We report quantitative label-free imaging with phase and polarization (QLIPP) for simultaneous measurement of density, anisotropy, and orientation of structures in unlabeled live cells and tissue slices, using advanced image processing techniques. We combine QLIPP with deep neural networks and machine learning models to predict fluorescence images of diverse cell and tissue structures, enhancing image classification and segmentation capabilities.

QLIPP images reveal anatomical regions and axon tract orientation in prenatal human brain tissue sections that are not visible using traditional brightfield imaging. This supports real world applications in medical imaging and neuroscience, leveraging deep learning technology to surpass limitations of traditional image processing.

We report a variant of the U-Net architecture, the multi-channel 2.5D U-Net, for computationally efficient prediction of fluorescence images in three dimensions and across large fields of view, enabling powerful semantic segmentation and image analysis. Further, we develop data normalization methods to improve model accuracy in predicting myelin distribution over extensive brain regions, crucial for medical image processing tasks.

Our approach addresses challenges in labeled data by recovering experimental defects in fluorescence labeling through quantitative label-free imaging and artificial neural networks. We utilize transfer learning, feature extraction, and deep learning algorithms to refine predictions.

This methodology demonstrates the power of deep learning models in analyzing image data and enhances the potential for recurrent neural networks, convolutional layers, and fully connected layers in imaging pipelines. We anticipate that the proposed method will enable new studies of architectural order at spatial scales ranging from organelles to tissue, contributing to fields such as remote sensing, facial recognition, and computer vision applications.