ArticleScientific reports2021
A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Femtosecond label-free imaging: A rapid and reliable alternative for intraoperative pathological assessment in thoracic oncology.JTCVS open · 2026Article
- Multimodal survival analysis of glioblastoma using whole-slide histopathology, gene expression, clinical variables and language-model-derived mutation features.Scientific reports · 2026Article
- Watershed Encoder-Decoder Neural Network for Nuclei Segmentation of Breast Cancer Histology Images.Bioengineering (Basel, Switzerland) · 2026Article
- Enhanced digital pathology image recognition via multi-attention mechanisms: the MACC-Net approach.Scientific reports · 2025Article
- Deep learning model for detecting high-grade dysplasia in colorectal adenomas.Journal of pathology informatics · 2025Article
- Viable tumor cell density after neoadjuvant chemotherapy assessed using deep learning model reflects the prognosis of osteosarcoma.NPJ precision oncology · 2024Article
- Article
- Automatic identification of pavement cracks in public roads using an optimized deep convolutional neural network model.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Article
- Breast Cancer Histopathological Images Segmentation Using Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Age-related morphometrics of normal adrenal glands based on deep learning-aided segmentation.Heliyon · 2023Article
- A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled Segmentation of Breast Cancer.Scientific data · 2023Article
- Multilevel Multiobjective Particle Swarm Optimization Guided Superpixel Algorithm for Histopathology Image Detection and Segmentation.Journal of imaging · 2023Article
- A survey on recent trends in deep learning for nucleus segmentation from histopathology images.Evolving systems · 2023Review
- A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images.Heliyon · 2023Article
- Transfer Learning Approach and Nucleus Segmentation with MedCLNet Colon Cancer Database.Journal of digital imaging · 2023Article
- EmergeNet: A novel deep-learning based ensemble segmentation model for emergence timing detection of coleoptile.Frontiers in plant science · 2023Article
- Efficient Staining-Invariant Nuclei Segmentation Approach Using Self-Supervised Deep Contrastive Network.Diagnostics (Basel, Switzerland) · 2022Article
- Predicting the outcome of radiotherapy in brain metastasis by integrating the clinical and MRI-based deep learning features.Medical physics · 2022Article
- InSiNet: a deep convolutional approach to skin cancer detection and segmentation.Medical & biological engineering & computing · 2022Article
- Software tools and platforms in Digital Pathology: a review for clinicians and computer scientists.Journal of pathology informatics · 2022Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is currently the second most common cause of cancer-related death in women. Presently, the clinical benchmark in cancer diagnosis is tissue biopsy examination. However, the manual process of histopathological analysis is laborious, time-consuming, and limited by the quality of the specimen and the experience of the pathologist. This study's objective was to determine if deep convolutional neural networks can be trained, with transfer learning, on a set of histopathological images independent of breast tissue to segment tumor nuclei of the breast. Various deep convolutional neural networks were evaluated for the study, including U-Net, Mask R-CNN, and a novel network (GB U-Net). The networks were trained on a set of Hematoxylin and Eosin (H&E)-stained images of eight diverse types of tissues. GB U-Net demonstrated superior performance in segmenting sites of invasive diseases (AJI = 0.53, mAP = 0.39 & AJI = 0.54, mAP = 0.38), validated on two hold-out datasets exclusively containing breast tissue images of approximately 7,582 annotated cells. The results of the networks, trained on images independent of breast tissue, demonstrated that tumor nuclei of the breast could be accurately segmented.
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