Localization of spleen and kidney organs from CT scans based on classification of slices in rotational views.

This article presents a novel multiple organ localization and tracking technique applied to spleen and kidney regions in computed tomography images. The proposed solution is based on a unique approach to classify regions in different spatial projections (e.g., side projection) using convolutional neural networks. Our procedure merges classification results from different projection resulting in a 3D segmentation. The proposed system is able to recognize the contour of the organ with an accuracy of 88-89% depending on the body organ. Research has shown that the use of a single method can be useful for the detection of different organs: kidney and spleen. Our solution can compete with U-Net based solutions in terms of hardware requirements, as it has significantly lower demands. Additionally, it gives better results in small data sets. Another advantage of our solution is a significantly lower training time on an equally sized data set and more capabilities to parallelize calculations. The proposed system enables visualization, localization and tracking of organs and is therefore a valuable tool in medical diagnostic problems.

siRNA-Mediated MELK Knockdown Induces Accelerated Wound Healing with Increased Collagen Deposition.

Skin wounds remain a significant problem for the healthcare system, affecting the clinical outcome, patients' quality of life, and financial costs. Reduced wound healing times would improve clinical, economic, and social aspects for both patients and the healthcare system. Skin wound healing has been studied for years, but effective therapy that leads to accelerated wound healing remains to be discovered. This study aimed to evaluate the potential of MELK silencing to accelerate wound healing. A vectorless, transient knockdown of the MELK gene using siRNA was performed in a murine skin wound model. The wound size, total collagen, type 3 collagen, vessel size, vessel number, cell proliferation, cell apoptosis, number of mast cells, and immune infiltration by CD45, CD11b, CD45, and CD8a cells were evaluated. We observed that treatment with MELK siRNA leads to significantly faster wound closing associated with increased collagen deposition.

A U-Net based framework to quantify glomerulosclerosis in digitized PAS and H&E stained human tissues.

Reliable counting of glomeruli and evaluation of glomerulosclerosis in renal specimens are essential steps to assess morphological changes in kidney and identify individuals requiring treatment. Because microscopic identification of sclerosed glomeruli performed under the microscope is labor intensive, we developed a deep learning (DL) approach to identify and classify glomeruli as normal or sclerosed in digital whole slide images (WSIs). The segmentation and classification of glomeruli was performed by the U-Net model. Subsequently, glomerular classifications were refined based on glomerular histomorphometry. The U-Net model was trained using patches from Periodic Acid-Schiff (PAS) stained WSIs (n=31) from the AIDPATH - a multi-center dataset, and then tested on an independent set of WSIs (n=20) including PAS (n=6), and hematoxylin and eosin (H&E) stained WSIs (n=14) from four other institutions. The training and test WSIs were obtained from formalin fixed and paraffin embedded blocks with of human kidney specimens each presenting various proportions of normal and sclerosed glomeruli. In the PAS stained WSIs, normal and sclerosed glomeruli were respectively classified with the F1-score of 97.5% and 68.8%. In the H&E stained WSIs, the F1-scores of 90.8% and 78.1% were achieved. Regardless the tissue staining, the glomeruli in the test WSIs were classified with the F1-score of 94.5% (n=923, normal) and 76.8% for (n=261, sclerosed). These results demonstrate for the first time that a framework based on the U-Net model trained with glomerular patches from PAS stained WSIs can reliably segment and classify normal and sclerosed glomeruli in PAS and also H&E stained WSIs. Our approach yielded higher accuracy of glomerular classifications than some of the recently published methods. Additionally, our test set of images with ground truth is publicly available.

Three-Dimensional Volumetric Renal Reconstruction Based on Geometrical Coefficients.

The article presents an innovative method of 3D computer tomography (CT) image reconstruction of kidney. Diagnosis based on CT scanning allows to obtain projections of multi-dimensional object, made from different directions in order to create cross-sectional (2D) slices. Standard techniques for identifying kidneys in CT images analyze each 2D slice separately. It causes different reconstruction accuracy for the same object at its different heights. This is the main problem of a machine-learning systems. Reconstruction error of end-slices of the kidney model is often greater than the error of the kidney's middle part. The main idea of the technique presented in this paper is to analyze the largest coherent 3D spatial-areas. This technique allows to increase the accuracy of kidney detection as well as to decrease the FP (false positive) error. An additional advantage of the developed algorithm is the possibility of obtaining a precise model representing the 3D view of an entire kidney.

Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides.

During the diagnostic workup of lung adenocarcinomas (LAC), pathologists evaluate distinct histological tumor growth patterns. The percentage of each pattern on multiple slides bears prognostic significance. To assist with the quantification of growth patterns, we constructed a pipeline equipped with a convolutional neural network (CNN) and soft-voting as the decision function to recognize solid, micropapillary, acinar, and cribriform growth patterns, and non-tumor areas. Slides of primary LAC were obtained from Cedars-Sinai Medical Center (CSMC), the Military Institute of Medicine in Warsaw and the TCGA portal. Several CNN models trained with 19,924 image tiles extracted from 78 slides (MIMW and CSMC) were evaluated on 128 test slides from the three sites by F1-score and accuracy using manual tumor annotations by pathologist. The best CNN yielded F1-scores of 0.91 (solid), 0.76 (micropapillary), 0.74 (acinar), 0.6 (cribriform), and 0.96 (non-tumor) respectively. The overall accuracy of distinguishing the five tissue classes was 89.24%. Slide-based accuracy in the CSMC set (88.5%) was significantly better (p < 2.3E-4) than the accuracy in the MIMW (84.2%) and TCGA (84%) sets due to superior slide quality. Our model can work side-by-side with a pathologist to accurately quantify the percentages of growth patterns in tumors with mixed LAC patterns.

Image processing methods for the structural detection and gradation of placental villi.

The context-based examination of stained tissue specimens is one of the most important procedures in histopathological practice. The development of image processing methods allows for the automation of this process. We propose a method of automatic segmentation of placental structures and assessment of edema present in placental structures from a spontaneous miscarriage. The presented method is based on texture analysis, mathematical morphology, and region growing operations that are applicable to the heterogeneous microscopic images representing histological slides of the placenta. The results presented in this study were obtained using a set of 50 images of single villi originating from 13 histological slides and was compared with the manual evaluation of the pathologist. In the presented experiments, various structures, such as villi, villous mesenchyme, trophoblast, collagen, and vessels have been recognized. Moreover, the gradation of villous edema for three classes (no villous edema, moderate villous edema, and massive villous edema) has been conducted. Villi images were correctly identified in 98.21%, villous mesenchyme was correctly identified in 83.95%, and the villi evaluation was correct in 74% for the edema degree and 86% for the number of vessels. The presented segmentation method may serve as a support for current manual diagnosis methods and reduce the bias related to individual, subjective assessment of experts.

Content-based analysis of Ki-67 stained meningioma specimens for automatic hot-spot selection.

BACKGROUND: Hot-spot based examination of immunohistochemically stained histological specimens is one of the most important procedures in pathomorphological practice. The development of image acquisition equipment and computational units allows for the automation of this process. Moreover, a lot of possible technical problems occur in everyday histological material, which increases the complexity of the problem. Thus, a full context-based analysis of histological specimens is also needed in the quantification of immunohistochemically stained specimens. One of the most important reactions is the Ki-67 proliferation marker in meningiomas, the most frequent intracranial tumour. The aim of our study is to propose a context-based analysis of Ki-67 stained specimens of meningiomas for automatic selection of hot-spots. METHODS: The proposed solution is based on textural analysis, mathematical morphology, feature ranking and classification, as well as on the proposed hot-spot gradual extinction algorithm to allow for the proper detection of a set of hot-spot fields. The designed whole slide image processing scheme eliminates such artifacts as hemorrhages, folds or stained vessels from the region of interest. To validate automatic results, a set of 104 meningioma specimens were selected and twenty hot-spots inside them were identified independently by two experts. The Spearman rho correlation coefficient was used to compare the results which were also analyzed with the help of a Bland-Altman plot. RESULTS: The results show that most of the cases (84) were automatically examined properly with two fields of view with a technical problem at the very most. Next, 13 had three such fields, and only seven specimens did not meet the requirement for the automatic examination. Generally, the Automatic System identifies hot-spot areas, especially their maximum points, better. Analysis of the results confirms the very high concordance between an automatic Ki-67 examination and the expert's results, with a Spearman rho higher than 0.95. CONCLUSION: The proposed hot-spot selection algorithm with an extended context-based analysis of whole slide images and hot-spot gradual extinction algorithm provides an efficient tool for simulation of a manual examination. The presented results have confirmed that the automatic examination of Ki-67 in meningiomas could be introduced in the near future.

