Direct three-dimensional segmentation of prostate glands with nnU-Net.

Significance: In recent years, we and others have developed non-destructive methods to obtain three-dimensional (3D) pathology datasets of clinical biopsies and surgical specimens. For prostate cancer risk stratification (prognostication), standard-of-care Gleason grading is based on examining the morphology of prostate glands in thin 2D sections. This motivates us to perform 3D segmentation of prostate glands in our 3D pathology datasets for the purposes of computational analysis of 3D glandular features that could offer improved prognostic performance. Aim: To facilitate prostate cancer risk assessment, we developed a computationally efficient and accurate deep learning model for 3D gland segmentation based on open-top light-sheet microscopy datasets of human prostate biopsies stained with a fluorescent analog of hematoxylin and eosin (H&E). Approach: For 3D gland segmentation based on our H&E-analog 3D pathology datasets, we previously developed a hybrid deep learning and computer vision-based pipeline, called image translation-assisted segmentation in 3D (ITAS3D), which required a complex two-stage procedure and tedious manual optimization of parameters. To simplify this procedure, we use the 3D gland-segmentation masks previously generated by ITAS3D as training datasets for a direct end-to-end deep learning-based segmentation model, nnU-Net. The inputs to this model are 3D pathology datasets of prostate biopsies rapidly stained with an inexpensive fluorescent analog of H&E and the outputs are 3D semantic segmentation masks of the gland epithelium, gland lumen, and surrounding stromal compartments within the tissue. Results: nnU-Net demonstrates remarkable accuracy in 3D gland segmentations even with limited training data. Moreover, compared with the previous ITAS3D pipeline, nnU-Net operation is simpler and faster, and it can maintain good accuracy even with lower-resolution inputs. Conclusions: Our trained DL-based 3D segmentation model will facilitate future studies to demonstrate the value of computational 3D pathology for guiding critical treatment decisions for patients with prostate cancer.

Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment.

Prostate cancer treatment decisions rely heavily on subjective visual interpretation [assigning Gleason patterns or International Society of Urological Pathology (ISUP) grade groups] of limited numbers of two-dimensional (2D) histology sections. Under this paradigm, interobserver variance is high, with ISUP grades not correlating well with outcome for individual patients, and this contributes to the over- and undertreatment of patients. Recent studies have demonstrated improved prognostication of prostate cancer outcomes based on computational analyses of glands and nuclei within 2D whole slide images. Our group has also shown that the computational analysis of three-dimensional (3D) glandular features, extracted from 3D pathology datasets of whole intact biopsies, can allow for improved recurrence prediction compared to corresponding 2D features. Here we seek to expand on these prior studies by exploring the prognostic value of 3D shape-based nuclear features in prostate cancer (e.g. nuclear size, sphericity). 3D pathology datasets were generated using open-top light-sheet (OTLS) microscopy of 102 cancer-containing biopsies extracted ex vivo from the prostatectomy specimens of 46 patients. A deep learning-based workflow was developed for 3D nuclear segmentation within the glandular epithelium versus stromal regions of the biopsies. 3D shape-based nuclear features were extracted, and a nested cross-validation scheme was used to train a supervised machine classifier based on 5-year biochemical recurrence (BCR) outcomes. Nuclear features of the glandular epithelium were found to be more prognostic than stromal cell nuclear features (area under the ROC curve [AUC] = 0.72 versus 0.63). 3D shape-based nuclear features of the glandular epithelium were also more strongly associated with the risk of BCR than analogous 2D features (AUC = 0.72 versus 0.62). The results of this preliminary investigation suggest that 3D shape-based nuclear features are associated with prostate cancer aggressiveness and could be of value for the development of decision-support tools. © 2023 The Pathological Society of Great Britain and Ireland.

Comprehensive Surface Histology of Fresh Resection Margins With Rapid Open-Top Light-Sheet (OTLS) Microscopy.

