Computer-Aided Prostate Cancer Diagnosis From Digitized Histopathology: A Review on Texture-Based Systems

Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment

The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.

Texture analysis using short-tau inversion recovery magnetic resonance images to differentiate squamous cell carcinoma of the gingiva from medication-related osteonecrosis of the jaw

OBJECTIVES

Despite the difficulty in distinguishing between squamous cell carcinoma (SCC) and medication-related osteonecrosis of the jaw (MRONJ) on the basis of medical imaging examinations, the two conditions have completely different treatment methods and prognoses. Therefore, differentiation of SCC from MRONJ on imaging examinations is very important. This study aimed to distinguish SCC from MRONJ by performing texture analysis using magnetic resonance imaging (MRI) short-tau inversion recovery images.

METHODS

This retrospective case-control study included 14 patients with SCC of the lower gingiva and 35 with MRONJ of the mandible who underwent MRI and computed tomography (CT) for suspected SCC or MRONJ. SCC was identified by histopathological examination of tissues excised during surgery. The radiomics features of SCC and MRONJ were analyzed using the open-access software MaZda version 3.3 (Technical University of Lodz, Institute of Electronics, Poland). CT was used to evaluate the presence or absence of qualitative findings (sclerosis, sequestrum, osteolysis, periosteal reaction, and cellulitis) of SCC and MRONJ.

RESULTS

Among the 19 texture features selected using MaZda feature-reduction methods, SCC of the gingiva and MRONJ of the mandible revealed differences in two histogram features, one absolute gradient feature, and 16 Gy level co-occurrence matrix features. In particular, the percentile, angular second moment, entropy, and difference entropy exhibited excellent diagnostic performance.

CONCLUSION

Non-contrast-enhanced MRI texture analysis revealed differences in texture parameters between mandibular SCC and mandibular MRONJ. MRI texture analysis can be a new noninvasive quantitative method for distinguishing between SCC and MRONJ.

An Experimental Platform for Tomographic Reconstruction of Tissue Images in Brightfield Microscopy

(1) Background: Reviewing biological material under the microscope is a demanding and time-consuming process, prone to diagnostic pitfalls. In this study, a methodology for tomographic imaging of tissue sections is presented, relying on the idea that each tissue sample has a finite thickness and, therefore, it is possible to create images at different levels within the sample, revealing details that would probably not be seen otherwise. (2) Methods: Optical slicing was possible by developing a custom-made microscopy stage controlled by an ARDUINO. The custom-made stage, besides the normal sample movements that it should provide along the x-, y-, and z- axes, may additionally rotate the sample around the horizontal axis of the microscope slide. This rotation allows the conversion of the optical microscope into a CT geometry, enabling optical slicing of the sample using projection-based tomographic reconstruction algorithms. (3) Results: The resulting images were of satisfactory quality, but they exhibited some artifacts, which are particularly evident in the axial plane images. (4) Conclusions: Using classical tomographic reconstruction algorithms at limited angles, it is possible to investigate the sample at any desired optical plane, revealing information that would be difficult to identify when focusing only on the conventional 2D images.

A comprehensive survey of intestine histopathological image analysis using machine vision approaches

Colorectal Cancer (CRC) is currently one of the most common and deadly cancers. CRC is the third most common malignancy and the fourth leading cause of cancer death worldwide. It ranks as the second most frequent cause of cancer-related deaths in the United States and other developed countries. Histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of CRC. In order to improve the objectivity and diagnostic efficiency for image analysis of intestinal histopathology, Computer-aided Diagnosis (CAD) methods based on machine learning (ML) are widely applied in image analysis of intestinal histopathology. In this investigation, we conduct a comprehensive study on recent ML-based methods for image analysis of intestinal histopathology. First, we discuss commonly used datasets from basic research studies with knowledge of intestinal histopathology relevant to medicine. Second, we introduce traditional ML methods commonly used in intestinal histopathology, as well as deep learning (DL) methods. Then, we provide a comprehensive review of the recent developments in ML methods for segmentation, classification, detection, and recognition, among others, for histopathological images of the intestine. Finally, the existing methods have been studied, and the application prospects of these methods in this field are given.

Graph 'texture' features as novel metrics that can summarize complex biological graphs

Objective.Image texture features, such as those derived by Haralicket al, are a powerful metric for image classification and are used across fields including cancer research. Our aim is to demonstrate how analogous texture features can be derived for graphs and networks. We also aim to illustrate how these new metrics summarize graphs, may aid comparative graph studies, may help classify biological graphs, and might assist in detecting dysregulation in cancer.Approach.We generate the first analogies of image texture for graphs and networks. Co-occurrence matrices for graphs are generated by summing over all pairs of neighboring nodes in the graph. We generate metrics for fitness landscapes, gene co-expression and regulatory networks, and protein interaction networks. To assess metric sensitivity we varied discretization parameters and noise. To examine these metrics in the cancer context we compare metrics for both simulated and publicly available experimental gene expression and build random forest classifiers for cancer cell lineage.Main results.Our novel graph 'texture' features are shown to be informative of graph structure and node label distributions. The metrics are sensitive to discretization parameters and noise in node labels. We demonstrate that graph texture features vary across different biological graph topologies and node labelings. We show how our texture metrics can be used to classify cell line expression by lineage, demonstrating classifiers with 82% and 89% accuracy.Significance.New metrics provide opportunities for better comparative analyzes and new models for classification. Our texture features are novel second-order graph features for networks or graphs with ordered node labels. In the complex cancer informatics setting, evolutionary analyses and drug response prediction are two examples where new network science approaches like this may prove fruitful.

Human Hepatocellular Carcinoma Classification from H&E Stained Histopathology Images with 3D Convolutional Neural Networks and Focal Loss Function

This paper proposes a new Hepatocellular Carcinoma (HCC) classification method utilizing a hyperspectral imaging system (HSI) integrated with a light microscope. Using our custom imaging system, we have captured 270 bands of hyperspectral images of healthy and cancer tissue samples with HCC diagnosis from a liver microarray slide. Convolutional Neural Networks with 3D convolutions (3D-CNN) have been used to build an accurate classification model. With the help of 3D convolutions, spectral and spatial features within the hyperspectral cube are incorporated to train a strong classifier. Unlike 2D convolutions, 3D convolutions take the spectral dimension into account while automatically collecting distinctive features during the CNN training stage. As a result, we have avoided manual feature engineering on hyperspectral data and proposed a compact method for HSI medical applications. Moreover, the focal loss function, utilized as a CNN cost function, enables our model to tackle the class imbalance problem residing in the dataset effectively. The focal loss function emphasizes the hard examples to learn and prevents overfitting due to the lack of inter-class balancing. Our empirical results demonstrate the superiority of hyperspectral data over RGB data for liver cancer tissue classification. We have observed that increased spectral dimension results in higher classification accuracy. Both spectral and spatial features are essential in training an accurate learner for cancer tissue classification.