Functional Assessment of Synoptic Pathology Reporting for Ovarian Cancer.

BACKGROUND: Ovarian cancer has one of the highest death/incidence rates and is commonly diagnosed at an advanced stage. In the recent WHO classification, new histotypes were classified which respond differently to chemotherapy. The e-standardized synoptic cancer pathology reports offer the clinicians essential and reliable information. The aim of our project was to develop an e-template for the standardized synoptic pathology reporting of ovarian carcinoma [based on the checklist of the College of American Pathologists (CAP) and the recent WHO/FIGO classification] to introduce a uniform and improved quality of cancer pathology reports. A functional and qualitative evaluation of the synoptic reporting was performed. METHODS: An indispensable module for e-synoptic reporting was developed and integrated into the Hospital Information System (HIS). The electronic pathology system used a standardized structure with drop-down lists of defined elements to ensure completeness and consistency of reporting practices with the required guidelines. All ovarian cancer pathology reports (partial and final) with the corresponding glass slides selected from a 1-year current workflow were revised for the standard structured reports, and 42 tumors [13 borderline tumors and 29 carcinomas (mainly serous)] were included in the study. RESULTS: Analysis of the reports for completeness against the CAP checklist standard showed a lack of pTNM staging in 80% of the partial or final unstructured reports; ICD-O coding was missing in 83%. Much less frequently missed or unstated data were: ovarian capsule infiltration, angioinvasion and implant evaluation. The e-records of ovarian tumors were supplemented with digital macro- and micro-images and whole-slide images. CONCLUSIONS: The e-module developed for synoptic ovarian cancer pathology reporting was easily incorporated into HIS.CGM CliniNet and facilitated comprehensive reporting; it also provided open access to the database for concerned recipients. The e-synoptic pathology reports appeared more accurate, clear and conclusive than traditional narrative reports. Standardizing structured reporting and electronic tools allows open access and downstream utilization of pathology data for clinicians and tumor registries.

Comparison of the manual, semiautomatic, and automatic selection and leveling of hot spots in whole slide images for Ki-67 quantification in meningiomas.

Background. This paper presents the study concerning hot-spot selection in the assessment of whole slide images of tissue sections collected from meningioma patients. The samples were immunohistochemically stained to determine the Ki-67/MIB-1 proliferation index used for prognosis and treatment planning. Objective. The observer performance was examined by comparing results of the proposed method of automatic hot-spot selection in whole slide images, results of traditional scoring under a microscope, and results of a pathologist's manual hot-spot selection. Methods. The results of scoring the Ki-67 index using optical scoring under a microscope, software for Ki-67 index quantification based on hot spots selected by two pathologists (resp., once and three times), and the same software but on hot spots selected by proposed automatic methods were compared using Kendall's tau-b statistics. Results. Results show intra- and interobserver agreement. The agreement between Ki-67 scoring with manual and automatic hot-spot selection is high, while agreement between Ki-67 index scoring results in whole slide images and traditional microscopic examination is lower. Conclusions. The agreement observed for the three scoring methods shows that automation of area selection is an effective tool in supporting physicians and in increasing the reliability of Ki-67 scoring in meningioma.

Computerized System for Quantitative Assessment of Atherosclerotic Plaques in the Femoral and Iliac Arteries Visualized by Multislice Computed Tomography.

OBJECTIVE: The investigation is aimed at the development of a semiautomatic method of examining the femoral and iliac arteries, and quantifying atherosclerotic plaques visible in the multislice computed tomography images. METHODS: We have utilized the advanced morphology and segmentation methods for processing of a series of the images. In particular, a novel sorted pixel intensity approach to segment the artery into the lumen/plaque regions has been used, and effectively combined with the Gaussian mixture modeling to increase the accuracy of the segmentation. RESULTS: Our numerical results are compared with those obtained manually by two experts. Statistics relevant to the progression of atherosclerosis have also been suggested. Results of the semiautomatic tracking of the femoral and iliac arteries and of the quantitative evaluation of atherosclerotic alterations therein have been shown to correspond well with the expert's results. CONCLUSION: The developed system is likely to be valuable tool for supporting the quantitative evaluation of atherosclerotic changes in arteries. SIGNIFICANCE: In its present form the system can be used for planning surgical treatment and/or predicting the course of the atherosclerotic alterations.

