A novel method for tissue segmentation in high-resolution H&E-stained histopathological whole-slide images

The Combined Effect of High-Intensity Interval Training and Time-Restricted Feeding on the AKT-IGF-1-mTOR Signaling Pathway in the Muscle Tissue of Type 2 Diabetic Rats

Background/Objectives: High-intensity interval training (HIIT) and time-restricted feeding (TRF) have shown potential in enhancing glucose metabolism, increasing insulin sensitivity, and promoting muscle health. This study investigates the combined effects of HIIT and TRF on the AKT-IGF-1-mTOR signaling pathway in the muscle tissue of type 2 diabetic (T2D) rats. Methods: 42 male Wistar rats (4-5 weeks of age) were included in the study. The animals were randomly divided into two groups: 1. Standard diet (SD) non-diabetic (n = 7) and 2. High-fat diet (HFD n = 35) for 4 weeks. T2D was induced by intraperitoneal injection (IP) of streptozotocin (STZ) at 35 mg/kg. Animals with blood glucose levels ≥ 250 mg/dL were considered diabetic. Diabetic rats were randomly divided into five groups (n = 7): 1. Diabetes-HIIT (D-HIIT), 2. Diabetes-TRF (D-T), 3. Diabetes-combined TRF and HIIT (D-T+HIIT), 4. Diabetes-Untreated Control (D), and 5. Diabetes with metformin (D-MET). The HIIT protocol and TRF regimen were followed for 10 weeks. Muscle tissue was collected for histological analysis, and the expression of proteins related to the AKT-IGF-1-mTOR pathway was measured. Results: Blood glucose levels, insulin resistance (IR), and markers of muscle degradation were significantly improved in the D-T+HIIT and D-MET groups compared to the non-diabetes group. Furthermore, the activation of the AKT and mTOR signaling proteins, as well as increased IGF-1 expression, was significantly elevated in the D-T+HIIT group compared to the diabetic control group and other treatment groups, and approached levels observed in the non-diabetes group. Additionally, muscle fiber size and overall tissue structure were improved in the treatment groups, particularly in the D-T+HIIT group. Conclusions: The combination of HIIT and TRF appears to offer superior benefits in improving muscle protein synthesis, and glucose regulation in T2D rats, as compared to either HIIT or TRF alone. These findings highlight the potential of this combined approach for addressing muscle-related complications in T2D.

Optomechanical Properties of Swine Skin Tissue Treated With a Nontoxic Optical Clearing Agent

This study evaluated the optomechanical and structural properties of individual macroscopic layers in swine skin tissues treated with a nontoxic optical clearing agent. The clearing agent was prepared by dissolving 2,2'-thiodiethanol in a phosphate-buffered solution and applied for up to 6 days. Prolonged clearing increased both the total and unscattered transmittance. Peaks associated with deoxygenated hemoglobin, oxygenated hemoglobin, and water showed marked reduction with extended clearing times. Histopathological examination revealed no significant structural alterations. A clearing time of approximately 4-5 days is recommended for optimal imaging, as it minimizes changes in mechanical properties. These findings support the use of optical clearing in deep tissue imaging, particularly when preserving macroscopic mechanical properties is essential, despite the inherent limitation of sample opacity.

Prediction of biochemical prostate cancer recurrence from any Gleason score using robust tissue structure and clinically available information

Biopsy information and protein Prostate-Specific Antigen (PSA) levels are the most robust information available to oncologists worldwide to diagnose and decide therapies for prostate cancer patients. However, prostate cancer presents a high risk of recurrence, and the technologies used to evaluate it demand more complex resources. This paper aims to predict Biochemical Recurrence (BCR) based on Whole Slide Images (WSI) of biopsies, Gleason scores, and PSA levels. A U-net model was used to segment phenotypic features and trained on images from the Prostate Cancer Grade Assessment (PANDA) database to segment tumorous regions from pre-processed and scored WSI of biopsies. Then, the model was tested on data from publicly available repositories achieving an Intersection over Union of 87%. Tissue features, Gleason scores, and PSA levels provided high accuracy and precision in classifying patients according to their risk of presenting recurrence, for any Gleason score sampled. The trained classifier model demonstrated a 79.2% relative accuracy, and a precision of 69.7% for patients experiencing recurrences before 24 months. Our results provide a robust, cost-efficient approach using already available information to predict the risk of BCR.