Risk assessment of external apical root resorption associated with orthodontic treatment using computed tomography texture analysis

OBJECTIVES

This study aimed to quantitatively assess maxillary central incisor roots using pre-orthodontics computed tomography (CT) texture analysis as part of a radiomics quantitative analysis.

METHODS

This retrospective case-control study included 16 patients with external apical root resorption (EARR) and 16 age- and sex-matched patients without EARR, after orthodontic treatment who underwent pre-orthodontics CT for jaw deformities. All patients were treated with a fixed orthodontic appliance before and after surgical orthodontic treatment. EARR was defined as root resorption ≥ 2 mm of the left and right maxillary central incisors on CT images more than 2 years after the start of orthodontic treatment. Texture features of the maxillary central incisor with and without EARR after orthodontic treatment were analyzed using the open-access software, MaZda Ver. 3.3. Ten texture features were selected using the Fisher method in MaZda from 279 original parameters, which were calculated for each of the maxillary central incisors with and without EARR. The results were tested using the Student's t test, Welch's t test, or Mann-Whitney U test.

RESULTS

Four gray-level run length matrix features and six gray-level co-occurrence matrix features displayed significant differences between both the groups (p < 0.01).

CONCLUSIONS

CT texture analysis was able to quantitatively assess maxillary central incisor roots and distinguish between maxillary central incisor roots with and without EARR. CT texture analysis may be a useful method for predicting EARR after orthodontic treatment.

Automated Caries Screening Using Ensemble Deep Learning on Panoramic Radiographs

Caries prevention is essential for oral hygiene. A fully automated procedure that reduces human labor and human error is needed. This paper presents a fully automated method that segments tooth regions of interest from a panoramic radiograph to diagnose caries. A patient's panoramic oral radiograph, which can be taken at any dental facility, is first segmented into several segments of individual teeth. Then, informative features are extracted from the teeth using a pre-trained deep learning network such as VGG, Resnet, or Xception. Each extracted feature is learned by a classification model such as random forest, k-nearest neighbor, or support vector machine. The prediction of each classifier model is considered as an individual opinion that contributes to the final diagnosis, which is decided by a majority voting method. The proposed method achieved an accuracy of 93.58%, a sensitivity of 93.91%, and a specificity of 93.33%, making it promising for widespread implementation. The proposed method, which outperforms existing methods in terms of reliability, and can facilitate dental diagnosis and reduce the need for tedious procedures.

Convolutional Neural Network-Based Automatic Classification of Colorectal and Prostate Tumor Biopsies Using Multispectral Imagery: System Development Study

BACKGROUND

Colorectal and prostate cancers are the most common types of cancer in men worldwide. To diagnose colorectal and prostate cancer, a pathologist performs a histological analysis on needle biopsy samples. This manual process is time-consuming and error-prone, resulting in high intra- and interobserver variability, which affects diagnosis reliability.

OBJECTIVE

This study aims to develop an automatic computerized system for diagnosing colorectal and prostate tumors by using images of biopsy samples to reduce time and diagnosis error rates associated with human analysis.

METHODS

In this study, we proposed a convolutional neural network (CNN) model for classifying colorectal and prostate tumors from multispectral images of biopsy samples. The key idea was to remove the last block of the convolutional layers and halve the number of filters per layer.

RESULTS

Our results showed excellent performance, with an average test accuracy of 99.8% and 99.5% for the prostate and colorectal data sets, respectively. The system showed excellent performance when compared with pretrained CNNs and other classification methods, as it avoids the preprocessing phase while using a single CNN model for the whole classification task. Overall, the proposed CNN architecture was globally the best-performing system for classifying colorectal and prostate tumor images.

CONCLUSIONS

The proposed CNN architecture was detailed and compared with previously trained network models used as feature extractors. These CNNs were also compared with other classification techniques. As opposed to pretrained CNNs and other classification approaches, the proposed CNN yielded excellent results. The computational complexity of the CNNs was also investigated, and it was shown that the proposed CNN is better at classifying images than pretrained networks because it does not require preprocessing. Thus, the overall analysis was that the proposed CNN architecture was globally the best-performing system for classifying colorectal and prostate tumor images.

Architecture of Computing System based on Chiplet

Computing systems are widely used in medical diagnosis, climate prediction, autonomous vehicles, etc. As the key part of electronics, the performance of computing systems is crucial in the intellectualization of the equipment. The conflict between performance, efficiency, and cost can be solved by choosing an appropriate computing system architecture. In order to provide useful advice and instructions for the designers to fabricate high-performance computing systems, this paper reviews the Chiplet-based computing system architectures, including computing architecture and memory architecture. Firstly, the computing architecture used for high-performance computing, mobile, and PC is presented and summarized. Secondly, the memory architecture based on mainstream memory and emerging non-volatile memory used for data storing and processing are introduced, and the key parameters of memory are compared and discussed. Finally, this paper is concluded, and the future perspectives of computing system architecture based on Chiplet are presented.

Quantitative assessment of mandibular bone marrow using computed tomography texture analysis for detect stage 0 medication-related osteonecrosis of the jaw

PURPOSE

Medication-related osteonecrosis of the jaw (MRONJ) is a serious complication of treatment with bisphosphonates or antiangiogenic inhibitors. MRONJ has four stages (0-3); however, stage 0 MRONJ is difficult to detect using computed tomography (CT). This study aimed to quantitatively assess the mandibular bone marrow using texture analysis to detect stage 0 MRONJ from CT images.

METHODS

This retrospective study included 25 patients with stage 0 MRONJ who had a history of treatment with bisphosphonates and underwent CT and magnetic resonance imaging (MRI). The mandibular bone marrow with abnormal signals (T1-weighted imaging: low, T2-weighted imaging: low or high, short-tau inversion recovery: high) on MRI, and no qualitative characteristic CT and oral findings indicative of osteonecrosis (exposed bone, sequestrum, periosteal reaction, and osteolysis) was identified as 0 MRONJ. Texture features of the bone marrow of the mandible with MRONJ and the contralateral, normal mandibular bone marrow were extracted using an open-access software, namely, LIFEx. The volumes of interest (VOIs) were manually placed on CT images by tracing the bilateral mandibular bone marrow regions, excluding the teeth, mandibular canal, and cortical bone. Thirty-seven texture features were extracted from each VOI.

RESULTS

Six gray-level run length matrix features and four gray-level zone length matrix features exhibited significant differences between mandibular bone marrow with and without MRONJ.

CONCLUSIONS

CT was able to quantitatively assess texture features of normal mandibular bone marrow and that with MRONJ. Texture analysis may be useful as a new method for detecting stage 0 MRONJ using CT.

Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.

