ReviewEvolving systems2023
A survey on recent trends in deep learning for nucleus segmentation from histopathology images.
Review in Evolving systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- NS-GUSL: Green U-Shaped Learning for Nuclei Segmentation from Histopathology Images.Journal of imaging · 2026Article
- High-frequency energy fusion (HFEF) for nuclei segmentation with boundary-aware loss.Quantitative imaging in medicine and surgery · 2026Article
- Systematic evaluation of computational methods for cell segmentation.Briefings in bioinformatics · 2026Article
- SciSt: single-cell reference-informed spatial gene expression prediction from pathological images.Briefings in bioinformatics · 2025Article
- Predicting ROS1 and ALK fusions in NSCLC from H&E slides with a two-step vision transformer approach.NPJ precision oncology · 2025Article
- Advanced Deep Learning Approaches in Detection Technologies for Comprehensive Breast Cancer Assessment Based on WSIs: A Systematic Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- Lightweight Evolving U-Net for Next-Generation Biomedical Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- LOGSAGE: LOG-BASED SALIENCY FOR GUIDED ENCODING IN ROBUST NUCLEI SEGMENTATION OF IMMUNOFLUORESCENCE HISTOLOGY IMAGES.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- Deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data.Communications medicine · 2025Article
- Instance-level semantic segmentation of nuclei based on multimodal structure encoding.BMC bioinformatics · 2025Article
- Cell Segmentation With Globally Optimized Boundaries (CSGO): A Deep Learning Pipeline for Whole-Cell Segmentation in Hematoxylin-and-Eosin-Stained Tissues.Laboratory investigation; a journal of technical methods and pathology · 2025Article
- Towards interactive AI-authoring with prototypical few-shot classifiers in histopathology.Journal of pathology informatics · 2024Article
- Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization.Computational and structural biotechnology journal · 2024Article
- A Quantitative Measurement Method for Nuclear-Pleomorphism Scoring in Breast Cancer.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- Image segmentation with Cellular Automata.Heliyon · 2024Article
- BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging.Bioengineering (Basel, Switzerland) · 2023Article
- NISNet3D: three-dimensional nuclear synthesis and instance segmentation for fluorescence microscopy images.Scientific reports · 2023Article
- Deep-learning segmentation of fascicles from microCT of the human vagus nerve.Frontiers in neuroscience · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nucleus segmentation is an imperative step in the qualitative study of imaging datasets, considered as an intricate task in histopathology image analysis. Segmenting a nucleus is an important part of diagnosing, staging, and grading cancer, but overlapping regions make it hard to separate and tell apart independent nuclei. Deep Learning is swiftly paving its way in the arena of nucleus segmentation, attracting quite a few researchers with its numerous published research articles indicating its efficacy in the field. This paper presents a systematic survey on nucleus segmentation using deep learning in the last five years (2017-2021), highlighting various segmentation models (U-Net, SCPP-Net, Sharp U-Net, and LiverNet) and exploring their similarities, strengths, datasets utilized, and unfolding research areas.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.