A Modified Deep Semantic Segmentation Model for Analysis of Whole Slide Skin Images

Automated segmentation of biomedical image has been recognized as an important step in computer-aided diagnosis systems for detection of abnormalities. Despite its importance, the segmentation process remains an open challenge due to variations in color, texture, shape diversity and boundaries. Semantic segmentation often requires deeper neural networks to achieve higher accuracy, making the segmentation model more complex and slower. Due to the need to process a large number of biomedical images, more efficient and cheaper image processing techniques for accurate segmentation are needed. In this article, we present a modified deep semantic segmentation model that utilizes the backbone of EfficientNet-B3 along with UNet for reliable segmentation. We trained our model on Non-melanoma skin cancer segmentation for histopathology dataset to divide the image in 12 different classes for segmentation. Our method outperforms the existing literature with an increase in average class accuracy from 79 to 83%. Our approach also shows an increase in overall accuracy from 85 to 94%.

Development of an Interpretable Deep Learning Model for Pathological Tumor Response Assessment After Neoadjuvant Therapy

BACKGROUND

Neoadjuvant therapy followed by surgery has become the standard of care for locally advanced esophageal squamous cell carcinoma (ESCC) and accurate pathological response assessment is critical to assess the therapeutic efficacy. However, it can be laborious and inconsistency between different observers may occur. Hence, we aim to develop an interpretable deep-learning model for efficient pathological response assessment following neoadjuvant therapy in ESCC.

METHODS

This retrospective study analyzed 337 ESCC resection specimens from 2020-2021 at the Pudong-Branch (Cohort 1) and 114 from 2021-2022 at the Puxi-Branch (External Cohort 2) of Fudan University Shanghai Cancer Center. Whole slide images (WSIs) from these two cohorts were generated using different scanning machines to test the ability of the model in handling color variations. Four pathologists independently assessed the pathological response. The senior pathologists annotated tumor beds and residual tumor percentages on WSIs to determine consensus labels. Furthermore, 1850 image patches were randomly extracted from Cohort 1 WSIs and binarily classified for tumor viability. A deep-learning model employing knowledge distillation was developed to automatically classify positive patches for each WSI and estimate the viable residual tumor percentages. Spatial heatmaps were output for model explanations and visualizations.

RESULTS

The approach achieved high concordance with pathologist consensus, with an R^2 of 0.8437, a RAcc_0.1 of 0.7586, a RAcc_0.3 of 0.9885, which were comparable to two senior pathologists (R^2 of 0.9202/0.9619, RAcc_0.1 of 8506/0.9425, RAcc_0.3 of 1.000/1.000) and surpassing two junior pathologists (R^2 of 0.5592/0.5474, RAcc_0.1 of 0.5287/0.5287, RAcc_0.3 of 0.9080/0.9310). Visualizations enabled the localization of residual viable tumor to augment microscopic assessment.

CONCLUSION

This work illustrates deep learning's potential for assisting pathological response assessment. Spatial heatmaps and patch examples provide intuitive explanations of model predictions, engendering clinical trust and adoption (Code and data will be available at https://github.com/WinnieLaugh/ESCC_Percentage once the paper has been conditionally accepted). Integrating interpretable computational pathology could help enhance the efficiency and consistency of tumor response assessment and empower precise oncology treatment decisions.

Enhancing wound healing and adhesion through dopamine-assisted gelatin-silica hybrid dressings

Gelatin, widely employed in hydrogel dressings, faces limitations when used in high fluid environments, hindering effective material adhesion to wound sites and subsequently reducing treatment efficacy. The rapid degradation of conventional hydrogels often results in breakdown before complete wound healing. Thus, there is a pressing need for the development of durable adhesive wound dressings. In this study, 3-glycidoxypropyltrimethoxysilane (GPTMS) was utilized as a coupling agent to create gelatin-silica hybrid (G-H) dressings through the sol-gel method. The coupling reaction established covalent bonds between gelatin and silica networks, enhancing structural stability. Dopamine (DP) was introduced to this hybrid (G-H-D) dressing to further boost adhesiveness. The efficacy of the dressings for wound management was assessed through in-vitro and in-vivo tests, along with ex-vivo bioadhesion testing on pig skin. Tensile bioadhesion tests demonstrated that the G-H-D material exhibited approximately 2.5 times greater adhesion to soft tissue in wet conditions compared to pure gelatin. Moreover, in-vitro and in-vivo wound healing experiments revealed a significant increase in wound healing rates. Consequently, this material shows promise as a viable option for use as a moist wound dressing.