Quantitative assessment of the maxillary sinusitis using computed tomography texture analysis: odontogenic vs non-odontogenic etiology

OBJECTIVES

The purpose of this study was to investigate computed tomography (CT) texture features of mucosal thickening of maxillary sinus mucosa to differentiate odontogenic maxillary sinusitis (OMS) from non-odontogenic maxillary sinusitis (NOMS).

METHODS

Eighteen OMS patients and age- and gender-matched 18 NOMS patients who underwent sinus CT were retrospectively reviewed. OMS patients were identified by histopathological examination of tissues excised at surgery combined with CT imaging findings. Patients with mucosal thickening in the maxillary sinus without apical periodontitis or advanced periodontal bone loss near the maxillary sinus on CT were defined as NOMS. Patients with thin mucosal thickening (< 10 mm), cyst, tumor, post-operative deformity, severe metal artifact precluding visualization of the maxillary sinus, and age younger than 20 years were excluded. CT texture features of the mucosal thickening were analyzed using an in-house developed Matlab-based texture analysis program. Forty-five texture features were extracted from each segmented volume. The results were tested with the Mann-Whitney U test.

RESULTS

Six histogram features (mean, median, standard deviation, entropy, geometric mean, harmonic mean) and two gray-level co-occurrence matrix features (entropy, correlation) showed significant differences between OMS and NOMS patients.

CONCLUSIONS

CT texture analysis revealed the quantitative differences between OMS and NOMS. The texture features can serve as a quantitative indicator of maxillary sinusitis to differentiate between OMS and NOMS and help prevent incorrect treatment choices.

Feature fusion of Raman chemical imaging and digital histopathology using machine learning for prostate cancer detection

The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP-RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and mean and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization.

Characterisation of prostate cancer using texture analysis for diagnostic and prognostic monitoring

Automated classification of significant prostate cancer (PCa) using MRI plays a potential role in assisting in clinical decision-making. Multiparametric MRI using a machine-aided approach is a better step to improve the overall accuracy of diagnosis of PCa. The objective of this study was to develop and validate a framework for differentiating Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) grades (grade 2 to grade 5) of PCa using texture features and machine learning (ML) methods with diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC). The study cohort included an MRI dataset of 59 patients with clinically proven PCa. Regions of interest (ROIs) for a total of 435 lesions were delineated from the segmented peripheral zones of DWI and ADC. Six texture methods comprising 98 texture features in total (49 each of DWI and ADC) were extracted from lesion ROIs. Random forest (RF) and correlation-based feature selection methods were applied on feature vectors to select the best features for classification. Two ML classifiers, support vector machine (SVM) and K-nearest neighbour, were used and validated by 10-fold cross-validation. The proposed framework achieved high diagnostic performance with a sensitivity of 85.25% ± 3.84%, specificity of 95.71% ± 1.96%, accuracy of 84.90% ± 3.37% and area under the receiver-operating characteristic curve of 0.98 for PI-RADS v2 grades (2 to 5) classification using the RF feature selection method and Gaussian SVM classifier with combined features of DWI + ADC. The proposed computer-assisted framework can distinguish between PCa lesions with different aggressiveness based on PI-RADS v2 standards using texture analysis to improve the efficiency of PCa diagnostic performance.

HER2 Molecular Marker Scoring Using Transfer Learning and Decision Level Fusion

In prognostic evaluation of breast cancer, immunohistochemical (IHC) marker human epidermal growth factor receptor 2 (HER2) is used for prognostic evaluation. Accurate assessment of HER2-stained tissue sample is essential in therapeutic decision making for the patients. In regular clinical settings, expert pathologists assess the HER2-stained tissue slide under microscope for manual scoring based on prior experience. Manual scoring is time consuming, tedious, and often prone to inter-observer variation among group of pathologists. With the recent advancement in the area of computer vision and deep learning, medical image analysis has got significant attention. A number of deep learning architectures have been proposed for classification of different image groups. These networks are also used for transfer learning to classify other image classes. In the presented study, a number of transfer learning architectures are used for HER2 scoring. Five pre-trained architectures viz. VGG16, VGG19, ResNet50, MobileNetV2, and NASNetMobile with decimating the fully connected layers to get 3-class classification have been used for the comparative assessment of the networks as well as further scoring of stained tissue sample image based on statistical voting using mode operator. HER2 Challenge dataset from Warwick University is used in this study. A total of 2130 image patches were extracted to generate the training dataset from 300 training images corresponding to 30 training cases. The output model is then tested on 800 new test image patches from 100 test images acquired from 10 test cases (different from training cases) to report the outcome results. The transfer learning models have shown significant accuracy with VGG19 showing the best accuracy for the test images. The accuracy is found to be 93%, which increases to 98% on the image-based scoring using statistical voting mechanism. The output shows a capable quantification pipeline in automated HER2 score generation.

Artificial intelligence in urological oncology: An update and future applications

There continues to be rapid developments and research in the field of Artificial Intelligence (AI) in Urological Oncology worldwide. In this review we discuss the basics of AI, application of AI per tumour group (Renal, Prostate and Bladder Cancer) and application of AI in Robotic Urological Surgery. We also discuss future applications of AI being developed with the benefits to patients with Urological Oncology.

Deep Learning Classifier with Patient's Metadata of Dermoscopic Images in Malignant Melanoma Detection

BACKGROUND

Incidence of skin cancer is one of the global burdens of malignancies that increase each year, with melanoma being the deadliest one. Imaging-based automated skin cancer detection still remains challenging owing to variability in the skin lesions and limited standard dataset availability. Recent research indicates the potential of deep convolutional neural networks (CNN) in predicting outcomes from simple as well as highly complicated images. However, its implementation requires high-class computational facility, that is not feasible in low resource and remote areas of health care. There is potential in combining image and patient's metadata, but the study is still lacking.

OBJECTIVE

We want to develop malignant melanoma detection based on dermoscopic images and patient's metadata using an artificial intelligence (AI) model that will work on low-resource devices.

METHODS

We used an open-access dermatology repository of International Skin Imaging Collaboration (ISIC) Archive dataset consist of 23,801 biopsy-proven dermoscopic images. We tested performance for binary classification malignant melanomas vs nonmalignant melanomas. From 1200 sample images, we split the data for training (72%), validation (18%), and testing (10%). We compared CNN with image data only (CNN model) vs CNN for image data combined with an artificial neural network (ANN) for patient's metadata (CNN+ANN model).

RESULTS

The balanced accuracy for CNN+ANN model was higher (92.34%) than the CNN model (73.69%). Combination of the patient's metadata using ANN prevents the overfitting that occurs in the CNN model using dermoscopic images only. This small size (24 MB) of this model made it possible to run on a medium class computer without the need of cloud computing, suitable for deployment on devices with limited resources.

CONCLUSION

The CNN+ANN model can increase the accuracy of classification in malignant melanoma detection even with limited data and is promising for development as a screening device in remote and low resources health care.

