Third-harmonic generation imaging of breast tissue biopsies

Quantitative Classification of 3D Collagen Fiber Organization From Volumetric Images

Collagen fibers in biological tissues have a complex 3D organization containing rich information linked to tissue mechanical properties and are affected by mutations that lead to diseases. Quantitative assessment of this 3D collagen fiber organization could help to develop reliable biomechanical models and understand tissue structure-function relationships, which impact diagnosis and treatment of diseases or injuries. While there are advanced techniques for imaging collagen fibers, published methods for quantifying 3D collagen fiber organization have been sparse and give limited structural information which cannot distinguish a wide range of 3D organizations. In this article, we demonstrate an algorithm for quantitative classification of 3D collagen fiber organization. The algorithm first simulates five groups, or classifications, of fiber organization: unidirectional, crimped, disordered, two-fiber family, and helical. These five groups are widespread in natural tissues and are known to affect the tissue's mechanical properties. We use quantitative metrics based on features such as preferred 3D fiber orientation and spherical variance to differentiate each classification in a repeatable manner. We validate our algorithm by applying it to second-harmonic generation images of collagen fibers in tendon and cervix tissue that has been sectioned in specified orientations, and we find strong agreement between classification from simulated data and the physical fiber organization. Our approach provides insight for interpreting 3D fiber organization directly from volumetric images. This algorithm could be applied to other fiber-like structures that are not necessarily made of collagen.

Compact portable multiphoton microscopy reveals histopathological hallmarks of unprocessed lung tumor tissue in real time

During lung cancer operations a rapid and reliable assessment of tumor tissue can reduce operation time and potentially improve patient outcomes. We show that third harmonic generation (THG), second harmonic generation (SHG) and two-photon excited autofluorescence (2PEF) microscopy reveals relevant, histopathological information within seconds in fresh unprocessed human lung samples. We used a compact, portable microscope and recorded images within 1 to 3 seconds using a power of 5 mW. The generated THG/SHG/2PEF images of tumorous and nontumorous tissues are compared with the corresponding standard histology images, to identify alveolar structures and histopathological hallmarks. Cellular structures (tumor cells, macrophages and lymphocytes) (THG), collagen (SHG) and elastin (2PEF) are differentiated and allowed for rapid identification of carcinoid with solid growth pattern, minimally enlarged monomorphic cell nuclei with salt-and-pepper chromatin pattern, and adenocarcinoma with lipidic and micropapillary growth patterns. THG/SHG/2PEF imaging is thus a promising tool for clinical intraoperative assessment of lung tumor tissue.

Epithelium segmentation and automated Gleason grading of prostate cancer via deep learning in label-free multiphoton microscopic images

In the current clinical care practice, Gleason grading system is one of the most powerful prognostic predictors for prostate cancer (PCa). The grading system is based on the architectural pattern of cancerous epithelium in histological images. However, the standard procedure of histological examination often involves complicated tissue fixation and staining, which are time-consuming and may delay the diagnosis and surgery. In this study, label-free multiphoton microscopy (MPM) was used to acquire subcellular-resolution images of unstained prostate tissues. Then, a deep learning architecture (U-net) was introduced for epithelium segmentation of prostate tissues in MPM images. The obtained segmentation results were then merged with the original MPM images to train a classification network (AlexNet) for automated Gleason grading. The developed method achieved an overall pixel accuracy of 92.3% with a mean F1 score of 0.839 for epithelium segmentation. By merging the segmentation results with the MPM images, the accuracy of Gleason grading was improved from 72.42% to 81.13% in hold-out test set. Our results suggest that MPM in combination with deep learning holds the potential to be used as a fast and powerful clinical tool for PCa diagnosis.

Quantitative Third Harmonic Generation Microscopy for Assessment of Glioma in Human Brain Tissue

Distinguishing tumors from normal brain cells is important but challenging in glioma surgery due to the lack of clear interfaces between the two. The ability of label-free third harmonic generation (THG) microscopy in combination with automated image analysis to quantitatively detect glioma infiltration in fresh, unprocessed tissue in real time is assessed. The THG images reveal increased cellularity in grades II-IV glioma samples from 23 patients, as confirmed by subsequent hematoxylin and eosin histology. An automated image quantification workflow is presented for quantitative assessment of the imaged cellularity as a reflection of the degree of glioma invasion. The cellularity is validated in three ways: 1) Quantitative comparison of THG imaging with fluorescence microscopy of nucleus-stained samples demonstrates that THG reflects the true tissue cellularity. 2) Thresholding of THG cellularity differentiates normal brain from glioma infiltration, with 96.6% sensitivity and 95.5% specificity, in nearly perfect (93%) agreement with pathologists. 3) In one patient, a good correlation between THG cellularity and preoperative magnetic resonance and positron emission tomography imaging is demonstrated. In conclusion, quantitative real-time THG microscopy accurately assesses glioma infiltration in ex vivo human brain samples, and therefore holds strong potential for improving the accuracy of surgical resection.