Hot-spot selection and evaluation methods for whole slice images of meningiomas and oligodendrogliomas.

The paper presents a combined method for an automatic hot-spot areas selection based on penalty factor in the whole slide images to support the pathomorphological diagnostic procedure. The studied slides represent the meningiomas and oligodendrogliomas tumor on the basis of the Ki-67/MIB-1 immunohistochemical reaction. It allows determining the tumor proliferation index as well as gives an indication to the medical treatment and prognosis. The combined method based on mathematical morphology, thresholding, texture analysis and classification is proposed and verified. The presented algorithm includes building a specimen map, elimination of hemorrhages from them, two methods for detection of hot-spot fields with respect to an introduced penalty factor. Furthermore, we propose localization concordance measure to evaluation localization of hot spot selection by the algorithms in respect to the expert's results. Thus, the results of the influence of the penalty factor are presented and discussed. It was found that the best results are obtained for 0.2 value of them. They confirm effectiveness of applied approach.

Computer approach to recognition of Fuhrman grade of cells in clear-cell renal cell carcinoma.

OBJECTIVE: To present a computerized system for recognition of Fuhrman grade of cells in clear-cell renal cell carcinoma on the basis of microscopic images of the neoplasm cells in application of hematoxylin and eosin staining. STUDY DESIGN: The applied methods use combined gradient and mathematical morphology to obtain nuclei and classifiers in the form of support vector machine to estimate their Fuhrman grade. The starting point is a microscopic kidney image, which is subject to the advanced methods of preprocessing, leading finally to estimation of Fuhrman grade of cells and the whole analyzed image. RESULTS: The results of the numerical experiments have shown that the proposed nuclei descriptors based on different principles of generation are well connected with the Fuhrman grade. These descriptors have been used as the diagnostic features forming the inputs to the classifier, which performs the final recognition of the cells. The average discrepancy rate between the score of our system and the human expert results, estimated on the basis of over 3,000 nuclei, is below 10%. CONCLUSION: The obtained results have shown that the system is able to recognize 4 Fuhrman grades of the cells with high statistical accuracy and agreement with different expert scores. This result gives a good perspective to apply the system for supporting and accelerating the research of kidney cancer.

Morphological evaluation of the iliac and femoral arteries; possibilities and perspectives.

UNLABELLED: The study presented an approach to the morphometric image of atherosclerotic lesions of the final segment of the abdominal aorta, femoral and iliac arteries, considering possible endovascular intervention. The evaluation of these arteries is very important, because they are often used as a point of access for endovascular procedures performed on the peripheral arteries, or within the thoracic and abdominal aorta and its branches, as well as coronary arteries. The aim of the study was to determine morphometric measurements describing the atherosclerotic lesions, including the methodology of their surgical interpretation. MATERIAL AND METHODS: The study group comprised 128 tomograms of patients qualified for surgery. An algorithm based on the mathematical morphology was designed to track the vessels, starting from the division of the common femoral artery, and ending at the bifurcation of the abdominal aorta. We proposed a set of numerical measurements of the observed arterial changes. RESULTS AND CONCLUSIONS: We analysed 128 tomograms with a 94.5% efficiency, and with the assessment accuracy of the degree of lumen reduction (MAE--1.5%). We observed much higher measurement values of local tortuosity of the atherosclerotic arteries (0.3-1 radians), as compared to their anatomical course in a healthy subject (0-0.2 radians). The presented method can be a very accurate and useful tool in the numerical analysis of the lumen distribution of the arteries and atherosclerosis, dedicated to surgeons elaborating management strategies.

Nucleolus detection in the Fuhrman grading system for application in CCRC.

The paper presents a method for nucleolus detection in images of nuclei in clear-cell renal carcinoma (CCRC). The method is based on the similarity of the nuclei image and the two-dimensional paraboloidal window function. The results of numerical experiments performed on almost 2600 images of CCRC nuclei have confirmed the good accuracy of the method. The developed algorithm will be used to accelerate further research in computer-assisted diagnosis of CCRC.