Pathomics in urology

PURPOSE OF REVIEW

Pathomics, the fusion of digitalized pathology and artificial intelligence, is currently changing the landscape of medical pathology and biologic disease classification. In this review, we give an overview of Pathomics and summarize its most relevant applications in urology.

RECENT FINDINGS

There is a steady rise in the number of studies employing Pathomics, and especially deep learning, in urology. In prostate cancer, several algorithms have been developed for the automatic differentiation between benign and malignant lesions and to differentiate Gleason scores. Furthermore, several applications have been developed for the automatic cancer cell detection in urine and for tumor assessment in renal cancer. Despite the explosion in research, Pathomics is not fully ready yet for widespread clinical application.

SUMMARY

In prostate cancer and other urologic pathologies, Pathomics is avidly being researched with commercial applications on the close horizon. Pathomics is set to improve the accuracy, speed, reliability, cost-effectiveness and generalizability of pathology, especially in uro-oncology.

Generative Deep Learning in Digital Pathology Workflows

Many modern histopathology laboratories are in the process of digitizing their workflows. Once images of the tissue exist as digital data, it becomes feasible to research the augmentation or automation of clinical reporting and diagnosis. The application of modern computer vision techniques, based on deep learning, promises systems that can identify pathologies in slide images with a high degree of accuracy. Generative modeling is an approach to machine learning and deep learning that can be used to transform and generate data. It can be applied to a broad range of tasks within digital pathology, including the removal of color and intensity artifacts, the adaption of images in one domain into those of another, and the generation of synthetic digital tissue samples. This review provides an introduction to the topic, considers these applications, and discusses some future directions for generative models within histopathology.

Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey

Prostate cancer is one of the most identified cancers and second most prevalent among cancer-related deaths of men worldwide. Early diagnosis and treatment are substantial to stop or handle the increase and spread of cancer cells in the body. Histopathological image diagnosis is a gold standard for detecting prostate cancer as it has different visual characteristics but interpreting those type of images needs a high level of expertise and takes too much time. One of the ways to accelerate such an analysis is by employing artificial intelligence (AI) through the use of computer-aided diagnosis (CAD) systems. The recent developments in artificial intelligence along with its sub-fields of conventional machine learning and deep learning provide new insights to clinicians and researchers, and an abundance of research is presented specifically for histopathology images tailored for prostate cancer. However, there is a lack of comprehensive surveys that focus on prostate cancer using histopathology images. In this paper, we provide a very comprehensive review of most, if not all, studies that handled the prostate cancer diagnosis using histopathological images. The survey begins with an overview of histopathological image preparation and its challenges. We also briefly review the computing techniques that are commonly applied in image processing, segmentation, feature selection, and classification that can help in detecting prostate malignancies in histopathological images.

Deep Fusion Feature Extraction for Caries Detection on Dental Panoramic Radiographs

Caries is the most well-known disease and relates to the oral health of billions of people around the world. Despite the importance and necessity of a well-designed detection method, studies in caries detection are still limited and show a restriction in performance. In this paper, we proposed a computer-aided diagnosis (CAD) method to detect caries among normal patients using dental radiographs. The proposed method mainly consists of two processes: feature extraction and classification. In the feature extraction phase, the chosen 2D tooth image was employed to extract deep activated features using a deep pre-trained model and geometric features using mathematic formulas. Both feature sets were then combined, called fusion feature, to complement each other defects. Then, the optimal fusion feature set was fed into well-known classification models such as support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), Naïve Bayes (NB), and random forest (RF) to determine the best classification model that fit the fusion features set and perform the most preeminent result. The results show 91.70%, 90.43%, and 92.67% for accuracy, sensitivity, and specificity, respectively. The proposed method has outperformed the previous state-of-the-art and shows promising results when none of the measured factors is less than 90%; therefore, the method is promising for dentists and capable of wide-scale implementation caries detection in hospitals.

Computed tomography texture analysis of mandibular condylar bone marrow in diabetes mellitus patients

OBJECTIVES

Diabetes mellitus (DM) is associated with a broad range of complications, such as retinopathy, nephropathy, neuropathy, and cardiovascular disease. Therefore, predicting DM from head and neck images is a challenge for clinicians. The purpose of this study was to assess the mandibular condylar bone marrow in DM patients using computed tomography (CT) texture analysis.

METHODS

This retrospective study included 16 DM and age and sex matched 16 control patients (11 men, 5 women; mean age, 56.8 ± 14.4 years; range 31-78 years). Patients with Type I DM, prior history of taking bisphosphonates, osteoarthritis of the temporomandibular joint, and CT images with metal artifacts were excluded from this study. Bilateral mandibular condylar bone marrow was manually contoured on axial CT images. The presence or absence of DM is the primary predictor variable. Texture features of the region of interest was the outcome variable, that were analyzed using an open-access software, MaZda Ver.3.3. For each group, 20 features out of 279 parameters were selected with Fisher, probability of error and average correlation coefficient methods in MaZda. Bivariate statistics were computed with the Mann-Whitney U test and the P value was set at .05.

RESULTS

One histogram feature, 15 Gy level co-occurrence matrix features, and four gray level run length matrix features showed differences between the DM patients and non-DM patients (P < 0.05).

CONCLUSIONS

Several texture features of the condyle demonstrated differences between the DM and non-DM patients. CT texture analysis may potentially detect DM from the condylar bone marrow.

Application of Machine Learning Algorithms in Breast Cancer Diagnosis and Classification

Breast cancer continues to be the most frequent cancer in females, affecting about one in 8 women and causing the highest number of cancer-related deaths in females worldwide despite remarkable progress in early diagnosis, screening, and patient management. All breast lesions are not malignant, and all the benign lesions do not progress to cancer. However, the accuracy of diagnosis can be increased by a combination or preoperative tests such as physical examination, mammography, fine-needle aspiration cytology, and core needle biopsy. Despite some limitations, these procedures are more accurate, reliable, and acceptable, when compared with a single adopted diagnostic procedure. Recent studies have shown that breast cancer can be accurately predicted and diagnosed using machine learning (ML) technology. The objective of this study was to explore the application of ML approaches to classify breast cancer based on feature values generated from a digitized image of a fine-needle aspiration (FNA) of a breast mass. To achieve this objective, we used ML algorithms, collected a scientific dataset of 569 breast cancer patients from Kaggle (https://www.kaggle.com/uciml/breast-cancer-wisconsin-data), analyze and interpreted the data based on ten real-valued features of a breast mass FNA including the radius, texture, perimeter, area, smoothness, compactness, concavity, concave points, symmetry, and fractal dimension. Among the 569 patients tested, 63% were diagnosed with benign breast cancer and 37% were diagnosed with malignant breast cancer. Benign tumors grow slowly and do not spread while malignant tumors grow rapidly and spread to other parts of the body.

Quantitative assessment of normal submandibular glands and submandibular sialadenitis using CT texture analysis: A retrospective study

OBJECTIVE

The purpose of this study was to quantitatively assess normal submandibular glands and submandibular sialadenitis (SS) using computed tomography (CT) texture analysis as part of radiomics quantitative analysis.

