The interpretation of tissue images is the first step performed by the pathologist before render an accurate diagnosis. Digital Pathology is challenging to implement in developing countries since, due to the acquisition process, these images usually become low illuminated and contrasted, which significantly affects their analysis. Transformation of these low-quality images would be needed to improve the pathologist diagnosis process. This article aims to show the results obtained from a test carried out by three pathologists on a series of classic color manipulation algorithms that improve the information (i.e., nuclear shape, stroma, and gland formation) contained in prostate images. For this study, images from prostatic tissue supplied by the Instituto Caldense de Patologia (ICP) were used. Seven biopsy samples were used, each of them captured with 10x, 20x, and 40x magnifications, forming a total of 50 images. Then, a subjective quality test was performed by three pathologists. This test consisted of grading a series of 42 classical image processing algorithms on a scale of 0 to 4. The value 0 represents the worst rating (the new image did not provide any information), and 4 represents the best rating (the original image highlights exciting diagnosis features and offers essential information). 52.27% of the transformations were classified as useful (scores between 3-4) by the pathologists (23 of 44 in total). The color transformations succeeded in highlighting the different structures such as the cytoplasm, stroma, and nucleus, improving the perception of contours and shapes, which will help to render an accurate diagnosis.
Automatic detection and quantification of glands in gastric cancer may contribute to objectively measure the lesion severity, to develop strategies for early diagnosis, and most importantly to improve the patient categorization. This article presents an entire framework for automatic detection of glands in gastric cancer images. This approach starts by selecting gland candidates from a binarized version of the hematoxylin channel. Next, the gland’s shape and nuclei are characterized using local features which feed a Monte Carlo Cross validation method classifier trained previously with manually labeled images. Validation was carried out using a dataset with 1330 annotated structures (2372 glands) from seven fields of view extracted from gastric cancer whole slide images. Results showed an accuracy of 93% using a simple linear classifier. The presented strategy is quite simple, flexible and easily adapted to an actual pathology laboratory.
Dermatopathology education meaningfully relies on consultation of books, which are expensive, quickly outdated and have limited possibilities. In recent years, virtual microscopy, a method that enables examination of digitized microscopy samples, has earn interest because of its possibilities in terms of interaction, availability, usability, low costs and adaptation to multiple clinic scenarios. This work introduces a customized low-cost system for consultation of dermatopathology samples. First, physical slides are digitized using an optical microscope coupled to a digital camera controlled by a custom-motorized scanner. Then, digitized images are automatically stitched to assembly the Whole Slide Image (WSI). A web application, developed using open source tools, gives access to such WSI and allows users to interact with the content by panning and zooming. The application also allows to hand-free annotate specific regions. A set of 100 dermatopathology slides, provided by the Pathology Department of the Universidad Nacional de Colombia, representing basic lesions and inflammatory skin diseases (based on Ackerman patterns) were digitized. Each WSI contains diagnosis and annotations of relevant regions. The platform is currently being used by trainees who highlight the benefits of this kind of tools that complement their training and help to improve their diagnostic skills.
Tumor-infiltrating lymphocytes (TILs) have proved to play an important role in predicting prognosis, survival, and response to treatment in patients with a variety of solid tumors. Unfortunately, currently, there are not a standardized methodology to quantify the infiltration grade. The aim of this work is to evaluate variability among the reports of TILs given by a group of pathologists who examined a set of digitized Non-Small Cell Lung Cancer samples (n=60). 28 pathologists answered a different number of histopathological images. The agreement among pathologists was evaluated by computing the Kappa index coefficient and the standard deviation of their estimations. Furthermore, TILs reports were correlated with patient’s prognosis and survival using the Pearson’s correlation coefficient. General results show that the agreement among experts grading TILs in the dataset is low since Kappa values remain below 0.4 and the standard deviation values demonstrate that in none of the images there was a full consensus. Finally, the correlation coefficient for each pathologist also reveals a low association between the pathologists’ predictions and the prognosis/survival data. Results suggest the need of defining standardized, objective, and effective strategies to evaluate TILs, so they could be used as a biomarker in the daily routine.
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