Vascular network labeling in transparent tissues provides more complete information on blood vessels. To achieve a fast and efficient method for vascular network labeling in transparent tissues, we compared various vascular labeling methods under different tissue clearing protocols. FITC-dextran labeling and CUBIC cleaning treatment were found to be the best options for vascular network labeling in cleared mouse tissues. Satisfactory labeling of vascular networks in various organs can be achieved by selecting FITC-dextran with different molecular weights and different administration methods.

To alleviate the ill-posed of the inverse problem in Fluorescent molecular tomography (FMT), many regularization methods based on L 2 or L 1 norm have been proposed. Whereas, the quality of regularization parameters affect the performance of the reconstruction algorithm. Some classical parameter selection strategies usually need initialization of parameter range and high computing costs, which is not universal in the practical application of FMT. In this paper, an universally applicable adaptive parameter selection method based on maximizing the probability of data (MPD) strategy was proposed. This strategy used maximum a posteriori (MAP) estimation and maximum likelihood (ML) estimation to establish a regularization parameters model. The stable optimal regularization parameters can be determined by multiple iterative estimates. Numerical simulations and in vivo experiments show that MPD strategy can obtain stable regularization parameters for both regularization algorithms based on L 2 or L 1 norm and achieve good reconstruction performance.

The present study presents an alternative analytical workflow that combines mid-infrared (MIR) microscopic imaging and deep learning to diagnose human lymphoma and differentiate between small and large cell lymphoma. We could show that using a deep learning approach to analyze MIR hyperspectral data obtained from benign and malignant lymph node pathology results in high accuracy for correct classification, learning the distinct region of 3900 cm-1 to 850 cm-1. The accuracy is above 95% for every pair of malignant lymphoid tissue and still above 90% for the distinction between benign and malignant lymphoid tissue for binary classification. These results demonstrate that a preliminary diagnosis and subtyping of human lymphoma could be streamlined by applying a deep learning approach to analyze MIR spectroscopic data.