OBJECTIVE: For tumor resections, margin status typically correlates with patient survival but positive margin rates are generally high (up to 45% for head and neck cancer). Frozen section analysis (FSA) is often used to intraoperatively assess the margins of excised tissue, but suffers from severe under-sampling of the actual margin surface, inferior image quality, slow turnaround, and tissue destructiveness. METHODS: Here, we have developed an imaging workflow to generate en face histologic images of freshly excised surgical margin surfaces based on open-top light-sheet (OTLS) microscopy. Key innovations include (1) the ability to generate false-colored H&E-mimicking images of tissue surfaces stained for < 1 min with a single fluorophore, (2) rapid OTLS surface imaging at a rate of 15 min/cm2 followed by real-time post-processing of datasets within RAM at a rate of 5 min/cm2, and (3) rapid digital surface extraction to account for topological irregularities at the tissue surface. RESULTS: In addition to the performance metrics listed above, we show that the image quality generated by our rapid surface-histology method approaches that of gold-standard archival histology. CONCLUSION: OTLS microscopy has the feasibility to provide intraoperative guidance of surgical oncology procedures. SIGNIFICANCE: The reported methods can potentially improve tumor-resection procedures, thereby improving patient outcomes and quality of life.

Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning-Assisted Gland Analysis.

Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic grading by pathologists. Interpretation of these convoluted three-dimensional (3D) glandular structures via visual inspection of a limited number of two-dimensional (2D) histology sections is often unreliable, which contributes to the under- and overtreatment of patients. To improve risk assessment and treatment decisions, we have developed a workflow for nondestructive 3D pathology and computational analysis of whole prostate biopsies labeled with a rapid and inexpensive fluorescent analogue of standard hematoxylin and eosin (H&E) staining. This analysis is based on interpretable glandular features and is facilitated by the development of image translation-assisted segmentation in 3D (ITAS3D). ITAS3D is a generalizable deep learning-based strategy that enables tissue microstructures to be volumetrically segmented in an annotation-free and objective (biomarker-based) manner without requiring immunolabeling. As a preliminary demonstration of the translational value of a computational 3D versus a computational 2D pathology approach, we imaged 300 ex vivo biopsies extracted from 50 archived radical prostatectomy specimens, of which, 118 biopsies contained cancer. The 3D glandular features in cancer biopsies were superior to corresponding 2D features for risk stratification of patients with low- to intermediate-risk prostate cancer based on their clinical biochemical recurrence outcomes. The results of this study support the use of computational 3D pathology for guiding the clinical management of prostate cancer. SIGNIFICANCE: An end-to-end pipeline for deep learning-assisted computational 3D histology analysis of whole prostate biopsies shows that nondestructive 3D pathology has the potential to enable superior prognostic stratification of patients with prostate cancer.

Diagnosing 12 prostate needle cores within an hour of biopsy via open-top light-sheet microscopy.

SIGNIFICANCE: Processing and diagnosing a set of 12 prostate biopsies using conventional histology methods typically take at least one day. A rapid and accurate process performed while the patient is still on-site could significantly improve the patient's quality of life. AIM: We develop and assess the feasibility of a one-hour-to-diagnosis (1Hr2Dx) method for processing and providing a preliminary diagnosis of a set of 12 prostate biopsies. APPROACH: We developed a fluorescence staining, optical clearing, and 3D open-top light-sheet microscopy workflow to enable 12 prostate needle core biopsies to be processed and diagnosed within an hour of receipt. We analyzed 44 biopsies by the 1Hr2Dx method, which does not consume tissue. The biopsies were then processed for routine, slide-based 2D histology. Three pathologists independently evaluated the 3D 1Hr2Dx and 2D slide-based datasets in a blinded, randomized fashion. Turnaround times were recorded, and the accuracy of our method was compared with gold-standard slide-based histology. RESULTS: The average turnaround time for tissue processing, imaging, and diagnosis was 44.5 min. The sensitivity and specificity of 1Hr2Dx in diagnosing cancer were both >90 % . CONCLUSIONS: The 1Hr2Dx method has the potential to improve patient care by providing an accurate preliminary diagnosis within an hour of biopsy.

FalseColor-Python: A rapid intensity-leveling and digital-staining package for fluorescence-based slide-free digital pathology.

