Densely Convolutional Spatial Attention Network for nuclei segmentation of histological images for computational pathology

IntroductionAutomatic nuclear segmentation in digital microscopic tissue images can aid pathologists to extract high-quality features for nuclear morphometrics and other analyses. However, image segmentation is a challenging task in medical image processing and analysis. This study aimed to develop a deep learning-based method for nuclei segmentation of histological images for computational pathology.MethodsThe original U-Net model sometime has a caveat in exploring significant features. Herein, we present the Densely Convolutional Spatial Attention Network (DCSA-Net) model based on U-Net to perform the segmentation task. Furthermore, the developed model was tested on external multi-tissue dataset – MoNuSeg. To develop deep learning algorithms for well-segmenting nuclei, a large quantity of data are mandatory, which is expensive and less feasible. We collected hematoxylin and eosin–stained image data sets from two hospitals to train the model with a variety of nuclear appearances. Because of the limited number of annotated pathology images, we introduced a small publicly accessible data set of prostate cancer (PCa) with more than 16,000 labeled nuclei. Nevertheless, to construct our proposed model, we developed the DCSA module, an attention mechanism for capturing useful information from raw images. We also used several other artificial intelligence-based segmentation methods and tools to compare their results to our proposed technique.ResultsTo prioritize the performance of nuclei segmentation, we evaluated the model’s outputs based on the Accuracy, Dice coefficient (DC), and Jaccard coefficient (JC) scores. The proposed technique outperformed the other methods and achieved superior nuclei segmentation with accuracy, DC, and JC of 96.4% (95% confidence interval [CI]: 96.2 – 96.6), 81.8 (95% CI: 80.8 – 83.0), and 69.3 (95% CI: 68.2 – 70.0), respectively, on the internal test data set.ConclusionOur proposed method demonstrates superior performance in segmenting cell nuclei of histological images from internal and external datasets, and outperforms many standard segmentation algorithms used for comparative analysis

3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading

One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D gray level cooccurrence matrix (GLCM) and a 3D wavelet based on two types of basis functions. To evaluate their validity, we predefined 6 different statistical classifiers and applied these to the extracted feature sets. In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results. Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system

Quantitative Analysis of Chemotherapeutic Effects in Tumors Using In Vivo Staining and Correlative Histology

Aims: To microscopically analyze the chemotherapeutic response of tumors using in vivo staining based on an annexinV-Cy5.5 probe and independently asses their apoptotic count using quantitative histological analysis. Methods:: Lewis Lung Carcinomas cells, that are sensitive (CS-LLC) and resistant (CR-LLC) to chemotherapy were implanted in nude mice and grown to tumours. Mice were treated with cyclophosphamide and injected with a Cy5.5-annexinV fluorescent probe. In vivo imaging was performed using Fluorescence Molecular Tomography. Subsequently tumours were excised and prepared for histology. The histological tumour sections were stained for apoptosis using a terminal deoxynucleotidyl transferase-mediated nick end labeling (TUNEL) assay. A minimum of ten tissue sections were analyzed per tumour for apoptosis quantification by TUNEL staining and corresponding Cy5.5 distribution. Results:: We detected higher levels of apoptosis and corresponding higher levels of Cy5.5 fluorescence in the CS-LLC vs. the CR-LLC tumours. The cell count rate on CS-LLC sections over CR-LLC was found to be ∼2 :1 where the corresponding area observed on Cy5.5 distribution measurements revealed a ∼1.7 :1 ratio of CS-LLC over CR-LLC. These observations are consistent with the higher apoptotic index expected from the CS-LLC cell line. Conclusions:: Quantitative analysis of histological slices revealed higher fluorescence and higher apoptotic count in the CS-LLC tumour images compared to the CR-LLC tumour images. These observations demonstrate that the annexinV-Cy5.5 probe sensed the chemotherapeutic effect of cyclophospamide and further confirmed in vivo FMT measurements

Image Analysis Based Grading of Bladder Carcinoma. Comparison of Object, Texture and Graph Based Methods and Their Reproducibility

The possibility that computerized image analysis could increase the reproducibility of grading of bladder carcinoma as compared to conventional subjective grading made by pathologists was investigated. Object, texture and graph based analysis were carried out from Feulgen stained histological tissue sections. The object based features were extracted from gray scale images, binary images obtained by thresholding the nuclei and several other images derived through image processing operations. The textural features were based on the spatial gray‐tone co‐occurrence probability matrices and the graph based features were extracted from the minimum spanning trees connecting all nuclei. The large numbers of extracted features were evaluated in relation to subjective grading and to factors related to prognosis using multivariate statistical methods and multilayer backpropagation neural networks. All the methods were originally developed and tested on material from one patient and then tested for reproducibility on entirely different patient material. The results indicate reasonably good reproducibility for the best sets of features. In addition, image analysis based grading showed almost identical correlation to mitotic density and expression of p53 protein as subjective grading. It should thus be possible to use this kind of image analysis as a prognostic tool for bladder carcinoma

Multi-Resolution Wavelet-Transformed Image Analysis of Histological Sections of Breast Carcinomas