STUDY DESIGN

In total, 31 patients with unilateral SS who underwent head and neck magnetic resonance imaging (MRI) and CT and were retrospectively reviewed. Submandibular glands with abnormal signals (STIR: high, T2-weighted image: high, T1-weighted image: low) on MRI were identified as SS. The radiomics features of the contralateral normal submandibular glands and SS were analyzed using an open-access software, MaZda Version 3.3. Sixteen radiomics features were selected with Fisher and probability of error and average correlation coefficient methods in MaZda from 279 original parameters calculated for each of the normal and SS glands. The results were statistically analyzed with the Wilcoxon rank sum test.

RESULTS

One gray-level co-occurrence matrix feature and 9 gray-level run length matrix features displayed significant differences between normal submandibular glands and glands with SS (P < .05).

CONCLUSIONS

CT texture analysis was able to quantitatively distinguish between normal and diseased submandibular glands. It therefore may have the potential to detect SS.

Automated gleason grading on prostate biopsy slides by statistical representations of homology profile

BACKGROUND AND OBJECTIVE

Gleason grading system is currently the clinical gold standard for determining prostate cancer aggressiveness. Prostate cancer is typically classified into one of 5 different categories with 1 representing the most indolent disease and 5 reflecting the most aggressive disease. Grades 3 and 4 are the most common and difficult patterns to be discriminated in clinical practice. Even though the degree of gland differentiation is the strongest determinant of Gleason grade, manual grading is subjective and is hampered by substantial inter-reader disagreement, especially with regard to intermediate grade groups.

METHODS

To capture the topological characteristics and the degree of connectivity between nuclei around the gland, the concept of Homology Profile (HP) for prostate cancer grading is presented in this paper. HP is an algebraic tool, whereby, certain algebraic invariants are computed based on the structure of a topological space. We utilized the Statistical Representation of Homology Profile (SRHP) features to quantify the extent of glandular differentiation. The quantitative characteristics which represent the image patch are fed into a supervised classifier model for discrimination of grade patterns 3 and 4.

RESULTS

On the basis of the novel homology profile, we evaluated 43 digitized images of prostate biopsy slides annotated for regions corresponding to Grades 3 and 4. The quantitative patch-level evaluation results showed that our approach achieved an Area Under Curve (AUC) of 0.96 and an accuracy of 0.89 in terms of discriminating Grade 3 and 4 patches. Our approach was found to be superior to comparative methods including handcrafted cellular features, Stacked Sparse Autoencoder (SSAE) algorithm and end-to-end supervised learning method (DLGg). Also, slide-level quantitative and qualitative evaluation results reflect the ability of our approach in discriminating Gleason Grade 3 from 4 patterns on H&E tissue images.

CONCLUSIONS

We presented a novel Statistical Representation of Homology Profile (SRHP) approach for automated Gleason grading on prostate biopsy slides. The most discriminating topological descriptions of cancerous regions for grade 3 and 4 in prostate cancer were identified. Moreover, these characteristics of homology profile are interpretable, visually meaningful and highly consistent with the rubric employed by pathologists for the task of Gleason grading.

Segmentation of White Blood Cell, Nucleus and Cytoplasm in Digital Haematology Microscope Images: A Review-Challenges, Current and Future Potential Techniques

Segmentation of white blood cells in digital haematology microscope images represents one of the major tools in the diagnosis and evaluation of blood disorders. Pathological examinations are being the gold standard in many haematology and histophathology, and also play a key role in the diagnosis of diseases. In clinical diagnosis, white blood cells are analysed by pathologists from peripheral blood smears samples of patients. This analysis is mainly based on morphological features and characteristics of the white blood cells and their nuclei and cytoplasm, including, shapes, sizes, colours, textures, maturity stages and staining processes. Recently, Computer Aided Diagnosis techniques have been rapidly growing in the digital haematology area related to white blood cells, and their nuclei and cytoplasm detection, as well as their segmentation and classification techniques. In digital haematology image analysis, these techniques have played and will continue to play, a vital role for providing traceable clinical information, consolidating pertinent second opinions, and minimizing human intervention. This study outlines, discusses, and introduces the major trends from a particular review of detection and segmentation methods for white blood cells and their nuclei and cytoplasm from digital haematology microscope images. Performance of existing methods have been comprehensively compared, taking into account databases used, number of images and limitations. This study can also help us to identify the challenges that remain, in achieving a robust analysis of white blood cell microscope images, which could support the diagnosis of blood disorders and assist researchers and pathologists in the future. The impact of this work is to enhance the accuracy of pathologists' decisions and their efficiency, and overall benefit the patients for faster and more accurate diagnosis. The significant of the paper on intelligent system is that provides future potential techniques for solving overlapping white blood cell identification and other problems microscopic images. The accurate segmentation and detection of white blood cells can increase the accuracy of cell counting system for diagnosing diseases in the future.

Multi-scale tissue architecture analysis of favorable-risk prostate cancer: Correlation with biochemical recurrence

Purpose

Prostate cancer (PCa) with biopsy-based grade group (GG) 1 or 2 characteristics has a favorable outcome, yet some cases still progress after radical prostatectomy and present with biochemical recurrence (BCR). We hypothesized that the multi-scale tissue architecture (MSTA) analysis score would correlate with the aggressive PCa phenotype and could be used as a tool for risk assessment to improve the management of patients with favorable-risk PCa.

Materials and Methods

MSTA was evaluated in needle-biopsy samples from 115 patients with favorable-risk PCa, as defined by GG1 and GG2, a prostate-specific antigen (PSA) level of <10 ng/mL, a clinical stage of cT1c to cT2b, and general Gleason GG (GGG) and expert pathologist-assessed GG (EGG). Algorithms based on Voronoi diagrams were applied to all Feulgen-thionin-stained diagnostic areas. One hundred tissue architecture features were calculated and an MSTA score, a linear combination of the most discriminant features, was generated. Correlation of MSTA score with BCR and other clinical variables was investigated.

Results

In a univariate regression model, EGG, clinical stage, and MSTA were significant predictors of BCR (respective p-values: 0.0016, 0.016, and 0.028). Survival analysis showed that patients with a high MSTA score were more likely to experience BCR than were patients with a low MSTA score (odds ratio, 2.9). Combining MSTA with GG assessment resulted in a significant stratification of risk for BCR.

Conclusions

MSTA score could be used as an objective adjunct risk stratification tool to pathologist assessments and could improve the management of patients with favorable-risk PCa.