The influence of the microscope lamp filament colour temperature on the process of digital images of histological slides acquisition standardization.

BACKGROUND: The aim of this study is to compare the digital images of the tissue biopsy captured with optical microscope using bright field technique under various light conditions. The range of colour's variation in immunohistochemically stained with 3,3'-Diaminobenzidine and Haematoxylin tissue samples is immense and coming from various sources. One of them is inadequate setting of camera's white balance to microscope's light colour temperature. Although this type of error can be easily handled during the stage of image acquisition, it can be eliminated with use of colour adjustment algorithms. The examination of the dependence of colour variation from microscope's light temperature and settings of the camera is done as an introductory research to the process of automatic colour standardization. METHODS: Six fields of view with empty space among the tissue samples have been selected for analysis. Each field of view has been acquired 225 times with various microscope light temperature and camera white balance settings. The fourteen randomly chosen images have been corrected and compared, with the reference image, by the following methods: Mean Square Error, Structural SIMilarity and visual assessment of viewer. RESULTS: For two types of backgrounds and two types of objects, the statistical image descriptors: range, median, mean and its standard deviation of chromaticity on a and b channels from CIELab colour space, and luminance L, and local colour variability for objects' specific area have been calculated. The results have been averaged for 6 images acquired in the same light conditions and camera settings for each sample. CONCLUSIONS: The analysis of the results leads to the following conclusions: (1) the images collected with white balance setting adjusted to light colour temperature clusters in certain area of chromatic space, (2) the process of white balance correction for images collected with white balance camera settings not matched to the light temperature moves image descriptors into proper chromatic space but simultaneously the value of luminance changes. So the process of the image unification in a sense of colour fidelity can be solved in separate introductory stage before the automatic image analysis.

Using MATLAB software with Tomcat server and Java platform for remote image analysis in pathology.

BACKGROUND: The Matlab software is a one of the most advanced development tool for application in engineering practice. From our point of view the most important is the image processing toolbox, offering many built-in functions, including mathematical morphology, and implementation of a many artificial neural networks as AI. It is very popular platform for creation of the specialized program for image analysis, also in pathology. Based on the latest version of Matlab Builder Java toolbox, it is possible to create the software, serving as a remote system for image analysis in pathology via internet communication. The internet platform can be realized based on Java Servlet Pages with Tomcat server as servlet container. METHODS: In presented software implementation we propose remote image analysis realized by Matlab algorithms. These algorithms can be compiled to executable jar file with the help of Matlab Builder Java toolbox. The Matlab function must be declared with the set of input data, output structure with numerical results and Matlab web figure. Any function prepared in that manner can be used as a Java function in Java Servlet Pages (JSP). The graphical user interface providing the input data and displaying the results (also in graphical form) must be implemented in JSP. Additionally the data storage to database can be implemented within algorithm written in Matlab with the help of Matlab Database Toolbox directly with the image processing. The complete JSP page can be run by Tomcat server. RESULTS: The proposed tool for remote image analysis was tested on the Computerized Analysis of Medical Images (CAMI) software developed by author. The user provides image and case information (diagnosis, staining, image parameter etc.). When analysis is initialized, input data with image are sent to servlet on Tomcat. When analysis is done, client obtains the graphical results as an image with marked recognized cells and also the quantitative output. Additionally, the results are stored in a server database. The internet platform was tested on PC Intel Core2 Duo T9600 2.8 GHz 4 GB RAM server with 768x576 pixel size, 1.28 Mb tiff format images reffering to meningioma tumour (x400, Ki-67/MIB-1). The time consumption was as following: at analysis by CAMI, locally on a server - 3.5 seconds, at remote analysis - 26 seconds, from which 22 seconds were used for data transfer via internet connection. At jpg format image (102 Kb) the consumption time was reduced to 14 seconds. CONCLUSIONS: The results have confirmed that designed remote platform can be useful for pathology image analysis. The time consumption is depended mainly on the image size and speed of the internet connections. The presented implementation can be used for many types of analysis at different staining, tissue, morphometry approaches, etc. The significant problem is the implementation of the JSP page in the multithread form, that can be used parallelly by many users. The presented platform for image analysis in pathology can be especially useful for small laboratory without its own image analysis system.

