ArticleMedical image analysis2021
Segmentation of cellular patterns in confocal images of melanocytic lesions in vivo via a multiscale encoder-decoder network (MED-Net).
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 4 citations in OpenAlex.
- Deep learning analysis of reflectance confocal microscopy for basal cell carcinoma diagnosis.JAAD international · 2026Article
- Noninvasive Diagnosis of Melanoma Using Machine Learning and Reflectance Confocal Microscopy.The Journal of investigative dermatology · 2026Article
- Quantitative assessment ofBiomedical optics express · 2025Article
- Context-guided segmentation for histopathologic cancer segmentation.Scientific reports · 2025Article
- Artificial Intelligence in the Non-Invasive Detection of Melanoma.Life (Basel, Switzerland) · 2024Review
- Confocal Microscopy for Diagnosis and Management of Cutaneous Malignancies: Clinical Impacts and Innovation.Diagnostics (Basel, Switzerland) · 2023Review
- Article
- Utilizing deep learning for dermal matrix quality assessment on in vivo line-field confocal optical coherence tomography images.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2023Article
- In vivo tumor immune microenvironment phenotypes correlate with inflammation and vasculature to predict immunotherapy response.Nature communications · 2022Article
- [Exploring and analyzing the improvement mechanism of U-Net and its application in medical image segmentation].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- In vivo microscopy as an adjunctive tool to guide detection, diagnosis, and treatment.Journal of biomedical optics · 2022Article
- Artificial Intelligence-Based Approaches to Reflectance Confocal Microscopy Image Analysis in Dermatology.Journal of clinical medicine · 2022Review
- Deep Learning for Basal Cell Carcinoma Detection for Reflectance Confocal Microscopy.The Journal of investigative dermatology · 2022Article
- Biopsy-free in vivo virtual histology of skin using deep learning.Light, science & applications · 2021Article
- A Clinical Perspective on the Automated Analysis of Reflectance Confocal Microscopy in Dermatology.Lasers in surgery and medicine · 2021Review
- Deep Learning in Biomedical Optics.Lasers in surgery and medicine · 2021Review
- Semantic segmentation of reflectance confocal microscopy mosaics of pigmented lesions using weak labels.Scientific reports · 2021Article
- Deep Learning for Automated Analysis of Cellular and Extracellular Components of the Foreign Body Response in Multiphoton Microscopy Images.Frontiers in bioengineering and biotechnology · 2021Article
- Utilizing Machine Learning for Image Quality Assessment for Reflectance Confocal Microscopy.The Journal of investigative dermatology · 2020Article
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Authors and funding
9 authors at 5 institutions in 3 countries.
Funding
Abstract
In-vivo optical microscopy is advancing into routine clinical practice for non-invasively guiding diagnosis and treatment of cancer and other diseases, and thus beginning to reduce the need for traditional biopsy. However, reading and analysis of the optical microscopic images are generally still qualitative, relying mainly on visual examination. Here we present an automated semantic segmentation method called "Multiscale Encoder-Decoder Network (MED-Net)" that provides pixel-wise labeling into classes of patterns in a quantitative manner. The novelty in our approach is the modeling of textural patterns at multiple scales (magnifications, resolutions). This mimics the traditional procedure for examining pathology images, which routinely starts with low magnification (low resolution, large field of view) followed by closer inspection of suspicious areas with higher magnification (higher resolution, smaller fields of view). We trained and tested our model on non-overlapping partitions of 117 reflectance confocal microscopy (RCM) mosaics of melanocytic lesions, an extensive dataset for this application, collected at four clinics in the US, and two in Italy. With patient-wise cross-validation, we achieved pixel-wise mean sensitivity and specificity of 74% and 92%, respectively, with 0.74 Dice coefficient over six classes. In the scenario, we partitioned the data clinic-wise and tested the generalizability of the model over multiple clinics. In this setting, we achieved pixel-wise mean sensitivity and specificity of 77% and 94%, respectively, with 0.77 Dice coefficient. We compared MED-Net against the state-of-the-art semantic segmentation models and achieved better quantitative segmentation performance. Our results also suggest that, due to its nested multiscale architecture, the MED-Net model annotated RCM mosaics more coherently, avoiding unrealistic-fragmented annotations.
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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.