Dual contrastive learning based image-to-image translation of unstained skin tissue into virtually stained H&E images

Staining is a crucial step in histopathology that prepares tissue sections for microscopic examination. Hematoxylin and eosin (H&E) staining, also known as basic or routine staining, is used in 80% of histopathology slides worldwide. To enhance the histopathology workflow, recent research has focused on integrating generative artificial intelligence and deep learning models. These models have the potential to improve staining accuracy, reduce staining time, and minimize the use of hazardous chemicals, making histopathology a safer and more efficient field. In this study, we introduce a novel three-stage, dual contrastive learning-based, image-to-image generative (DCLGAN) model for virtually applying an "H&E stain" to unstained skin tissue images. The proposed model utilizes a unique learning setting comprising two pairs of generators and discriminators. By employing contrastive learning, our model maximizes the mutual information between traditional H&E-stained and virtually stained H&E patches. Our dataset consists of pairs of unstained and H&E-stained images, scanned with a brightfield microscope at 20 × magnification, providing a comprehensive set of training and testing images for evaluating the efficacy of our proposed model. Two metrics, Fréchet Inception Distance (FID) and Kernel Inception Distance (KID), were used to quantitatively evaluate virtual stained slides. Our analysis revealed that the average FID score between virtually stained and H&E-stained images (80.47) was considerably lower than that between unstained and virtually stained slides (342.01), and unstained and H&E stained (320.4) indicating a similarity virtual and H&E stains. Similarly, the mean KID score between H&E stained and virtually stained images (0.022) was significantly lower than the mean KID score between unstained and H&E stained (0.28) or unstained and virtually stained (0.31) images. In addition, a group of experienced dermatopathologists evaluated traditional and virtually stained images and demonstrated an average agreement of 78.8% and 90.2% for paired and single virtual stained image evaluations, respectively. Our study demonstrates that the proposed three-stage dual contrastive learning-based image-to-image generative model is effective in generating virtual stained images, as indicated by quantified parameters and grader evaluations. In addition, our findings suggest that GAN models have the potential to replace traditional H&E staining, which can reduce both time and environmental impact. This study highlights the promise of virtual staining as a viable alternative to traditional staining techniques in histopathology.

Ensemble-based multi-tissue classification approach of colorectal cancer histology images using a novel hybrid deep learning framework

Colorectal cancer (CRC) is the second leading cause of cancer death in the world, so digital pathology is essential for assessing prognosis. Due to the increasing resolution and quantity of whole slide images (WSIs), as well as the lack of annotated information, previous methodologies cannot be generalized as effective decision-making systems. Since deep learning (DL) methods can handle large-scale applications, they can provide a viable alternative to histopathology image (HI) analysis. DL architectures, however, may not be sufficient to classify CRC tissues based on anatomical histopathology data. A dilated ResNet (dResNet) structure and attention module are used to generate deep feature maps in order to classify multiple tissues in HIs. In addition, neighborhood component analysis (NCA) overcomes the constraint of computational complexity. Data is fed into a deep support vector machine (SVM) based on an ensemble learning algorithm called DeepSVM after the features have been selected. CRC-5000 and NCT-CRC-HE-100 K datasets were analyzed to validate and test the hybrid procedure. We demonstrate that the hybrid model achieves 98.75% and 99.76% accuracy on CRC datasets. The results showed that only pathologists' labels could successfully classify unseen WSIs. Furthermore, the hybrid deep learning method outperforms state-of-the-art approaches in terms of computational efficiency and time. Using the proposed mechanism for tissue analysis, it will be possible to correctly predict CRC based on accurate pathology image classification.

