ArticleJournal of biomedical optics2022
Hyperspectral imaging for tumor segmentation on pigmented skin lesions.
Article in Journal of biomedical optics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- Personalized digital pipeline for basal cell carcinoma delineation using hyperspectral imaging.Biomedical optics express · 2026Article
- A novel multi-agent spatiotemporal fusion framework for intelligent skin cancer diagnosis.Frontiers in oncology · 2026Article
- Digital Dermatopathology of Scabies: HE-Compatible VIS-NIR Hyperspectral Imaging as a Label-Free Proof-of-Concept Approach.Bioengineering (Basel, Switzerland) · 2025Article
- Hyperspectral and machine-learning-based classification of ischemic intestinal tissue.Journal of biomedical optics · 2025Article
- Advancing non-invasive melanoma diagnostics with deep learning and multispectral photoacoustic imaging.Photoacoustics · 2025Article
- Article
- Hyperspectral imaging with machine learning for in vivo skin carcinoma margin assessment: a preliminary study.Physical and engineering sciences in medicine · 2024Article
- One-Stop Shop: Diagnosis and Treatment of Basal Cell Carcinoma in One Step.Journal of clinical medicine · 2024Review
- Hyperspectral imaging-based erythema classification in atopic dermatitis.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) · 2024Article
- Bibliometric analysis of the current status and trends on medical hyperspectral imaging.Frontiers in medicine · 2023Review
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9 authors.
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Abstract
Significance: Malignant skin tumors, which include melanoma and nonmelanoma skin cancers, are the most prevalent type of malignant tumor. Gross pathology of pigmented skin lesions (PSL) remains manual, time-consuming, and heavily dependent on the expertise of the medical personnel. Hyperspectral imaging (HSI) can assist in the detection of tumors and evaluate the status of tumor margins by their spectral signatures. Aim: Tumor segmentation of medical HSI data is a research field. The goal of this study is to propose a framework for HSI-based tumor segmentation of PSL. Approach: An HSI dataset of 28 PSL was prepared. Two frameworks for data preprocessing and tumor segmentation were proposed. Models based on machine learning and deep learning were used at the core of each framework. Results: Cross-validation performance showed that pixel-wise processing achieves higher segmentation performance, in terms of the Jaccard coefficient. Simultaneous use of spatio-spectral features produced more comprehensive tumor masks. A three-dimensional Xception-based network achieved performance similar to state-of-the-art networks while allowing for more detailed detection of the tumor border. Conclusions: Good performance was achieved for melanocytic lesions, but margins were difficult to detect in some cases of basal cell carcinoma. The frameworks proposed in this study could be further improved for robustness against different pathologies and detailed delineation of tissue margins to facilitate computer-assisted diagnosis during gross pathology.
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