ArticleMedical & biological engineering & computing2017
Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships.
Article in Medical & biological engineering & computing, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 102 citations in OpenAlex.
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- Application of Deep Learning in Histopathology Images of Breast Cancer: A Review.Micromachines · 2022Review
- An Open-Source AI Framework for the Analysis of Single Cells in Whole-Slide Images with a Note on CD276 in Glioblastoma.Cancers · 2022Article
- Random Neural Network Based Epileptic Seizure Episode Detection Exploiting Electroencephalogram Signals.Sensors (Basel, Switzerland) · 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
- Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training.Medical & biological engineering & computing · 2020Article
- Activity landscape image analysis using convolutional neural networks.Journal of cheminformatics · 2020Article
- Segmentation of Heavily Clustered Nuclei from Histopathological Images.Scientific reports · 2019Article
- Deep learning fully convolution network for lumen characterization in diabetic patients using carotid ultrasound: a tool for stroke risk.Medical & biological engineering & computing · 2019Article
- Automated clear cell renal carcinoma grade classification with prognostic significance.PloS one · 2019Article
- PCSeg: Color model driven probabilistic multiphase level set based tool for plasma cell segmentation in multiple myeloma.PloS one · 2018Article
- Deep-learning based, automated segmentation of macular edema in optical coherence tomography.Biomedical optics express · 2017Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies on cancer, pathologists can use CAD systems as second readers to analyze high-resolution histopathological images. Since cell detection and segmentation are critical for cancer grade assessments, cellular and extracellular structures should primarily be extracted from histopathological images. In response, we sought to identify a useful cell segmentation approach with histopathological images that uses not only prominent deep learning algorithms (i.e., convolutional neural networks, stacked autoencoders, and deep belief networks), but also spatial relationships, information of which is critical for achieving better cell segmentation results. To that end, we collected cellular and extracellular samples from histopathological images by windowing in small patches with various sizes. In experiments, the segmentation accuracies of the methods used improved as the window sizes increased due to the addition of local spatial and contextual information. Once we compared the effects of training sample size and influence of window size, results revealed that the deep learning algorithms, especially convolutional neural networks and partly stacked autoencoders, performed better than conventional methods in cell segmentation.
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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.