Classifying Malignancy in Prostate Glandular Structures from Biopsy Scans with Deep Learning

Histopathological classification in prostate cancer remains a challenge with high dependence on the expert practitioner. We develop a deep learning (DL) model to identify the most prominent Gleason pattern in a highly curated data cohort and validate it on an independent dataset. The histology images are partitioned in tiles (14,509) and are curated by an expert to identify individual glandular structures with assigned primary Gleason pattern grades. We use transfer learning and fine-tuning approaches to compare several deep neural network architectures that are trained on a corpus of camera images (ImageNet) and tuned with histology examples to be context appropriate for histopathological discrimination with small samples. In our study, the best DL network is able to discriminate cancer grade (GS3/4) from benign with an accuracy of 91%, F₁-score of 0.91 and AUC 0.96 in a baseline test (52 patients), while the cancer grade discrimination of the GS3 from GS4 had an accuracy of 68% and AUC of 0.71 (40 patients).

Accurate diagnostic tissue segmentation and concurrent disease subtyping with small datasets

Purpose

To provide a flexible, end-to-end platform for visually distinguishing diseased from undiseased tissue in a medical image, in particular pathology slides, and classifying diseased regions by subtype. Highly accurate results are obtained using small training datasets and reduced-scale source images that can be easily shared.

Approach

An ensemble of lightweight convolutional neural networks (CNNs) is trained on different subsets of images derived from a relatively small number of annotated whole-slide histopathology images (WSIs). The WSIs are first reduced in scale in a manner that preserves anatomic features critical to analysis while also facilitating convenient handling and storage. The segmentation and subtyping tasks are performed sequentially on the reduced-scale images using the same basic workflow: generating and sifting tiles from the image, then classifying each tile with an ensemble of appropriately trained CNNs. For segmentation, the CNN predictions are combined using a function to favor a selected similarity metric, and a mask or map for a a candidate image is produced from tiles whose combined predictions exceed a decision boundary. For subtyping, the resulting mask is applied to the candidate image, and new tiles are derived from the unoccluded regions. These are classified by the subtyping CNNs to produce an overall subtype prediction.

Results and conclusion

This approach was applied successfully to two very different datasets of large WSIs, one (PAIP2020) involving multiple subtypes of colorectal cancer and the other (CAMELYON16) single-type breast cancer metastases. Scored using standard similarity metrics, the segmentations outperformed more complex models typifying the state of the art.

Efficient Staining-Invariant Nuclei Segmentation Approach Using Self-Supervised Deep Contrastive Network

Existing nuclei segmentation methods face challenges with hematoxylin and eosin (H&E) whole slide imaging (WSI) due to the variations in staining methods and nuclei shapes and sizes. Most existing approaches require a stain normalization step that may cause losing source information and fail to handle the inter-scanner feature instability problem. To mitigate these issues, this article proposes an efficient staining-invariant nuclei segmentation method based on self-supervised contrastive learning and an effective weighted hybrid dilated convolution (WHDC) block. In particular, we propose a staining-invariant encoder (SIE) that includes convolution and transformers blocks. We also propose the WHDC block allowing the network to learn multi-scale nuclei-relevant features to handle the variation in the sizes and shapes of nuclei. The SIE network is trained on five unlabeled WSIs datasets using self-supervised contrastive learning and then used as a backbone for the downstream nuclei segmentation network. Our method outperforms existing approaches in challenging multiple WSI datasets without stain color normalization.

A generalized framework of feature learning enhanced convolutional neural network for pathology-image-oriented cancer diagnosis

In this paper, a feature learning enhanced convolutional neural network (FLE-CNN) is proposed for cancer detection from histopathology images. To build a highly generalized computer-aided diagnosis (CAD) system, an information refinement unit employing depth- and point-wise convolutions is meticulously designed, where a dual-domain attention mechanism is adopted to focus primarily on the important areas. By deploying a residual fusion unit, context information is further integrated to extract highly discriminative features with strong representation ability. Experimental results demonstrate the merits of the proposed FLE-CNN in terms of feature extraction, which has achieved average sensitivity, specificity, precision, accuracy and F1 score of 0.9992, 0.9998, 0.9992, 0.9997 and 0.9992 in a five-class cancer detection task, and in comparison to some other advanced deep learning models, above indicators have been improved by 1.23%, 0.31%, 1.24%, 0.5% and 1.26%, respectively. Moreover, the proposed FLE-CNN provides satisfactory results in three important diagnosis, which further validates that FLE-CNN is a competitive CAD model with high generalization ability.

Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review

Over the last few decades, computer-aided diagnosis systems have become a part of clinical practice. They have the potential to assist clinicians in daily diagnostic tasks. The image processing techniques are fast, repeatable, and robust, which helps physicians to detect, classify, segment, and measure various structures. The recent rapid development of computer methods for high-frequency ultrasound image analysis opens up new diagnostic paths in dermatology, allergology, cosmetology, and aesthetic medicine. This paper, being the first in this area, presents a research overview of high-frequency ultrasound image processing techniques, which have the potential to be a part of computer-aided diagnosis systems. The reviewed methods are categorized concerning the application, utilized ultrasound device, and image data-processing type. We present the bridge between diagnostic needs and already developed solutions and discuss their limitations and future directions in high-frequency ultrasound image analysis. A search was conducted of the technical literature from 2005 to September 2022, and in total, 31 studies describing image processing methods were reviewed. The quantitative and qualitative analysis included 39 algorithms, which were selected as the most effective in this field. They were completed by 20 medical papers and define the needs and opportunities for high-frequency ultrasound application and CAD development.

SCALE SPACE DETECTOR FOR ANALYZING SPATIOTEMPORAL VENTRICULAR CONTRACTILITY AND NUCLEAR MORPHOGENESIS IN ZEBRAFISH

In vivo quantitative assessment of structural and functional biomarkers is essential for characterizing the pathophysiology of congenital disorders. In this regard, fixed tissue analysis has offered revolutionary insights into the underlying cellular architecture. However, histological analysis faces major drawbacks with respect to lack of spatiotemporal sampling and tissue artifacts during sample preparation. This study demonstrates the potential of light sheet fluorescence microscopy (LSFM) as a non-invasive, 4D (3days + time) optical sectioning tool for revealing cardiac mechano-transduction in zebrafish. Furthermore, we have described the utility of a scale and size-invariant feature detector, for analyzing individual morphology of fused cardiomyocyte nuclei and characterizing zebrafish ventricular contractility.

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Cardiac defect genes in humans have corresponding zebrafish orthologs

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Light sheet modality is very effective for non-invasive, 4D modeling of zebrafish

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Hessian detector is robust to varying nuclei scales and geometric transformations

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Watershed filter is effective for separating fused cellular volumes

A review: The detection of cancer cells in histopathology based on machine vision

Machine vision is being employed in defect detection, size measurement, pattern recognition, image fusion, target tracking and 3D reconstruction. Traditional cancer detection methods are dominated by manual detection, which wastes time and manpower, and heavily relies on the pathologists' skill and work experience. Therefore, these manual detection approaches are not convenient for the inheritance of domain knowledge, and are not suitable for the rapid development of medical care in the future. The emergence of machine vision can iteratively update and learn the domain knowledge of cancer cell pathology detection to achieve automated, high-precision, and consistent detection. Consequently, this paper reviews the use of machine vision to detect cancer cells in histopathology images, as well as the benefits and drawbacks of various detection approaches. First, we review the application of image preprocessing and image segmentation in histopathology for the detection of cancer cells, and compare the benefits and drawbacks of different algorithms. Secondly, for the characteristics of histopathological cancer cell images, the research progress of shape, color and texture features and other methods is mainly reviewed. Furthermore, for the classification methods of histopathological cancer cell images, the benefits and drawbacks of traditional machine vision approaches and deep learning methods are compared and analyzed. Finally, the above research is discussed and forecasted, with the expected future development tendency serving as a guide for future research.

High throughput whole-slide scanning to enable large-scale data repository building

Digital pathology and artificial intelligence (AI) rely on digitization of patient material as a necessary first step. AI development benefits from large sample sizes and diverse cohorts, and therefore efforts to digitize glass slides must meet these needs in an efficient and cost-effective manner. Technical innovation in whole-slide imaging has enabled high throughput slide scanning through the coordinated increase in scanner capacity, speed, and automation. Combining these hardware innovations with automated informatics approaches has enabled more efficient workflows and the opportunity to provide higher quality imaging data using fewer personnel. Here we review several practical considerations for deploying high throughput scanning and we present strategies to increase efficiency with a focus on quality. Finally, we review remaining challenges and issue a call to vendors to innovate in the areas of automation and quality control in order to make high throughput scanning realizable to laboratories with limited resources. This article is protected by copyright. All rights reserved.