Automatic cancer detection on digital histopathology images of mid-gland radical prostatectomy specimens

Purpose: Automatic cancer detection on radical prostatectomy (RP) sections facilitates graphical and quantitative surgical pathology reporting, which can potentially benefit postsurgery follow-up care and treatment planning. It can also support imaging validation studies using a histologic reference standard and pathology research studies. This problem is challenging due to the large sizes of digital histopathology whole-mount whole-slide images (WSIs) of RP sections and staining variability across different WSIs. Approach: We proposed a calibration-free adaptive thresholding algorithm, which compensates for staining variability and yields consistent tissue component maps (TCMs) of the nuclei, lumina, and other tissues. We used and compared three machine learning methods for classifying each cancer versus noncancer region of interest (ROI) throughout each WSI: (1) conventional machine learning methods and 14 texture features extracted from TCMs, (2) transfer learning with pretrained AlexNet fine-tuned by TCM ROIs, and (3) transfer learning with pretrained AlexNet fine-tuned with raw image ROIs. Results: The three methods yielded areas under the receiver operating characteristic curve of 0.96, 0.98, and 0.98, respectively, in leave-one-patient-out cross validation using 1.3 million ROIs from 286 mid-gland whole-mount WSIs from 68 patients. Conclusion: Transfer learning with the use of TCMs demonstrated state-of-the-art overall performance and is more stable with respect to sample size across different tissue types. For the tissue types involving Gleason 5 (most aggressive) cancer, it achieved the best performance compared to the other tested methods. This tool can be translated to clinical workflow to assist graphical and quantitative pathology reporting for surgical specimens upon further multicenter validation.

Histologic tissue components provide major cues for machine learning-based prostate cancer detection and grading on prostatectomy specimens

Automatically detecting and grading cancerous regions on radical prostatectomy (RP) sections facilitates graphical and quantitative pathology reporting, potentially benefitting post-surgery prognosis, recurrence prediction, and treatment planning after RP. Promising results for detecting and grading prostate cancer on digital histopathology images have been reported using machine learning techniques. However, the importance and applicability of those methods have not been fully investigated. We computed three-class tissue component maps (TCMs) from the images, where each pixel was labeled as nuclei, lumina, or other. We applied seven different machine learning approaches: three non-deep learning classifiers with features extracted from TCMs, and four deep learning, using transfer learning with the 1) TCMs, 2) nuclei maps, 3) lumina maps, and 4) raw images for cancer detection and grading on whole-mount RP tissue sections. We performed leave-one-patient-out cross-validation against expert annotations using 286 whole-slide images from 68 patients. For both cancer detection and grading, transfer learning using TCMs performed best. Transfer learning using nuclei maps yielded slightly inferior overall performance, but the best performance for classifying higher-grade cancer. This suggests that 3-class TCMs provide the major cues for cancer detection and grading primarily using nucleus features, which are the most important information for identifying higher-grade cancer.

Automated classification of cancer from fine needle aspiration cytological image use neural networks: A meta-analysis

BACKGROUND

The role of retrospective analysis has been evolved greatly in cancer research. We undertook this meta-analysis to evaluate the diagnostic value of Neural networks (NNs) in Fine needle aspiration cytological (FNAC) image of cancer.

METHODS

We systematically retrieved 396 literatures on cytodiagnosis of NNs from Cochrane, PubMed, and EMBASE. After screening, only six studies were included in meta-analysis finally. Data was comprehensively analyzed by RevMan and meta-Disc software.

RESULTS

A total of 1165 cases were extracted from six articles. Among them, 593 cases were in the abnormal/positive group and 572 cases in the normal/negative group. The pooled estimates for the NNs cytology were Area under ROC curve (AUC): 0.99, Sensitivity: 0.85 (95% CI:0.82-0.88), Specificity: 0.96 (95% CI:0.94-0.97), Positive Likelihood Ratio (LR):18.43 (95% CI:6.83-49.74), Negative Likelihood Ratio (LR): 0.06 (95% CI:0.001-0.58), and Diagnostic odds ratio (DOR): 343.21 (34.41-3422.77).

CONCLUSIONS

This meta-analysis confirms that NNs Automated Classification algorithm can facilitate to some extent the FNCA diagnosis of cancer.

A new era: artificial intelligence and machine learning in prostate cancer

Artificial intelligence (AI) - the ability of a machine to perform cognitive tasks to achieve a particular goal based on provided data - is revolutionizing and reshaping our health-care systems. The current availability of ever-increasing computational power, highly developed pattern recognition algorithms and advanced image processing software working at very high speeds has led to the emergence of computer-based systems that are trained to perform complex tasks in bioinformatics, medical imaging and medical robotics. Accessibility to 'big data' enables the 'cognitive' computer to scan billions of bits of unstructured information, extract the relevant information and recognize complex patterns with increasing confidence. Computer-based decision-support systems based on machine learning (ML) have the potential to revolutionize medicine by performing complex tasks that are currently assigned to specialists to improve diagnostic accuracy, increase efficiency of throughputs, improve clinical workflow, decrease human resource costs and improve treatment choices. These characteristics could be especially helpful in the management of prostate cancer, with growing applications in diagnostic imaging, surgical interventions, skills training and assessment, digital pathology and genomics. Medicine must adapt to this changing world, and urologists, oncologists, radiologists and pathologists, as high-volume users of imaging and pathology, need to understand this burgeoning science and acknowledge that the development of highly accurate AI-based decision-support applications of ML will require collaboration between data scientists, computer researchers and engineers.

Annotations, Ontologies, and Whole Slide Images - Development of an Annotated Ontology-Driven Whole Slide Image Library of Normal and Abnormal Human Tissue

Objective:

Digital pathology is today a widely used technology, and the digitalization of microscopic slides into whole slide images (WSIs) allows the use of machine learning algorithms as a tool in the diagnostic process. In recent years, “deep learning” algorithms for image analysis have been applied to digital pathology with great success. The training of these algorithms requires a large volume of high-quality images and image annotations. These large image collections are a potent source of information, and to use and share the information, standardization of the content through a consistent terminology is essential. The aim of this project was to develop a pilot dataset of exhaustive annotated WSI of normal and abnormal human tissue and link the annotations to appropriate ontological information.

Materials and Methods:

Several biomedical ontologies and controlled vocabularies were investigated with the aim of selecting the most suitable ontology for this project. The selection criteria required an ontology that covered anatomical locations, histological subcompartments, histopathologic diagnoses, histopathologic terms, and generic terms such as normal, abnormal, and artifact. WSIs of normal and abnormal tissue from 50 colon resections and 69 skin excisions, diagnosed 2015-2016 at the Department of Clinical Pathology in Linköping, were randomly collected. These images were manually and exhaustively annotated at the level of major subcompartments, including normal or abnormal findings and artifacts.

Results:

Systemized nomenclature of medicine clinical terms (SNOMED CT) was chosen, and the annotations were linked to its codes and terms. Two hundred WSI were collected and annotated, resulting in 17,497 annotations, covering a total area of 302.19 cm², equivalent to 107,7 gigapixels. Ninety-five unique SNOMED CT codes were used. The time taken to annotate a WSI varied from 45 s to over 360 min, a total time of approximately 360 h.