Slide-free digital pathology techniques, including nondestructive 3D microscopy, are gaining interest as alternatives to traditional slide-based histology. In order to facilitate clinical adoption of these fluorescence-based techniques, software methods have been developed to convert grayscale fluorescence images into color images that mimic the appearance of standard absorptive chromogens such as hematoxylin and eosin (H&E). However, these false-coloring algorithms often require manual and iterative adjustment of parameters, with results that can be inconsistent in the presence of intensity nonuniformities within an image and/or between specimens (intra- and inter-specimen variability). Here, we present an open-source (Python-based) rapid intensity-leveling and digital-staining package that is specifically designed to render two-channel fluorescence images (i.e. a fluorescent analog of H&E) to the traditional H&E color space for 2D and 3D microscopy datasets. However, this method can be easily tailored for other false-coloring needs. Our package offers (1) automated and uniform false coloring in spite of uneven staining within a large thick specimen, (2) consistent color-space representations that are robust to variations in staining and imaging conditions between different specimens, and (3) GPU-accelerated data processing to allow these methods to scale to large datasets. We demonstrate this platform by generating H&E-like images from cleared tissues that are fluorescently imaged in 3D with open-top light-sheet (OTLS) microscopy, and quantitatively characterizing the results in comparison to traditional slide-based H&E histology.

Multi-immersion open-top light-sheet microscope for high-throughput imaging of cleared tissues.

Recent advances in optical clearing and light-sheet microscopy have provided unprecedented access to structural and molecular information from intact tissues. However, current light-sheet microscopes have imposed constraints on the size, shape, number of specimens, and compatibility with various clearing protocols. Here we present a multi-immersion open-top light-sheet microscope that enables simple mounting of multiple specimens processed with a variety of clearing protocols, which will facilitate wide adoption by preclinical researchers and clinical laboratories. In particular, the open-top geometry provides unsurpassed versatility to interface with a wide range of accessory technologies in the future.

Rapid pathology of lumpectomy margins with open-top light-sheet (OTLS) microscopy.

Open-top light-sheet microscopy is a technique that can potentially enable rapid ex vivo inspection of large tissue surfaces and volumes. Here, we have optimized an open-top light-sheet (OTLS) microscope and image-processing workflow for the comprehensive examination of surgical margin surfaces, and have also developed a novel fluorescent analog of H&E staining that is robust for staining fresh unfixed tissues. Our tissue-staining method can be achieved within 2.5 minutes followed by OTLS microscopy of lumpectomy surfaces at a rate of up to 1.5 cm2/minute. An image atlas is presented to show that OTLS image quality surpasses that of intraoperative frozen sectioning and can approximate that of gold-standard H&E histology of formalin-fixed paraffin-embedded (FFPE) tissues. Qualitative evidence indicates that these intraoperative methods do not interfere with downstream post-operative H&E histology and immunohistochemistry. These results should facilitate the translation of OTLS microscopy for intraoperative guidance of lumpectomy and other surgical oncology procedures.

Microscopy with ultraviolet surface excitation for wide-area pathology of breast surgical margins.

Intraoperative assessment of breast surgical margins will be of value for reducing the rate of re-excision surgeries for lumpectomy patients. While frozen-section histology is used for intraoperative guidance of certain cancers, it provides limited sampling of the margin surface (typically <1 % of the margin) and is inferior to gold-standard histology, especially for fatty tissues that do not freeze well, such as breast specimens. Microscopy with ultraviolet surface excitation (MUSE) is a nondestructive superficial optical-sectioning technique that has the potential to enable rapid, high-resolution examination of excised margin surfaces. Here, a MUSE system is developed with fully automated sample translation to image fresh tissue surfaces over large areas and at multiple levels of defocus, at a rate of ∼5 min / cm2. Surface extraction is used to improve the comprehensiveness of surface imaging, and 3-D deconvolution is used to improve resolution and contrast. In addition, an improved fluorescent analog of conventional H&E staining is developed to label fresh tissues within ∼5 min for MUSE imaging. We compare the image quality of our MUSE system with both frozen-section and conventional H&E histology, demonstrating the feasibility to provide microscopic visualization of breast margin surfaces at speeds that are relevant for intraoperative use.

Utilizing a Pyrazine-Containing Aggregation-Induced Emission Luminogen as an Efficient Photosensitizer for Imaging-Guided Two-Photon Photodynamic Therapy.