Second and third harmonic generation microscopy visualizes key structural components in fresh unprocessed healthy human breast tissue

Real-time assessment of excised tissue may help to improve surgical results in breast tumor surgeries. Here, as a step towards this purpose, the potential of second and third harmonic generation (SHG, THG) microscopy is explored. SHG and THG are nonlinear optical microscopic techniques that do not require labeling of tissue to generate 3D images with intrinsic depth-sectioning at sub-cellular resolution. Until now, this technique had been applied on fixated breast tissue or to visualize the stroma only, whereas most tumors start in the lobules and ducts. Here, SHG/THG images of freshly excised unprocessed healthy human tissue are shown to reveal key breast components-lobules, ducts, fat tissue, connective tissue and blood vessels, in good agreement with hematoxylin and eosin histology. DNA staining of fresh unprocessed mouse breast tissue was performed to aid in the identification of cell nuclei in label-free THG images. Furthermore, 2- and 3-photon excited auto-fluorescence images of mouse and human tissue are collected for comparison. The SHG/THG imaging modalities generate high quality images of freshly excised tissue in less than a minute with an information content comparable to that of the gold standard, histopathology. Therefore, SHG/THG microscopy is a promising tool for real-time assessment of excised tissue during surgery.

Tensor regularized total variation for denoising of third harmonic generation images of brain tumors

Third harmonic generation (THG) microscopy shows great potential for instant pathology of brain tissue during surgery. However, the rich morphologies contained and the noise associated makes image restoration, necessary for quantification of the THG images, challenging. Anisotropic diffusion filtering (ADF) has been recently applied to restore THG images of normal brain, but ADF is hard-to-code, time-consuming and only reconstructs salient edges. This work overcomes these drawbacks by expressing ADF as a tensor regularized total variation model, which uses the Huber penalty and the L1 norm for tensor regularization and fidelity measurement, respectively. The diffusion tensor is constructed from the structure tensor of ADF yet the tensor decomposition is performed only in the non-flat areas. The resulting model is solved by an efficient and easy-to-code primal-dual algorithm. Tests on THG brain tumor images show that the proposed model has comparable denoising performance as ADF while it much better restores weak edges and it is up to 60% more time efficient.

Second-harmonic patterned polarization-analyzed reflection confocal microscopy of stromal collagen in benign and malignant breast tissues

We present the results of polarimetric analysis of collagen on varying pathologies of breast tissues using second-harmonic patterned polarization-analyzed reflection confocal (SPPARC) microscopy. Experiments are conducted on a breast tissue microarray having benign tissues (BT), malignant invasive lobular carcinoma (ILC), and benign stroma adjacent to the malignant tissues (called the benign adjacent tissue, or BAT). Stroma in BAT and ILC exhibit the largest parameter differences. We observe that stromal collagen readings in ILC show lower depolarization, lower diattenuation and higher linear degree-of-polarization values than stromal collagen in BAT. This suggests that the optical properties of collagen change most in the vicinity of tumors. A similar trend is also exhibited in the non-collagenous extrafibrillar matrix plus cells (EFMC) region. The three highlighted parameters show greatest sensitivity to changes in the polarization response of collagen between pathologies.

Quantitative analysis of the effect of environmental-scanning electron microscopy on collagenous tissues

Environmental-scanning electron microscopy (ESEM) is routinely applied to various biological samples due to its ability to maintain a wet environment while imaging; moreover, the technique obviates the need for sample coating. However, there is limited research carried out on electron-beam (e-beam) induced tissue damage resulting from using the ESEM. In this paper, we use quantitative second-harmonic generation (SHG) microscopy to examine the effects of e-beam exposure from the ESEM on collagenous tissue samples prepared as either fixed, frozen, wet or dehydrated. Quantitative SHG analysis of tissues, before and after ESEM e-beam exposure in low-vacuum mode, reveals evidence of cross-linking of collagen fibers, however there are no structural differences observed in fixed tissue. Meanwhile wet-mode ESEM appears to radically alter the structure from a regular fibrous arrangement to a more random fiber orientation. We also confirm that ESEM images of collagenous tissues show higher spatial resolution compared to SHG microscopy, but the relative tradeoff with collagen specificity reduces its effectiveness in quantifying collagen fiber organization. Our work provides insight on both the limitations of the ESEM for tissue imaging, and the potential opportunity to use as a complementary technique when imaging fine features in the non-collagenous regions of tissue samples.

Application of a reflective microscope objective for multiphoton microscopy

Reflective objectives (ROs) mitigate chromatic aberration across a broad wavelength range. Yet, a systematic performance characterisation of ROs has not been done. In this paper, we compare the performance of a 0.5 numerical-aperture (NA) reflective objective (RO) with a 0.55 NA standard glass objective (SO), using two-photon fluorescence (TPF) and second-harmonic generation (SHG). For experiments spanning ∼1 octave in the visible and NIR wavelengths, the SO leads to defocusing errors of 25-40% for TPF images of subdiffraction fluorescent beads and 10-12% for SHG images of collagen fibres. The corresponding error for the RO is ∼4% for both imaging modalities. This work emphasises the potential utility of ROs for multimodal multiphoton microscopy applications.