A Review of Nuclei Detection and Segmentation on Microscopy Images Using Deep Learning With Applications to Unbiased Stereology Counting

The detection and segmentation of stained cells and nuclei are essential prerequisites for subsequent quantitative research for many diseases. Recently, deep learning has shown strong performance in many computer vision problems, including solutions for medical image analysis. Furthermore, accurate...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2024-06, Vol.35 (6), p.7458-7477
Hauptverfasser:	Alahmari, Saeed S., Goldgof, Dmitry, Hall, Lawrence O., Mouton, Peter R.
Format:	Artikel
Sprache:	eng
Schlagworte:	Active learning Algorithms Alzheimer Disease - diagnostic imaging Alzheimer Disease - pathology Alzheimer's disease Animals cell Cell Nucleus Computer vision convolutional neural network (CNN) Deep Learning Feature extraction generative adversarial network (GAN) Histopathology Humans Image analysis Image processing Image Processing, Computer-Assisted - methods Image segmentation Medical imaging Microscopy Microscopy - methods microscopy image analysis Neoplasms - diagnostic imaging Neoplasms - pathology Neural networks Neural Networks, Computer Neurodegenerative diseases nuclei Nuclei (cytology) nuclei detection nuclei segmentation Probes Quantitative research Segmentation Stereology unbiased stereology
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