ArticleBiomedical optics express2021
Explainable liver tumor delineation in surgical specimens using hyperspectral imaging and deep learning.
Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 3 syntheses or guidelines pooled it, 23 citations in OpenAlex.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- Clinical applications of hyperspectral imaging in gastroenterology and hepatology: A systematic review.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2026Pooled it
- Explainable artificial intelligence for spectroscopy data: a review.Pflugers Archiv : European journal of physiology · 2025Pooled it
- Hyperspectral imaging for orthopedic surgery: lightweight AI enables real-time bone and cartilage segmentation.Medical & biological engineering & computing · 2026Article
- Beyond the convergence of metabolic reprogramming in primary liver cancer: a comprehensive review on energy and lipids metabolism.Frontiers in cell and developmental biology · 2026Review
- Article
- Deformable multi-modal image registration for the correlation between optical measurements and histology images.Journal of biomedical optics · 2024Article
- SAHIS-Net: a spectral attention and feature enhancement network for microscopic hyperspectral cholangiocarcinoma image segmentation.Biomedical optics express · 2024Article
- Intelligent tumor tissue classification for Hybrid Health Care Units.Frontiers in medicine · 2024Article
- SpeCamX: mobile app that turns unmodified smartphones into multispectral imagers.Biomedical optics express · 2023Article
- Application of 3D Bioprinting in Liver Diseases.Micromachines · 2023Review
- Signal to noise ratio quantifies the contribution of spectral channels to classification of human head and neck tissuesJournal of biomedical optics · 2023Article
- Detecting Asymptomatic Infections of Rice Bacterial Leaf Blight Using Hyperspectral Imaging and 3-Dimensional Convolutional Neural Network With Spectral Dilated Convolution.Frontiers in plant science · 2022Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Surgical removal is the primary treatment for liver cancer, but frequent recurrence caused by residual malignant tissue remains an important challenge, as recurrence leads to high mortality. It is unreliable to distinguish tumors from normal tissues merely under visual inspection. Hyperspectral imaging (HSI) has been proved to be a promising technology for intra-operative use by capturing the spatial and spectral information of tissue in a fast, non-contact and label-free manner. In this work, we investigated the feasibility of HSI for liver tumor delineation on surgical specimens using a multi-task U-Net framework. Measurements are performed on 19 patients and a dataset of 36 specimens was collected with corresponding pathological results serving as the ground truth. The developed framework can achieve an overall sensitivity of 94.48% and a specificity of 87.22%, outperforming the baseline SVM method by a large margin. In particular, we propose to add explanations on the well-trained model from the spatial and spectral dimensions to show the contribution of pixels and spectral channels explicitly. On that basis, a novel saliency-weighted channel selection method is further proposed to select a small subset of 5 spectral channels which provide essentially as much information as using all 224 channels. According to the dominant channels, the absorption difference of hemoglobin and bile content in the normal and malignant tissues seems to be promising markers that could be further exploited.
Identifiers
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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 Socratic graph.