across 3 major diseases: cancer, dementia, and schizophrenia.GigaScience · 2025Article
- State-of-the-Art Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues.Journal of imaging · 2024Review
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Multimodal Machine Learning for Predicting Post-Surgery Quality of Life in Colorectal Cancer Patients.Journal of imaging · 2024Article
- Adversarial robustness improvement for X-ray bone segmentation using synthetic data created from computed tomography scans.Scientific reports · 2024Article
- Leveraging weak complementary labels enhances semantic segmentation of hepatocellular carcinoma and intrahepatic cholangiocarcinoma.Scientific reports · 2024Article
- Deep learning-based virtual staining, segmentation, and classification in label-free photoacoustic histology of human specimens.Light, science & applications · 2024Article
- Unsupervised Segmentation of 3D Microvascular Photoacoustic Images Using Deep Generative Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Deep Image Segmentation for Cardiomyocyte Proliferation.JACC. Basic to translational science · 2024Article
- Predicting treatment plan approval probability for high-dose-rate brachytherapy of cervical cancer using adversarial deep learning.Physics in medicine and biology · 2024Article
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37 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
Nuclei mymargin segmentation is a fundamental task for various computational pathology applications including nuclei morphology analysis, cell type classification, and cancer grading. Deep learning has emerged as a powerful approach to segmenting nuclei but the accuracy of convolutional neural networks (CNNs) depends on the volume and the quality of labeled histopathology data for training. In particular, conventional CNN-based approaches lack structured prediction capabilities, which are required to distinguish overlapping and clumped nuclei. Here, we present an approach to nuclei segmentation that overcomes these challenges by utilizing a conditional generative adversarial network (cGAN) trained with synthetic and real data. We generate a large dataset of H&E training images with perfect nuclei segmentation labels using an unpaired GAN framework. This synthetic data along with real histopathology data from six different organs are used to train a conditional GAN with spectral normalization and gradient penalty for nuclei segmentation. This adversarial regression framework enforces higher-order spacial-consistency when compared to conventional CNN models. We demonstrate that this nuclei segmentation approach generalizes across different organs, sites, patients and disease states, and outperforms conventional approaches, especially in isolating individual and overlapping nuclei.
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