Pathologists face a substantial increase in workload and complexity of histopathologic cancer diagnosis due to the advent of personalized medicine. Therefore, diagnostic protocols have to focus equally on efficiency and accuracy. In this paper we introduce ‘deep learning’ as a technique to improve the objectivity and efficiency of histopathologic slide analysis. Through two examples, prostate cancer identification in biopsy specimens and breast cancer metastasis detection in sentinel lymph nodes, we show the potential of this new methodology to reduce the workload for pathologists, while at the same time increasing objectivity of diagnoses. We found that all slides containing prostate cancer and micro- and macro-metastases of breast cancer could be identified automatically while 30–40% of the slides containing benign and normal tissue could be excluded without the use of any additional immunohistochemical markers or human intervention. We conclude that ‘deep learning’ holds great promise to improve the efficacy of prostate cancer diagnosis and breast cancer staging.

Variations in the color and intensity of hematoxylin and eosin (H&E) stained histological slides can potentially hamper the effectiveness of quantitative image analysis. This paper presents a fully automated algorithm for standardization of whole-slide histopathological images to reduce the effect of these variations. The proposed algorithm, called whole-slide image color standardizer (WSICS), utilizes color and spatial information to classify the image pixels into different stain components. The chromatic and density distributions for each of the stain components in the hue-saturation-density color model are aligned to match the corresponding distributions from a template whole-slide image (WSI). The performance of the WSICS algorithm was evaluated on two datasets. The first originated from 125 H&E stained WSIs of lymph nodes, sampled from 3 patients, and stained in 5 different laboratories on different days of the week. The second comprised 30 H&E stained WSIs of rat liver sections. The result of qualitative and quantitative evaluations using the first dataset demonstrate that the WSICS algorithm outperforms competing methods in terms of achieving color constancy. The WSICS algorithm consistently yields the smallest standard deviation and coefficient of variation of the normalized median intensity measure. Using the second dataset, we evaluated the impact of our algorithm on the performance of an already published necrosis quantification system. The performance of this system was significantly improved by utilizing the WSICS algorithm. The results of the empirical evaluations collectively demonstrate the potential contribution of the proposed standardization algorithm to improved diagnostic accuracy and consistency in computer-aided diagnosis for histopathology data.

This paper presents data on the sources of variation of the widely used hematoxylin and eosin (H&E) histological staining, as well as a new algorithm to reduce these variations in digitally scanned tissue sections. Experimental results demonstrate that staining protocols in different laboratories and staining on different days of the week are the major factors causing color variations in histopathological images. The proposed algorithm for standardizing histology slides is based on an initial clustering of the image into two tissue components having different absorption characteristics for different dyes. The color distribution for each tissue component is standardized by aligning the 2D histogram of color distribution in the hue-saturation-density (HSD) model. Qualitative evaluation of the proposed standardization algorithm shows that color constancy of the standardized images is improved. Quantitative evaluation demonstrates that the algorithm outperforms competing methods. In conclusion, the paper demonstrates that staining variations, which may potentially hamper usefulness of computer assisted analysis of histopathological images, can be reduced considerably by applying the proposed algorithm.

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Background/Aims: Aberrant skin barrier and intercorneocyte adhesion are potential contributors to the pathomechanism of sensitive skin (SS). Here we aimed to develop a novel and easy-to-apply method to analyze corneodesmosomes and to interrogate potential differences between corneocytes of subjects with SS and non-SS (NSS). Methods: Corneocytes of the volar forearm and upper outer quadrant of the left buttock of SS (n = 10) and NSS (n = 8) subjects were extracted as a function of depth using adhesive tape and stained with anti-desmoglein 1 (DSG1) antibody. The total area of corneocytes and the number and average size of cells per tape was estimated using image processing. Results: The total area of extracted corneocytes and the quantity of DSG1 decreased with depth. The level of decrease, total area of corneocytes, and average area of individual cells differed between anatomical locations. In SS, a larger total area of extracted corneocytes and a larger average cell size per tape was found at all inspected depths. Conclusion: The developed novel and easy-to-apply approach allows investigation of corneodesmosome components. We confirm a role of altered corneocytes in the pathomechanism of SS. The disclosed protocol can further be optimized in studies of skin conditions with strongly affected corneodesmosomes.