Development of Planar Illumination Strategies for Solving Mysteries in the Sub-Cellular Realm

Optical microscopy has vastly expanded the frontiers of structural and functional biology, due to the non-invasive probing of dynamic volumes in vivo. However, traditional widefield microscopy illuminating the entire field of view (FOV) is adversely affected by out-of-focus light scatter. Consequently, standard upright or inverted microscopes are inept in sampling diffraction-limited volumes smaller than the optical system's point spread function (PSF). Over the last few decades, several planar and structured (sinusoidal) illumination modalities have offered unprecedented access to sub-cellular organelles and 4D (3D + time) image acquisition. Furthermore, these optical sectioning systems remain unaffected by the size of biological samples, providing high signal-to-noise (SNR) ratios for objective lenses (OLs) with long working distances (WDs). This review aims to guide biologists regarding planar illumination strategies, capable of harnessing sub-micron spatial resolution with a millimeter depth of penetration.

A high dose of estrogen can improve renal ischemia-reperfusion-induced pulmonary injury in ovariectomized female rats

Renal ischemia-reperfusion injury (RIRI) as a pathological process induces remote organ injury such as lung complications and it is regulated in a hormone-dependent manner. This study investigates the effect of estrogen on RIR-induced pulmonary injury in ovariectomized (OV) rats. A total of 60 female Wistar rats were divided into six groups: (i) intact sham, (ii) OV sham, (iii) OV sham + estradiol valerate (E), (iv) intact ischemia, (v) OV ischemia, and (vi) OV ischemia + E. Bilateral ischemia was performed for 45 min in all groups except sham. Before the ischemia, OV groups received an intramuscular (i.m.) injection of E. After reperfusion, blood samples were collected for serum analysis and kidney and lung tissue were separated for pathological experiment and malondialdehyde (MDA) and nitrite measurement. The left lung was weighed to measure pulmonary edema. Estrogen deficiency caused a greater increase in blood urea nitrogen and creatinine levels during IRI. Ischemia reduced nitrite of serum and lung tissue. The increased level of MDA during ischemia, returned to normal levels via estrogen injection. The severity of renal and lung damage in ischemic groups increased significantly, and estrogen improved this injury. Estrogen as an antioxidant agent can reduce oxidative stress and may improve renal function and ameliorating lung damage caused by RIR.

Deep Convolutional Neural Networks Detect Tumor Genotype from Pathological Tissue Images in Gastrointestinal Stromal Tumors

Gastrointestinal stromal tumors (GIST) are common mesenchymal tumors, and their effective treatment depends upon the mutational subtype of the KIT/PDGFRA genes. We established deep convolutional neural network (DCNN) models to rapidly predict drug-sensitive mutation subtypes from images of pathological tissue. A total of 5153 pathological images of 365 different GISTs from three different laboratories were collected and divided into training and validation sets. A transfer learning mechanism based on DCNN was used with four different network architectures, to identify cases with drug-sensitive mutations. The accuracy ranged from 87% to 75%. Cross-institutional inconsistency, however, was observed. Using gray-scale images resulted in a 7% drop in accuracy (accuracy 80%, sensitivity 87%, specificity 73%). Using images containing only nuclei (accuracy 81%, sensitivity 87%, specificity 73%) or cytoplasm (accuracy 79%, sensitivity 88%, specificity 67%) produced 6% and 8% drops in accuracy rate, respectively, suggesting buffering effects across subcellular components in DCNN interpretation. The proposed DCNN model successfully inferred cases with drug-sensitive mutations with high accuracy. The contribution of image color and subcellular components was also revealed. These results will help to generate a cheaper and quicker screening method for tumor gene testing.

The Mandible Ameliorates Facial Allograft Rejection and Is Associated with the Development of Regulatory T Cells and Mixed Chimerism

