ArticleScientific reports2020
Improved small blob detection in 3D images using jointly constrained deep learning and Hessian analysis.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Distinguishing a Drone from Birds Based on Trajectory Movement and Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- A Supervised Deep Learning Model Was Developed to Classify Nelore Cattle (Animals : an open access journal from MDPI · 2026Article
- AutoGlom: software tool for segmentation and analysis of magnetic resonance images of the kidney.American journal of physiology. Renal physiology · 2026Article
- Preterm birth increases susceptibility to hyperglycemia induced glomerular alterations in male mice.Scientific reports · 2025Article
- Automatic Blob Detection Method for Cancerous Lesions in Unsupervised Breast Histology Images.Bioengineering (Basel, Switzerland) · 2025Article
- Laplacian of Gaussian for Fast Cell Detection and Segmentation in Cervical Cytology to Help in Cancer Diagnosis.Cureus · 2025Article
- Uncertainty Quantification for Scale-Space Blob Detection.Journal of mathematical imaging and vision · 2024Article
- BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging.Bioengineering (Basel, Switzerland) · 2023Article
- CenFind: a deep-learning pipeline for efficient centriole detection in microscopy datasets.BMC bioinformatics · 2023Article
- Self-supervised machine learning pushes the sensitivity limit in label-free detection of single proteins below 10 kDa.Nature methods · 2023Article
- Scale space detector for analyzing spatiotemporal ventricular contractility and nuclear morphogenesis in zebrafish.iScience · 2022Article
- Small Blob Detector Using Bi-Threshold Constrained Adaptive Scales.IEEE transactions on bio-medical engineering · 2021Article
- Image analysis techniques to map pyramids, pyramid structure, glomerular distribution, and pathology in the intact human kidney from 3-D MRI.American journal of physiology. Renal physiology · 2021Article
- Nephron number and its determinants: a 2020 update.Pediatric nephrology (Berlin, Germany) · 2021Article
- Magnetic resonance imaging accurately tracks kidney pathology and heterogeneity in the transition from acute kidney injury to chronic kidney disease.Kidney international · 2021Article
- Nuclei Detection for 3D Microscopy With a Fully Convolutional Regression Network.IEEE access : practical innovations, open solutions · 2021Article
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5 authors.
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Abstract
Imaging biomarkers are being rapidly developed for early diagnosis and staging of disease. The development of these biomarkers requires advances in both image acquisition and analysis. Detecting and segmenting objects from images are often the first steps in quantitative measurement of these biomarkers. The challenges of detecting objects in images, particularly small objects known as blobs, include low image resolution, image noise and overlap between the blobs. The Difference of Gaussian (DoG) detector has been used to overcome these challenges in blob detection. However, the DoG detector is susceptible to over-detection and must be refined for robust, reproducible detection in a wide range of medical images. In this research, we propose a joint constraint blob detector from U-Net, a deep learning model, and Hessian analysis, to overcome these problems and identify true blobs from noisy medical images. We evaluate this approach, UH-DoG, using a public 2D fluorescent dataset for cell nucleus detection and a 3D kidney magnetic resonance imaging dataset for glomerulus detection. We then compare this approach to methods in the literature. While comparable to the other four comparing methods on recall, the UH-DoG outperforms them on both precision and F-score.
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