ArticleJournal of biomedical physics & engineering2021
Automated Segmentation of Abnormal Tissues in Medical Images.
Article in Journal of biomedical physics & engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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Who cites it
4 citing papers in PubMed.
- Modeling radiologists' cognitive processes using a digital gaze twin to enhance radiology training.Scientific reports · 2025Article
- Independent evaluation of the accuracy of 5 artificial intelligence software for detecting lung nodules on chest X-rays.Quantitative imaging in medicine and surgery · 2024Article
- Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine.Current oncology (Toronto, Ont.) · 2023Review
- Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis-a narrative review.Exploration of targeted anti-tumor therapy · 2022Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nowadays, medical image modalities are almost available everywhere. These modalities are bases of diagnosis of various diseases sensitive to specific tissue type. Usually physicians look for abnormalities in these modalities in diagnostic procedures. Count and volume of abnormalities are very important for optimal treatment of patients. Segmentation is a preliminary step for these measurements and also further analysis. Manual segmentation of abnormalities is cumbersome, error prone, and subjective. As a result, automated segmentation of abnormal tissue is a need. In this study, representative techniques for segmentation of abnormal tissues are reviewed. Main focus is on the segmentation of multiple sclerosis lesions, breast cancer masses, lung nodules, and skin lesions. As experimental results demonstrate, the methods based on deep learning techniques perform better than other methods that are usually based on handy feature engineering techniques. Finally, the most common measures to evaluate automated abnormal tissue segmentation methods are reported.
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Registered trials
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