Quantifying acute kidney injury in an Ischaemia-Reperfusion Injury mouse model using deep-learning-based semantic segmentation in histology

Use of deep learning for the classification of hyperplastic lymph node and common subtypes of canine lymphomas: a preliminary study

Artificial Intelligence has observed significant growth in its ability to classify different types of tumors in humans due to advancements in digital pathology technology. Among these tumors, lymphomas are quite common in dogs, despite studies on the application of AI in domestic species are scarce. This research aims to employ deep learning (DL) through convolutional neural networks (CNNs) to distinguish between normal lymph nodes and 3 WHO common subtypes of canine lymphomas. To train and validate the CNN, 1,530 high-resolution microscopic images derived from whole slide scans (WSIs) were used, including those of background areas, hyperplastic lymph nodes (n = 4), and three different lymphoma subtypes: diffuse large B cell lymphoma (DLBCL; n = 5), lymphoblastic (LBL; n = 5), and marginal zone lymphoma (MZL; n = 3). The CNN was able to correctly identify 456 images of the possible 457 test sets, achieving a maximum accuracy of 99.34%. The results of this study have demonstrated the feasibility of using deep learning to differentiate between hyperplastic lymph nodes and lymphomas, as well as to classify common WHO subtypes. Further research is required to explore the implications of these findings and validate the ability of the network to classify a broader range of lymphomas.

Combined colour deconvolution and artificial intelligence approach for region-selective immunohistochemical labelling quantification: The example of alpha smooth muscle actin in mouse kidney

Immunohistochemical (IHC) localisation of protein expression is a widely used tool in pathology. This is semi-quantitative and exhibits substantial intra- and inter-observer variability. Digital approaches based on stain quantification applied to IHC are precise but still operator-dependent and time-consuming when regions of interest (ROIs) must be defined to quantify protein expression in a specific tissue area. This study aimed at developing an IHC quantification workflow that benefits from colour deconvolution for stain quantification and artificial intelligence for automatic ROI definition. The method was tested on 10 whole slide images (WSI) of alpha-smooth muscle actin (aSMA) stained mouse kidney sections. The task was to identify aSMA-positive areas within the glomeruli automatically. Total aSMA detection was performed using two channels (DAB, haematoxylin) colour deconvolution. Glomeruli segmentation within the same IHC WSI was performed by training a convolutional neural network with annotated examples of glomeruli. For both aSMA and glomeruli, binary masks were created. Co-localisation was performed by overlaying the masks and assigning red/green colours, with yellow indicative of a co-localised signal. The workflow described and exemplified using the case of aSMA expression in glomeruli can be applied to quantify the expression of IHC markers within different structures of immunohistochemically stained slides. The technique is objective, has a fully automated threshold approach (colour deconvolution phase) and uses AI to eliminate operator-dependent steps.