The development of novel photosensitizers with aggregation-induced emission (AIE) characteristics has aroused tremendous interest, because it could combine efficient bioimaging with precise photodynamic therapy, which would thus dramatically promote applications in biomedical treatment. Among various AIE luminogens (AIEgens), heterocycle-containing molecules are highly promising for this usage because of their high photostability and tunable electronic properties. In this work, a pyrazine-containing AIEgen with a dicyanopyrazine moiety as an electron acceptor and a triphenylamine unit as an electron donor was chosen for study. The V-shaped donor-π-acceptor-π-donor structure of the AIEgen endowed its nanoparticles with excellent nonlinear optical properties for two-photon cell imaging under near-infrared laser excitation. Also, under the same excitation, the nanoparticles could produce reactive oxygen species and further kill the cells efficiently and accurately. The present work thus presents a pyrazine-containing AIEgen as a new photosensitizer for imaging-guided two-photon photodynamic therapy and gives more opportunities for deep-tissue treatment of cancer in future research.

Sparse Parallel MRI Based on Accelerated Operator Splitting Schemes.

Recently, the sparsity which is implicit in MR images has been successfully exploited for fast MR imaging with incomplete acquisitions. In this paper, two novel algorithms are proposed to solve the sparse parallel MR imaging problem, which consists of l1 regularization and fidelity terms. The two algorithms combine forward-backward operator splitting and Barzilai-Borwein schemes. Theoretically, the presented algorithms overcome the nondifferentiable property in l1 regularization term. Meanwhile, they are able to treat a general matrix operator that may not be diagonalized by fast Fourier transform and to ensure that a well-conditioned optimization system of equations is simply solved. In addition, we build connections between the proposed algorithms and the state-of-the-art existing methods and prove their convergence with a constant stepsize in Appendix. Numerical results and comparisons with the advanced methods demonstrate the efficiency of proposed algorithms.

Mechanistic Study of a Photocatalyzed C-S Bond Formation Involving Alkyl/Aryl Thiosulfate.

This study presents thioether construction involving alkyl/aryl thiosulfates and diazonium salt catalyzed by visible-light-excited [Ru(bpy)3 Cl2 ] at room temperature in 44-86 % yield. Electron paramagnetic resonance studies found that thiosulfate radical formation was promoted by K2 CO3 . Conversely, radicals generated from BnSH or BnSSBn (Bn=benzyl) were clearly suppressed, demonstrating the special property of thiosulfate in this system. Transient absorption spectra confirmed the electron-transfer process between [Ru(bpy)3 Cl2 ] and 4-MeO-phenyl diazonium salt, which occurred with a rate constant of 1.69×10(9) M(-1) s(-1) . The corresponding radical trapping product was confirmed by X-ray diffraction. The full reaction mechanism was determined together with emission quenching data. Furthermore, this system efficiently avoided the over-oxidation of sulfide caused by H2 O in the photoexcited system containing Ru(2+) . Both aryl and heteroaryl diazonium salts with various electronic properties were investigated for synthetic compatibility. Both alkyl- and aryl-substituted thiosulfates could be used as substrates. Notably, pharmaceutical derivatives afforded late-stage sulfuration smoothly under mild conditions.

GcsDecolor: Gradient Correlation Similarity for Efficient Contrast Preserving Decolorization.

This paper presents a novel gradient correlation similarity (Gcs) measure-based decolorization model for faithfully preserving the appearance of the original color image. Contrary to the conventional data-fidelity term consisting of gradient error-norm-based measures, the newly defined Gcs measure calculates the summation of the gradient correlation between each channel of the color image and the transformed grayscale image. Two efficient algorithms are developed to solve the proposed model. On one hand, due to the highly nonlinear nature of Gcs measure, a solver consisting of the augmented Lagrangian and alternating direction method is adopted to deal with its approximated linear parametric model. The presented algorithm exhibits excellent iterative convergence and attains superior performance. On the other hand, a discrete searching solver is proposed by determining the solution with the minimum function value from the linear parametric model-induced candidate images. The non-iterative solver has advantages in simplicity and speed with only several simple arithmetic operations, leading to real-time computational speed. In addition, it is very robust with respect to the parameter and candidates. Extensive experiments under a variety of test images and a comprehensive evaluation against existing state-of-the-art methods consistently demonstrate the potential of the proposed model and algorithms.