Accuracy of a remote quantitative image analysis in the whole slide images.

The rationale for choosing a remote quantitative method supporting a diagnostic decision requires some empirical studies and knowledge on scenarios including valid telepathology standards. The tumours of the central nervous system [CNS] are graded on the base of the morphological features and the Ki-67 labelling Index [Ki-67 LI]. Various methods have been applied for Ki-67 LI estimation. Recently we have introduced the Computerized Analysis of Medical Images [CAMI] software for an automated Ki-67 LI counting in the digital images. Aims of our study was to explore the accuracy and reliability of a remote assessment of Ki-67 LI with CAMI software applied to the whole slide images [WSI]. The WSI representing CNS tumours: 18 meningiomas and 10 oligodendrogliomas were stored on the server of the Warsaw University of Technology. The digital copies of entire glass slides were created automatically by the Aperio ScanScope CS with objective 20x or 40x. Aperio's Image Scope software provided functionality for a remote viewing of WSI. The Ki-67 LI assessment was carried on within 2 out of 20 selected fields of view (objective 40x) representing the highest labelling areas in each WSI. The Ki-67 LI counting was performed by 3 various methods: 1) the manual reading in the light microscope - LM, 2) the automated counting with CAMI software on the digital images - DI , and 3) the remote quantitation on the WSIs - as WSI method. The quality of WSIs and technical efficiency of the on-line system were analysed. The comparative statistical analysis was performed for the results obtained by 3 methods of Ki-67 LI counting. The preliminary analysis showed that in 18% of WSI the results of Ki-67 LI differed from those obtained in other 2 methods of counting when the quality of the glass slides was below the standard range. The results of our investigations indicate that the remote automated Ki-67 LI analysis performed with the CAMI algorithm on the whole slide images of meningiomas and oligodendrogliomas could be successfully used as an alternative method to the manual reading as well as to the digital images quantitation with CAMI software. According to our observation a need of a remote supervision/consultation and training for the effective use of remote quantitative analysis of WSI is necessary.

Computer system for cell counting in selected brain tumors at Ki-67 immunohistochemical staining.

OBJECTIVE: To present a computerized system for cell counting in histopathologic slides of meningioma and oligodendroglioma stained immunohistochemically against Ki-67 antigen and discuss the variability of tumor cell numbers in the field of view of analyzed neoplasms to standardize tumor cellularity. STUDY DESIGN: A computer program using an algorithm based on mathematical morphology was developed to perform quantitative evaluation of slides. That solution was combined with the Support Vector Machine used for classification of cell immunoreactivity. RESULTS: The mean number of cells in the analyzed field of view from patients with meningioma was 623. Of these, 95% were in the 386-781 cells range. In oligodendrogliomas the mean was 474 cells and all results were in the 204-736 range. The mean relative discrepancy between results of our system and human expert score was 8%. CONCLUSION: The proposed system appeared to be an efficient tool for supporting histopathologic diagnosis. The applied sequential thresholding simulated well the human process of cell recognition. Cellularity of the analyzed tumors did not show stability within the specimens from different patients. The results were also highly variable in different fields of view obtained from the same patient.

Comparative analysis of methods for accurate recognition of cells through nuclei staining of Ki-67 in neuroblastoma and estrogen/progesterone status staining in breast cancer.

OBJECTIVE: To compare 2 automatic systems for the recognition and counting of 2 different families of cells through nuclei staining: Ki-67 in neuroblastoma and estrogen/progesterone (ER/PR) status staining in breast cancer. STUDY DESIGN: Morphology-based segmentation strategies and the Support Vector Machine approach have been used for the accurate extraction and recognition of the cells. To achieve the highest possible accuracy, 2 specialized systems specially suited for Ki-67 and ER/PR staining have been developed. RESULTS: The testing set of histologic slides of Ki-67 and ER/PR staining has been assessed by our system and the results compared to the score of a human expert. The results are in good agreement. The average differences are within the acceptable limits of 10%. The main advantage of the system is its absolute repeatability of scores. CONCLUSION: The proposed computer-assisted automatic system of cell extraction and recognition through nuclei staining has confirmed sufficient accuracy for the tested images and may provide a useful tool for cell recognition and counting on the basis of histologic slides with Ki-67 and ER/PR staining.