Vascularized composite allografts contain various tissue components and possess relative antigenicity, eliciting different degrees of alloimmune responses. To investigate the strategies for achieving facial allograft tolerance, we established a mouse hemiface transplant model, including the skin, muscle, mandible, mucosa, and vessels. However, the immunomodulatory effects of the mandible on facial allografts remain unclear. To understand the effects of the mandible on facial allograft survival, we compared the diversities of different facial allograft-elicited alloimmunity between a facial osteomyocutaneous allograft (OMC), including skin, muscle, oral mucosa, and vessels, and especially the mandible, and a myocutaneous allograft (MC) including the skin, muscle, oral mucosa, and vessels, but not the mandible. The different facial allografts of a BALB/c donor were transplanted into a heterotopic neck defect on fully major histocompatibility complex-mismatched C57BL/6 mice. The allogeneic OMC (Allo-OMC) group exhibited significant prolongation of facial allograft survival compared to the allogeneic MC group, both in the presence and absence of FK506 immunosuppressive drugs. With the use of FK506 monotherapy (2 mg/kg) for 21 days, the allo-OMC group, including the mandible, showed prolongation of facial allograft survival of up to 65 days, whereas the myocutaneous allograft, without the mandible, only survived for 34 days. The Allo-OMC group also displayed decreased lymphocyte infiltration into the facial allograft. Both groups showed similar percentages of B cells, T cells, natural killer cells, macrophages, and dendritic cells in the blood, spleen, and lymph nodes. However, a decrease in pro-inflammatory T helper 1 cells and an increase in anti-inflammatory regulatory T cells were observed in the blood and lymph nodes of the Allo-OMC group. Significantly increased percentages of donor immune cells were also observed in three lymphoid organs of the Allo-OMC group, suggesting mixed chimerism induction. These results indicated that the mandible has the potential to induce anti-inflammatory effects and mixed chimerism for prolonging facial allograft survival. The immunomodulatory understanding of the mandible could contribute to reducing the use of immunosuppressive regimens in clinical face allotransplantation including the mandible.

Efficient deep learning architecture with dimension-wise pyramid pooling for nuclei segmentation of histopathology images

Image segmentation remains to be one of the most vital tasks in the area of computer vision and more so in the case of medical image processing. Image segmentation quality is the main metric that is often considered with memory and computation efficiency overlooked, limiting the use of power hungry models for practical use. In this paper, we propose a novel framework (Kidney-SegNet) that combines the effectiveness of an attention based encoder-decoder architecture with atrous spatial pyramid pooling with highly efficient dimension-wise convolutions. The segmentation results of the proposed Kidney-SegNet architecture have been shown to outperform existing state-of-the-art deep learning methods by evaluating them on two publicly available kidney and TNBC breast H&E stained histopathology image datasets. Further, our simulation experiments also reveal that the computational complexity and memory requirement of our proposed architecture is very efficient compared to existing deep learning state-of-the-art methods for the task of nuclei segmentation of H&E stained histopathology images. The source code of our implementation will be available at https://github.com/Aaatresh/Kidney-SegNet.

Comparison of texture-based classification and deep learning for plantar soft tissue histology segmentation

Histomorphological measurements can be used to identify microstructural changes related to disease pathomechanics, in particular, plantar soft tissue changes with diabetes. However, these measurements are time-consuming and susceptible to sampling and human measurement error. We investigated two approaches to automate segmentation of plantar soft tissue stained with modified Hart's stain for elastin with the eventual goal of subsequent morphological analysis. The first approach used multiple texture- and color-based features with tile-wise classification. The second approach used a convolutional neural network modified from the U-Net architecture with fewer channel dimensions and additional downsampling steps. A hybrid color and texture feature, Fourier reduced histogram of uniform improved opponent color local binary patterns (f-IOCLBP), yielded the best feature-based segmentation, but still performed 3.6% worse on average than the modified U-Net. The texture-based method was sensitive to changes in illumination and stain intensity, and segmentation errors were often in large regions of single tissues or at tissue boundaries. The U-Net was able to segment small, few-pixel tissue boundaries, and errors were often trivial to clean up with post-processing. A U-Net approach outperforms hand-crafted features for segmentation of plantar soft tissue stained with modified Hart's stain for elastin.

An unsupervised style normalization method for cytopathology images

Diverse styles of cytopathology images have a negative effect on the generalization ability of automated image analysis algorithms. This article proposes an unsupervised method to normalize cytopathology image styles. We design a two-stage style normalization framework with a style removal module to convert the colorful cytopathology image into a gray-scale image with a color-encoding mask and a domain adversarial style reconstruction module to map them back to a colorful image with user-selected style. Our method enforces both hue and structure consistency before and after normalization by using the color-encoding mask and per-pixel regression. Intra-domain and inter-domain adversarial learning are applied to ensure the style of normalized images consistent with the user-selected for input images of different domains. Our method shows superior results against current unsupervised color normalization methods on six cervical cell datasets from different hospitals and scanners. We further demonstrate that our normalization method greatly improves the recognition accuracy of lesion cells on unseen cytopathology images, which is meaningful for model generalization.