Conclusion:

This work resulted in a dataset of 200 exhaustive annotated WSIs of normal and abnormal tissue from the colon and skin, and it has informed plans to build a comprehensive library of annotated WSIs. SNOMED CT was found to be the best ontology for annotation labeling. This project also demonstrates the need for future development of annotation tools in order to make the annotation process more efficient.

Phenotype Algorithm based Big Data Analytics for Cancer Diagnose

Nowadays, Cancer diagnosis is one of the major challenging characteristics for treating cancer. The reality of cancer patients rely on the diagnosis of cancer at the early stages (either in stage 1 or stage 2). If the cancer is diagnosed in stage 3 or later stages means the changes of survival of the patient will become more critical. Normally, single patient records will generate a huge amount of data if the data could be manage and analyze means to solve many problems for identifying the patterns it will leads to diagnose the cancer. Recent work several machine learning algorithms are introduced for the classification of cancer. However still the classification accuracy of machine learning algorithms are reduced because of huge number of samples. So the proposed work introduces a new Hadoop Distributed File System (HDFS) is focused in this work. In this paper, the proposed phenotype techniques are used which handle and classifies the raw EHR (Electronic Health Record) and EMR (Electronic Medical Record). It is based on the HDFS and Two-Phase Map Reduce. Phenotype algorithm uses NLP (National Language Processing) tool which will analyze and classify the cancer patient data like gene mapping, age related data, image and ultrasonic frequency processing, identification and analysis of irregularities, disease and personal histories. In this paper, the three factorized model is used which calculates the mean score values. The values are calculated by disease stage, pain status, etc. This paper focuses big data analytics for cancer diagnosis and the simulation results shows the proposed system produces the highest performance.

Similar image search for histopathology: SMILY

The increasing availability of large institutional and public histopathology image datasets is enabling the searching of these datasets for diagnosis, research, and education. Although these datasets typically have associated metadata such as diagnosis or clinical notes, even carefully curated datasets rarely contain annotations of the location of regions of interest on each image. As pathology images are extremely large (up to 100,000 pixels in each dimension), further laborious visual search of each image may be needed to find the feature of interest. In this paper, we introduce a deep-learning-based reverse image search tool for histopathology images: Similar Medical Images Like Yours (SMILY). We assessed SMILY's ability to retrieve search results in two ways: using pathologist-provided annotations, and via prospective studies where pathologists evaluated the quality of SMILY search results. As a negative control in the second evaluation, pathologists were blinded to whether search results were retrieved by SMILY or randomly. In both types of assessments, SMILY was able to retrieve search results with similar histologic features, organ site, and prostate cancer Gleason grade compared with the original query. SMILY may be a useful general-purpose tool in the pathologist's arsenal, to improve the efficiency of searching large archives of histopathology images, without the need to develop and implement specific tools for each application.

An Automatic Classification Method on Chronic Venous Insufficiency Images

Chronic venous insufficiency (CVI) affect a large population, and it cannot heal without doctors' interventions. However, many patients do not get the medical advisory service in time. At the same time, the doctors also need an assistant tool to classify the patients according to the severity level of CVI. We propose an automatic classification method, named CVI-classifier to help doctors and patients. In this approach, first, low-level image features are mapped into middle-level semantic features by a concept classifier, and a multi-scale semantic model is constructed to form the image representation with rich semantics. Second, a scene classifier is trained using an optimized feature subset calculated by the high-order dependency based feature selection approach, and is used to estimate CVI's severity. At last, classification accuracy, kappa coefficient, F1-score are used to evaluate classification performance. Experiments on the CVI images from 217 patients' medical records demonstrated superior performance and efficiency for CVI-classifier, with classification accuracy up to 90.92%, kappa coefficient of 0.8735 and F1score of 0.9006. This method also outperformed doctors' diagnosis (doctors rely solely on images to make judgments) with accuracy, kappa and F1-score improved by 9.11%, 0.1250 and 0.0955 respectively.

Image quilting and wavelet fusion for creation of synthetic microscopy nuclei images

BACKGROUND AND OBJECTIVE

In this study a texture simulation methodology is proposed for composing synthetic tissue microscopy images that could serve as a quantitative gold standard for the evaluation of the reliability, accuracy and performance of segmentation algorithms in computer-aided diagnosis.

METHODS

A library of background and nuclei regions was generated using pre-segmented Haematoxylin and Eosin images of brain tumours. Background image samples were used as input to an image quilting algorithm that produced the synthetic background image. Randomly selected pre-segmented nuclei were randomly fused on the synthetic background using a wavelet-based fusion approach. To investigate whether the produced synthetic images are meaningful and similar to real world images, two different tests were performed, one qualitative by an experienced histopathologist and one quantitative using the normalized mutual information and the Kullback-Leibler tests. To illustrate the challenges that synthetic images may pose to object recognition algorithms, two segmentation methodologies were utilized for nuclei detection, one based on the Otsu thresholding and another based on the seeded region growing approach.

RESULTS

Results showed a satisfactory to good resemblance of the synthetic with the real world images according to both qualitative and quantitative tests. The segmentation accuracy was slightly higher for the seeded region growing algorithm (87.2%) than the Otsu's algorithm (86.3%).

CONCLUSIONS

Since we know the exact coordinates of the regions of interest within the synthesised images, these images could then serve as a 'gold standard' for evaluation of segmentation algorithms in computer-aided diagnosis in tissue microscopy.

Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network

BACKGROUND AND OBJECTIVE

Development of machine assisted tools for automatic analysis of psoriasis skin biopsy image plays an important role in clinical assistance. Development of automatic approach for accurate segmentation of psoriasis skin biopsy image is the initial prerequisite for developing such system. However, the complex cellular structure, presence of imaging artifacts, uneven staining variation make the task challenging. This paper presents a pioneering attempt for automatic segmentation of psoriasis skin biopsy images.

METHODS

Several deep neural architectures are tried for segmenting psoriasis skin biopsy images. Deep models are used for classifying the super-pixels generated by Simple Linear Iterative Clustering (SLIC) and the segmentation performance of these architectures is compared with the traditional hand-crafted feature based classifiers built on popularly used classifiers like K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). A U-shaped Fully Convolutional Neural Network (FCN) is also used in an end to end learning fashion where input is the original color image and the output is the segmentation class map for the skin layers.

RESULTS

An annotated real psoriasis skin biopsy image data set of ninety (90) images is developed and used for this research. The segmentation performance is evaluated with two metrics namely, Jaccard's Coefficient (JC) and the Ratio of Correct Pixel Classification (RCPC) accuracy. The experimental results show that the CNN based approaches outperform the traditional hand-crafted feature based classification approaches.

CONCLUSIONS

The present research shows that practical system can be developed for machine assisted analysis of psoriasis disease.

