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Background: Deep learning tasks, which require large numbers of images, are widely applied in digital pathology. This poses challenges especially for supervised tasks since manual image annotation is an expensive and laborious process. This situation deteriorates even more in the case of a large variability of images. Coping with this problem requires methods such as image augmentation and synthetic image generation. In this regard, unsupervised stain translation via GANs has gained much attention recently, but a separate network must be trained for each pair of source and target domains. This work enables unsupervised many-to-many translation of histopathological stains with a single network while seeking to maintain the shape and structure of the tissues., Methods: StarGAN-v2 is adapted for unsupervised many-to-many stain translation of histopathology images of breast tissues. An edge detector is incorporated to motivate the network to maintain the shape and structure of the tissues and to have an edge-preserving translation. Additionally, a subjective test is conducted on medical and technical experts in the field of digital pathology to evaluate the quality of generated images and to verify that they are indistinguishable from real images. As a proof of concept, breast cancer classifiers are trained with and without the generated images to quantify the effect of image augmentation using the synthetized images on classification accuracy., Results: The results show that adding an edge detector helps to improve the quality of translated images and to preserve the general structure of tissues. Quality control and subjective tests on our medical and technical experts show that the real and artificial images cannot be distinguished, thereby confirming that the synthetic images are technically plausible. Moreover, this research shows that, by augmenting the training dataset with the outputs of the proposed stain translation method, the accuracy of breast cancer classifier with ResNet-50 and VGG-16 improves by 8.0% and 9.3%, respectively., Conclusions: This research indicates that a translation from an arbitrary source stain to other stains can be performed effectively within the proposed framework. The generated images are realistic and could be employed to train deep neural networks to improve their performance and cope with the problem of insufficient numbers of annotated images., Competing Interests: The authors declare that they have no competing interests., (© 2023 The Authors.)

Background: In digital pathology, many image analysis tasks are challenged by the need for large and time-consuming manual data annotations to cope with various sources of variability in the image domain. Unsupervised domain adaptation based on image-to-image translation is gaining importance in this field by addressing variabilities without the manual overhead. Here, we tackle the variation of different histological stains by unsupervised stain-to-stain translation to enable a stain-independent applicability of a deep learning segmentation model., Methods: We use CycleGANs for stain-to-stain translation in kidney histopathology, and propose two novel approaches to improve translational effectivity. First, we integrate a prior segmentation network into the CycleGAN for a self-supervised, application-oriented optimization of translation through semantic guidance, and second, we incorporate extra channels to the translation output to implicitly separate artificial meta-information otherwise encoded for tackling underdetermined reconstructions., Results: The latter showed partially superior performances to the unmodified CycleGAN, but the former performed best in all stains providing instance-level Dice scores ranging between 78% and 92% for most kidney structures, such as glomeruli, tubules, and veins. However, CycleGANs showed only limited performance in the translation of other structures, e.g. arteries. Our study also found somewhat lower performance for all structures in all stains when compared to segmentation in the original stain., Conclusions: Our study suggests that with current unsupervised technologies, it seems unlikely to produce "generally" applicable simulated stains., (© 2022 The Authors.)

Whole slide images contain a magnitude of quantitative information that may not be fully explored in qualitative visual assessments. We propose: (1) a novel pipeline for extracting a comprehensive set of visual features, which are detectable by a pathologist, as well as sub-visual features, which are not discernible by human experts and (2) perform detailed analyses on renal images from mice with experimental unilateral ureteral obstruction. An important criterion for these features is that they are easy to interpret, as opposed to features obtained from neural networks. We extract and compare features from pathological and healthy control kidneys to learn how the compartments (glomerulus, Bowman's capsule, tubule, interstitium, artery, and arterial lumen) are affected by the pathology. We define feature selection methods to extract the most informative and discriminative features. We perform statistical analyses to understand the relation of the extracted features, both individually, and in combinations, with tissue morphology and pathology. Particularly for the presented case-study, we highlight features that are affected in each compartment. With this, prior biological knowledge, such as the increase in interstitial nuclei, is confirmed and presented in a quantitative way, alongside with novel findings, like color and intensity changes in glomeruli and Bowman's capsule. The proposed approach is therefore an important step towards quantitative, reproducible, and rater-independent analysis in histopathology., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

Context: Diseases of the hematopoietic system such as leukemia is diagnosed using bone marrow samples. The cell type distribution plays a major role but requires manual analysis of different cell types in microscopy images., Aims: Automated analysis of bone marrow samples requires detection and classification of different cell types. In this work, we propose and compare algorithms for cell localization, which is a key component in automated bone marrow analysis., Settings and Design: We research fully supervised detection architectures but also propose and evaluate several techniques utilizing weak annotations in a segmentation network. We further incorporate typical cell-like artifacts into our analysis. Whole slide microscopy images are acquired from the human bone marrow samples and annotated by expert hematologists., Subjects and Methods: We adapt and evaluate state-of-the-art detection networks. We further propose to utilize the popular U-Net for cell detection by applying suitable preprocessing steps to the annotations., Statistical Analysis Used: Evaluations are performed on a held-out dataset using multiple metrics based on the two different matching algorithms., Results: The results show that the detection of cells in hematopoietic images using state-of-the-art detection networks yields very accurate results. U-Net-based methods are able to slightly improve detection results using adequate preprocessing - despite artifacts and weak annotations., Conclusions: In this work, we propose, U-Net-based cell detection methods and compare with state-of-the-art detection methods for the localization of hematopoietic cells in high-resolution bone marrow images. We show that even with weak annotations and cell-like artifacts, cells can be localized with high precision., Competing Interests: There are no conflicts of interest., (Copyright: © 2021 Journal of Pathology Informatics.)

Background: Virtual microscopy and automated processing of cytological slides are more challenging compared to histological slides. Since cytological slides exhibit a three-dimensional surface and the required microscope objectives with high resolution have a low depth of field, these cannot capture all objects of a single field of view in focus. One solution would be to scan multiple focal planes; however, the increase in processing time and storage requirements are often prohibitive for clinical routine., Materials and Methods: In this paper, we show that it is a reasonable trade-off to scan a single focal plane and automatically reject defocused objects from the analysis. To this end, we have developed machine learning solutions for the automated identification of defocused objects. Our approach includes creating novel features, systematically optimizing their parameters, selecting adequate classifier algorithms, and identifying the correct decision boundary between focused and defocused objects. We validated our approach for computer-assisted DNA image cytometry., Results and Conclusions: We reach an overall sensitivity of 96.08% and a specificity of 99.63% for identifying defocused objects. Applied on ninety cytological slides, the developed classifiers automatically removed 2.50% of the objects acquired during scanning, which otherwise would have interfered the examination. Even if not all objects are acquired in focus, computer-assisted DNA image cytometry still identified more diagnostically or prognostically relevant objects compared to manual DNA image cytometry. At the same time, the workload for the expert is reduced dramatically.