A Review of Ex Vivo X-ray Microfocus Computed Tomography-Based Characterization of the Cardiovascular System

Cardiovascular malformations and diseases are common but complex and often not yet fully understood. To better understand the effects of structural and microstructural changes of the heart and the vasculature on their proper functioning, a detailed characterization of the microstructure is crucial. In vivo imaging approaches are noninvasive and allow visualizing the heart and the vasculature in 3D. However, their spatial image resolution is often too limited for microstructural analyses, and hence, ex vivo imaging is preferred for this purpose. Ex vivo X-ray microfocus computed tomography (microCT) is a rapidly emerging high-resolution 3D structural imaging technique often used for the assessment of calcified tissues. Contrast-enhanced microCT (CE-CT) or phase-contrast microCT (PC-CT) improve this technique by additionally allowing the distinction of different low X-ray-absorbing soft tissues. In this review, we present the strengths of ex vivo microCT, CE-CT and PC-CT for quantitative 3D imaging of the structure and/or microstructure of the heart, the vasculature and their substructures in healthy and diseased state. We also discuss their current limitations, mainly with regard to the contrasting methods and the tissue preparation.

Development of patient-specific 3D models from histopathological samples for applications in radiation therapy

The biological effects of ionizing radiation depend on the tissue, tumor type, radiation quality, and patient-specific factors. Inter-patient variation in cell/nucleus size may influence patient-specific dose response. However, this variability in dose response is not well investigated due to lack of available cell/nucleus size data. The aim of this study was to develop methods to derive cell/nucleus size distributions from digital images of 2D histopathological samples and use them to build digital 3D models for use in cellular dosimetry. Nineteen of sixty hematoxylin and eosin stained lung adenocarcinoma samples investigated passed exclusion criterion to be analyzed in the study. A difference of gaussians blob detection algorithm was used to identify nucleus centers and quantify cell spacing. Hematoxylin content was measured to determine nucleus radius. Pouring simulations were conducted to generate one-hundred 3D models containing volumes of equivalent cell spacing and nuclei radius to those in histopathological samples. The nuclei radius distributions of non-tumoral and cancerous regions appearing in the same slide were significantly different (p < 0.01) in all samples analyzed. The median nuclear-cytoplasmic ratio was 0.36 for non-tumoral cells and 0.50 for cancerous cells. The average cellular and nucleus packing densities in the 3D models generated were 65.9% (SD: 1.5%) and 13.3% (SD: 0.3%) respectively. Software to determine cell spacing and nuclei radius from histopathological samples was developed. 3D digital tissue models containing volumes with equivalent cell spacing, nucleus radius, and packing density to cancerous tissues were generated.

Histopathology classification and localization of colorectal cancer using global labels by weakly supervised deep learning

Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide. In coping with it, histopathology image analysis (HIA) provides key information for clinical diagnosis of CRC. Nowadays, the deep learning methods are widely used in improving cancer classification and localization of tumor-regions in HIA. However, these efforts are both time-consuming and labor-intensive due to the manual annotation of tumor-regions in the whole slide images (WSIs). Furthermore, classical deep learning methods to analyze thousands of patches extracted from WSIs may cause loss of integrated information of image. Herein, a novel method was developed, which used only global labels to achieve WSI classification and localization of carcinoma by combining features from different magnifications of WSIs. The model was trained and tested using 1346 colorectal cancer WSIs from the Cancer Genome Atlas (TCGA). Our method classified colorectal cancer with an accuracy of 94.6 %, which slightly outperforms most of the existing methods. Its cancerous-location probability maps were in good agreement with annotations from three individual expert pathologists. Independent tests on 50 newly-collected colorectal cancer WSIs from hospitals produced 92.0 % accuracy and cancerous-location probability maps were in good agreement with the three pathologists. The results thereby demonstrated that the method sufficiently achieved WSI classification and localization utilizing only global labels. This weakly supervised deep learning method is effective in time and cost, as it delivered a better performance in comparison with the state-of-the-art methods.