We present nonlinear microscopy imaging results and analysis from canine mammary cancer biopsies. PU-H71 cell line Second harmonic generation imaging allows information of the collagen structure in the extracellular matrix that together with the fluorescence of the cell regions of the biopsies form a base for comprehensive image analysis. We demonstrate an automated image analysis method to classify the histological type of canine mammary cancer using a range of parameters extracted from the images. The software developed for image processing and analysis allows for the extraction of the collagen fibre network and the cell regions of the images. Thus, the tissue properties are obtained after the segmentation of the image and the metrics are measured specifically for the collagen and the cell regions. A linear discriminant analysis including all the extracted metrics allowed to clearly separate between the healthy and cancerous tissue with a 91%-accuracy. Also, a 61%-accuracy was achieved for a comparison of healthy and three histological cancer subtypes studied.Two main bottlenecks prevent time-domain diffuse optics instruments to reach their maximum performances, namely the limited light harvesting capability of the detection chain and the bounded data throughput of the timing electronics. In this work, for the first time to our knowledge, we overcome both those limitations using a probe-hosted large area silicon photomultiplier detector coupled to high-throughput timing electronics. The system performances were assessed based on international protocols for diffuse optical imagers showing better figures with respect to a state-of-the-art device. As a first step towards applications, proof-of-principle in-vivo brain activation measurements demonstrated superior signal-to-noise ratio as compared to current technologies.When imaging birefringent samples using optical coherence tomography angiography (OCTA), the phase retardation may appear opposite to the phase change due to the blood flow in the orthogonal signals, for which a cancellation effect can occur when deriving OCTA signals. This effect can diminish the ability of OCTA to detect vascular information, leading to an erroneous interpretation of the final OCTA images. To mitigate this issue, we demonstrate polarization-sensitive optical coherence tomography (PS-OCT) to image microvascular information within a living sample without polarization induced artifacts. The system is furnished with a swept source OCT (SS-OCT) that incorporates two imaging modes OCTA imaging and polarization-sensitive imaging. PS-OCT is used to provide birefringent contrast where the color-encoded Stokes parameters are used to obtain high contrast polarization-state images. OCTA is used to acquire high-resolution images of functional microvascular networks permeating the scanned tissue volume. Taking the advantages of the dual-channel PS-OCT configuration, the polarization induced artifacts are eliminated from OCTA vascular imaging. The proposed PS-OCTA system is employed to visualize the birefringent components and the vascular networks of the human skin in vivo. It is expected that the proposed system setup would have useful and practical applications in the investigations of the vasculature in the birefringent tissue samples both pre-clinically and clinically.Recently proposed deep learning (DL) algorithms for the segmentation of optical coherence tomography (OCT) images to quantify the morphological changes to the optic nerve head (ONH) tissues during glaucoma have limited clinical adoption due to their device specific nature and the difficulty in preparing manual segmentations (training data). We propose a DL-based 3D segmentation framework that is easily translatable across OCT devices in a label-free manner (i.e. without the need to manually re-segment data for each device). Specifically, we developed 2 sets of DL networks the ‘enhancer’ (enhance OCT image quality and harmonize image characteristics from 3 devices) and the ‘ONH-Net’ (3D segmentation of 6 ONH tissues). We found that only when the ‘enhancer’ was used to preprocess the OCT images, the ‘ONH-Net’ trained on any of the 3 devices successfully segmented ONH tissues from the other two unseen devices with high performance (Dice coefficients > 0.92). We demonstrate that is possible to automatically segment OCT images from new devices without ever needing manual segmentation data from them.Corneal biomechanics play a fundamental role in the genesis and progression of corneal pathologies, such as keratoconus; in corneal remodeling after corneal surgery; and in affecting the measurement accuracy of glaucoma biomarkers, such as the intraocular pressure (IOP). Air-puff induced corneal deformation imaging reveals information highlighting normal and pathological corneal response to a non-contact mechanical excitation. However, current commercial systems are limited to monitoring corneal deformation only on one corneal meridian. Here, we present a novel custom-developed swept-source optical coherence tomography (SSOCT) system, coupled with a collinear air-puff excitation, capable of acquiring dynamic corneal deformation on multiple meridians. Backed by numerical simulations of corneal deformations, we propose two different scan patterns, aided by low coil impedance galvanometric scan mirrors that permit an appropriate compromise between temporal and spatial sampling of the corneal deformation profiles. We customized the air-puff module to provide an unobstructed SSOCT field of view and different peak pressures, air-puff durations, and distances to the eye. We acquired multi-meridian corneal deformation profiles (a) in healthy human eyes in vivo, (b) in porcine eyes ex vivo under varying controlled IOP, and (c) in a keratoconus-mimicking porcine eye ex vivo. We detected deformation asymmetries, as predicted by numerical simulations, otherwise missed on a single meridian that will substantially aid in corneal biomechanics diagnostics and pathology screening.Scaffold-based bone tissue engineering aims to develop 3D scaffolds that mimic the extracellular matrix to regenerate bone defects and damages. In this paper, we provide a laser speckle analysis to characterize the highly porous scaffold. The experimental procedure includes in situ acquisition of speckle patterns of the bone scaffold at different times under preserved environmental conditions, and follow-up statistical post-processing toward examining its internal activity. The activity and overall viscoelastic properties of scaffolds are expressed via several statistical parameters, and the variations in the computed parameters are attributed to time-varying activity of the samples during their internal substructure migration.