ArticleBiomedical engineering online2018
Multi-tissue and multi-scale approach for nuclei segmentation in H&E stained images.
Article in Biomedical engineering online, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
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- A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images.Heliyon · 2023Article
- Role of tumor infiltrating lymphocytes and spatial immune heterogeneity in sensitivity to PD-1 axis blockers in non-small cell lung cancer.Journal for immunotherapy of cancer · 2022Article
- Automated Cell Foreground-Background Segmentation with Phase-Contrast Microscopy Images: An Alternative to Machine Learning Segmentation Methods with Small-Scale Data.Bioengineering (Basel, Switzerland) · 2022Article
- Local Integral Regression Network for Cell Nuclei Detection.Entropy (Basel, Switzerland) · 2021Article
- Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features.Journal of mammary gland biology and neoplasia · 2021Article
- Ex Vivo MR Histology and Cytometric Feature Mapping Connect Three-dimensional in Vivo MR Images to Two-dimensional Histopathologic Images of Murine Sarcomas.Radiology. Imaging cancer · 2021Article
- Histopathological Classification of Canine Cutaneous Round Cell Tumors Using Deep Learning: A Multi-Center Study.Frontiers in veterinary science · 2021Article
- Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images.IEEE transactions on medical imaging · 2020Article
- Intraoperative assessment of skull base tumors using stimulated Raman scattering microscopy.Scientific reports · 2019Article
- Automatic skin lesion area determination of basal cell carcinoma using optical coherence tomography angiography and a skeletonization approach: Preliminary results.Journal of biophotonics · 2019Article
- Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow.Micromachines · 2019Article
- Automated Segmentation of Fluorescence Microscopy Images for 3D Cell Detection in human-derived Cardiospheres.Scientific reports · 2019Article
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Abstract
backgroundAccurate nuclei detection and segmentation in histological images is essential for many clinical purposes. While manual annotations are time-consuming and operator-dependent, full automated segmentation remains a challenging task due to the high variability of cells intensity, size and morphology. Most of the proposed algorithms for the automated segmentation of nuclei were designed for specific organ or tissues.
resultsThe aim of this study was to develop and validate a fully multiscale method, named MANA (Multiscale Adaptive Nuclei Analysis), for nuclei segmentation in different tissues and magnifications. MANA was tested on a dataset of H&E stained tissue images with more than 59,000 annotated nuclei, taken from six organs (colon, liver, bone, prostate, adrenal gland and thyroid) and three magnifications (10×, 20×, 40×). Automatic results were compared with manual segmentations and three open-source software designed for nuclei detection. For each organ, MANA obtained always an F1-score higher than 0.91, with an average F1 of 0.9305 ± 0.0161. The average computational time was about 20 s independently of the number of nuclei to be detected (anyway, higher than 1000), indicating the efficiency of the proposed technique.
conclusionTo the best of our knowledge, MANA is the first fully automated multi-scale and multi-tissue algorithm for nuclei detection. Overall, the robustness and versatility of MANA allowed to achieve, on different organs and magnifications, performances in line or better than those of state-of-art algorithms optimized for single tissues.
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