ArticleFrontiers in bioengineering and biotechnology2020
Nuclear Segmentation in Histopathological Images Using Two-Stage Stacked U-Nets With Attention Mechanism.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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17 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Mitotic activity: A systematic literature review of the assessment methodology and prognostic value in feline tumors.Veterinary pathology · 2024Pooled it
- Image-based analysis of lymphoma cell heterogeneity: Comparative study of CRYO and FFPE techniques.Journal of pathology informatics · 2026Article
- High-frequency energy fusion (HFEF) for nuclei segmentation with boundary-aware loss.Quantitative imaging in medicine and surgery · 2026Article
- Biomechanics-Driven 3D Architecture Inference from Histology Using CellSqueeze3D.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Weak-to-strong generalization enables fully automated training of multi-head mask-RCNN model for segmenting densely overlapping cell nuclei in multiplex whole-slice brain images.Frontiers in bioinformatics · 2026Article
- Decision level scheme for fusing multiomics and histology slide images using deep neural network for tumor prognosis prediction.Scientific reports · 2025Article
- Efficient merging and validation of deep learning-based nuclei segmentations in H&E slides from multiple models.Journal of pathology informatics · 2025Article
- Object-based feedback attention in convolutional neural networks improves tumour detection in digital pathology.Scientific reports · 2024Article
- Segmentation of Variants of Nuclei on Whole Slide Images by Using Radiomic Features.Bioengineering (Basel, Switzerland) · 2024Article
- All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning.Journal of physics. Conference series · 2024Article
- Deep learning approaches for breast cancer detection in histopathology images: A review.Cancer biomarkers : section A of Disease markers · 2024Review
- Automatic semantic segmentation of the lumbar spine: Clinical applicability in a multi-parametric and multi-center study on magnetic resonance images.Artificial intelligence in medicine · 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
- Deep Learning-Based Segmentation of 3D Volumetric Image and Microstructural Analysis.Sensors (Basel, Switzerland) · 2023Article
- Deep-Fuzz: A synergistic integration of deep learning and fuzzy water flows for fine-grained nuclei segmentation in digital pathology.PloS one · 2023Article
- MSAL-Net: improve accurate segmentation of nuclei in histopathology images by multiscale attention learning network.BMC medical informatics and decision making · 2022Article
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5 authors at 2 institutions in 2 countries.
Funding
Abstract
Nuclei segmentation is a fundamental but challenging task in histopathological image analysis. One of the main problems is the existence of overlapping regions which increases the difficulty of independent nuclei separation. In this study, to solve the segmentation of nuclei and overlapping regions, we introduce a nuclei segmentation method based on two-stage learning framework consisting of two connected Stacked U-Nets (SUNets). The proposed SUNets consists of four parallel backbone nets, which are merged by the attention generation model. In the first stage, a Stacked U-Net is utilized to predict pixel-wise segmentation of nuclei. The output binary map together with RGB values of the original images are concatenated as the input of the second stage of SUNets. Due to the sizable imbalance of overlapping and background regions, the first network is trained with cross-entropy loss, while the second network is trained with focal loss. We applied the method on two publicly available datasets and achieved state-of-the-art performance for nuclei segmentation-mean Aggregated Jaccard Index (AJI) results were 0.5965 and 0.6210, and F1 scores were 0.8247 and 0.8060, respectively; our method also segmented the overlapping regions between nuclei, with average AJI = 0.3254. The proposed two-stage learning framework outperforms many current segmentation methods, and the consistent good segmentation performance on images from different organs indicates the generalized adaptability of our approach.
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