Absolute cosine-based SVM-RFE feature selection method for prostate histopathological grading

OBJECTIVE

Feature selection (FS) methods are widely used in grading and diagnosing prostate histopathological images. In this context, FS is based on the texture features obtained from the lumen, nuclei, cytoplasm and stroma, all of which are important tissue components. However, it is difficult to represent the high-dimensional textures of these tissue components. To solve this problem, we propose a new FS method that enables the selection of features with minimal redundancy in the tissue components.

METHODOLOGY

We categorise tissue images based on the texture of individual tissue components via the construction of a single classifier and also construct an ensemble learning model by merging the values obtained by each classifier. Another issue that arises is overfitting due to the high-dimensional texture of individual tissue components. We propose a new FS method, SVM-RFE(AC), that integrates a Support Vector Machine-Recursive Feature Elimination (SVM-RFE) embedded procedure with an absolute cosine (AC) filter method to prevent redundancy in the selected features of the SV-RFE and an unoptimised classifier in the AC.

RESULTS

We conducted experiments on H&E histopathological prostate and colon cancer images with respect to three prostate classifications, namely benign vs. grade 3, benign vs. grade 4 and grade 3 vs. grade 4. The colon benchmark dataset requires a distinction between grades 1 and 2, which are the most difficult cases to distinguish in the colon domain. The results obtained by both the single and ensemble classification models (which uses the product rule as its merging method) confirm that the proposed SVM-RFE(AC) is superior to the other SVM and SVM-RFE-based methods.

CONCLUSION

We developed an FS method based on SVM-RFE and AC and successfully showed that its use enabled the identification of the most crucial texture feature of each tissue component. Thus, it makes possible the distinction between multiple Gleason grades (e.g. grade 3 vs. grade 4) and its performance is far superior to other reported FS methods.

Development of a Reference Image Collection Library for Histopathology Image Processing, Analysis and Decision Support Systems Research

Histopathology image processing, analysis and computer-aided diagnosis have been shown as effective assisting tools towards reliable and intra-/inter-observer invariant decisions in traditional pathology. Especially for cancer patients, decisions need to be as accurate as possible in order to increase the probability of optimal treatment planning. In this study, we propose a new image collection library (HICL-Histology Image Collection Library) comprising 3831 histological images of three different diseases, for fostering research in histopathology image processing, analysis and computer-aided diagnosis. Raw data comprised 93, 116 and 55 cases of brain, breast and laryngeal cancer respectively collected from the archives of the University Hospital of Patras, Greece. The 3831 images were generated from the most representative regions of the pathology, specified by an experienced histopathologist. The HICL Image Collection is free for access under an academic license at http://medisp.bme.teiath.gr/hicl/ . Potential exploitations of the proposed library may span over a board spectrum, such as in image processing to improve visualization, in segmentation for nuclei detection, in decision support systems for second opinion consultations, in statistical analysis for investigation of potential correlations between clinical annotations and imaging findings and, generally, in fostering research on histopathology image processing and analysis. To the best of our knowledge, the HICL constitutes the first attempt towards creation of a reference image collection library in the field of traditional histopathology, publicly and freely available to the scientific community.

Histopathology: ditch the slides, because digital and 3D are on show

Due to the growing field of digital pathology, more and more digital histology slides are becoming available. This improves the accessibility, allows teleconsultations from specialized pathologists, improves education, and might give urologist the possibility to review the slides in patient management systems. Moreover, by stacking multiple two-dimensional (2D) digital slides, three-dimensional volumes can be created, allowing improved insight in the growth pattern of a tumor. With the addition of computer-aided diagnosis systems, pathologist can be guided to regions of interest, potentially reducing the workload and interobserver variation. Digital (3D) pathology has the potential to improve dialog between the pathologist and urologist, and, therefore, results in a better treatment selection for urologic patients.

Continuous stacking computational approach based automated microscope slide scanner

Cost-effective and automated acquisition of whole slide images is a bottleneck for wide-scale deployment of digital pathology. In this article, a computation augmented approach for the development of an automated microscope slide scanner is presented. The realization of a prototype device built using inexpensive off-the-shelf optical components and motors is detailed. The applicability of the developed prototype to clinical diagnostic testing is demonstrated by generating good quality digital images of malaria-infected blood smears. Further, the acquired slide images have been processed to identify and count the number of malaria-infected red blood cells and thereby perform quantitative parasitemia level estimation. The presented prototype would enable cost-effective deployment of slide-based cyto-diagnostic testing in endemic areas.

Multi-radial LBP Features as a Tool for Rapid Glomerular Detection and Assessment in Whole Slide Histopathology Images

We demonstrate a simple and effective automated method for the localization of glomeruli in large (~1 gigapixel) histopathological whole-slide images (WSIs) of thin renal tissue sections and biopsies, using an adaptation of the well-known local binary patterns (LBP) image feature vector to train a support vector machine (SVM) model. Our method offers high precision (>90%) and reasonable recall (>70%) for glomeruli from WSIs, is readily adaptable to glomeruli from multiple species, including mouse, rat, and human, and is robust to diverse slide staining methods. Using 5 Intel(R) Core(TM) i7-4790 CPUs with 40 GB RAM, our method typically requires ~15 sec for training and ~2 min to extract glomeruli reproducibly from a WSI. Deploying a deep convolutional neural network trained for glomerular recognition in tandem with the SVM suffices to reduce false positives to below 3%. We also apply our LBP-based descriptor to successfully detect pathologic changes in a mouse model of diabetic nephropathy. We envision potential clinical and laboratory applications for this approach in the study and diagnosis of glomerular disease, and as a means of greatly accelerating the construction of feature sets to fuel deep learning studies into tissue structure and pathology.

Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images

Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human error. In this study, we utilize several computational methods based on convolutional neural networks (CNN) and build a stand-alone pipeline to effectively classify different histopathology images across different types of cancer. In particular, we demonstrate the utility of our pipeline to discriminate between two subtypes of lung cancer, four biomarkers of bladder cancer, and five biomarkers of breast cancer. In addition, we apply our pipeline to discriminate among four immunohistochemistry (IHC) staining scores of bladder and breast cancers. Our classification pipeline includes a basic CNN architecture, Google's Inceptions with three training strategies, and an ensemble of two state-of-the-art algorithms, Inception and ResNet. Training strategies include training the last layer of Google's Inceptions, training the network from scratch, and fine-tunning the parameters for our data using two pre-trained version of Google's Inception architectures, Inception-V1 and Inception-V3. We demonstrate the power of deep learning approaches for identifying cancer subtypes, and the robustness of Google's Inceptions even in presence of extensive tumor heterogeneity. On average, our pipeline achieved accuracies of 100%, 92%, 95%, and 69% for discrimination of various cancer tissues, subtypes, biomarkers, and scores, respectively. Our pipeline and related documentation is freely available at https://github.com/ih-_lab/CNN_Smoothie.