The fundamental principle of hepatectomy is to entirely excise the tumor while preserving adequate functional liver tissue volume. Thus, identifying tumor and non-tumor areas swiftly can enhance the precision and efficiency of liver resection, ultimately improving patient survival rates. In this study, we utilized multiphoton microscopy (MPM) to label-free identify liver tumor and non-tumor regions, following by automated classification with an open-source convolutional neural network, ResNet. The outcomes demonstrate that the network model can automatically and effectively distinguish tumor and non-tumor regions without human recognition, and MPM combining with deep learning may serve as an auxiliary tool for rapidly detection of hepatocellular carcinoma and aiding in liver resection treatment.
Liver fibrosis is a response to chronic liver damage, causing the accumulation of extracellular components like collagen fibers. Accurate evaluation of fibrosis is key for predicting disease prognosis. Multiphoton microscopy (MPM) is an advanced technique that allows label-free imaging of biomedical tissues using femtosecond laser-induced nonlinear optical effects. In this study, hepatic tissue samples were imaged via MPM and then imaging data were analyzed with Hover-Net, a convolutional neural network. We find that MPM has the ability to directly observe fibrotic changes, and also find that the number of portal bile ducts are positively related to liver fibrosis and can be automatically identified by the Hover-Net. These findings suggest MPM combining with deep learning can assess liver fibrosis quickly and reliably without the need for exogenous contrast agents.
Hepatic steatosis, the accumulation of lipids within hepatocytes, is defined as intrahepatic fat of at least 5% of liver weight and is an important histological feature. Steatosis may manifest in a variety of liver diseases, and its clinical significance depends on the degree of hepatic steatosis. Excess intrahepatic fat content is a risk factor for disease progression. Increased hepatic steatosis could trigger metabolic dysfunction leading to insulin resistance, dyslipidemia, cardiovascular disease, and progression to non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma (HCC). In many chronic liver diseases, hepatic steatosis is associated with increased hepatic fibrosis. Clinical methods of quantifying hepatic steatosis remain semi-quantitative, with potential limitations in precision. Moreover, the evaluation of hepatic steatosis and fibrosis cannot be performed simultaneously. In this work, multiphoton microscopy (MPM) combined two-photon excited fluorescence with second harmonic generation imaging was used to identify the hepatic steatosis and fibrosis in chronic liver disease. The result showed that MPM has the potential to be a pathological diagnostic tool for hepatic steatosis and fibrosis.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.