Multi-resolution images of histological sections of breast cancer tissue were analyzed using texture features of Haar- and Daubechies transform wavelets. Tissue samples analyzed were from ductal regions of the breast and included benign ductal hyperplasia, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (CA). To assess the correlation between computerized image analysis and visual analysis by a pathologist, we created a two-step classification system based on feature extraction and classification. In the feature extraction step, we extracted texture features from wavelet-transformed images at 10× magnification. In the classification step, we applied two types of classifiers to the extracted features, namely a statistics-based multivariate (discriminant) analysis and a neural network. Using features from second-level Haar transform wavelet images in combination with discriminant analysis, we obtained classification accuracies of 96.67 and 87.78% for the training and testing set (90 images each), respectively. We conclude that the best classifier of carcinomas in histological sections of breast tissue are the texture features from the second-level Haar transform wavelet images used in a discriminant function

An Efficient Lightweight CNN and Ensemble Machine Learning Classification of Prostate Tissue Using Multilevel Feature Analysis

Prostate carcinoma is caused when cells and glands in the prostate change their shape and size from normal to abnormal. Typically, the pathologist’s goal is to classify the staining slides and differentiate normal from abnormal tissue. In the present study, we used a computational approach to classify images and features of benign and malignant tissues using artificial intelligence (AI) techniques. Here, we introduce two lightweight convolutional neural network (CNN) architectures and an ensemble machine learning (EML) method for image and feature classification, respectively. Moreover, the classification using pre-trained models and handcrafted features was carried out for comparative analysis. The binary classification was performed to classify between the two grade groups (benign vs. malignant) and quantile-quantile plots were used to show their predicted outcomes. Our proposed models for deep learning (DL) and machine learning (ML) classification achieved promising accuracies of 94.0% and 92.0%, respectively, based on non-handcrafted features extracted from CNN layers. Therefore, these models were able to predict nearly perfectly accurately using few trainable parameters or CNN layers, highlighting the importance of DL and ML techniques and suggesting that the computational analysis of microscopic anatomy will be essential to the future practice of pathology

Quantitative Analysis of Benign and Malignant Tumors in Histopathology: Predicting Prostate Cancer Grading Using SVM

An adenocarcinoma is a type of malignant cancerous tissue that forms from a glandular structure in epithelial tissue. Analyzed stained microscopic biopsy images were used to perform image manipulation and extract significant features for support vector machine (SVM) classification, to predict the Gleason grading of prostate cancer (PCa) based on the morphological features of the cell nucleus and lumen. Histopathology biopsy tissue images were used and categorized into four Gleason grade groups, namely Grade 3, Grade 4, Grade 5, and benign. The first three grades are considered malignant. K-means and watershed algorithms were used for color-based segmentation and separation of overlapping cell nuclei, respectively. In total, 400 images, divided equally among the four groups, were collected for SVM classification. To classify the proposed morphological features, SVM classification based on binary learning was performed using linear and Gaussian classifiers. The prediction model yielded an accuracy of 88.7% for malignant vs. benign, 85.0% for Grade 3 vs. Grade 4, 5, and 92.5% for Grade 4 vs. Grade 5. The SVM, based on biopsy-derived image features, consistently and accurately classified the Gleason grading of prostate cancer. All results are comparatively better than those reported in the literature

Targeted cytotoxic effect of anti-JL1 immunotoxin against a human leukemic cell line and its clinical implications

We have previously reported the identification of a unique thymocyte-specific surface molecule, JL1, which was detected using the monoclonal antibody (mAb), anti-JL1. Interestingly, JL1 was shown to be expressed in most leukemias, irrespective of their immunophenotype, and subpopulations of normal bone marrow (BM) mononuclear cells (MNCs). Here we investigated the potential usefulness of the anti-JL1 mAb as a therapeutic tool for leukemia. We demonstrated that the roliferation of cultured human leukemia cells was dramatically inhibited in vitro by anti-JL1 mAb conjugated with the polypeptide toxin, gelonin, but not by gelonin alone. We then systematically investigated the reactivity of the anti-JL1 mAb against normal human tissues to evaluate possible side effects along with various hematopoietic and nonhematopoietic tumor cell lines. All of 33 types of normal tissues except thymus and subpopulation of BM MNCs were clearly devoid of JL1 expression. Among tumor cell lines, all the nonhematopoietic cell lines tested were negative for JL1 expression, while some hematopoietic cell lines contained JL1 antigen. Collectively, the results showed the cytotoxic effects of anti-JL1-based immunotoxin against JL1-positive leukemic cells, sparing most normal tissues other than thymocytes and some BM MNCs. Therefore, we strongly suggest that gelonin-conjugated anti-JL1 mAb immunotoxin could be developed as a potential immunotherapeutic agent in the treatment of various types of JL1-positive acute leukemias.This work was supported in part by a research grant (no. M1010400012401J00000551) from the National Research Laboratory Program of the Korea Ministry of Science and Technology and the 01 DiNonA R&D Project, Seoul, Korea