Validation of wastewater data using artificial intelligence tools and the evaluation of their performance regarding annotator agreement.

To prevent the pollution of water resources, the measurement and the limitation of wastewater discharges are required. Despite the progress in the field of data acquisition systems, sensors are subject to malfunctions that can bias the evaluation of the pollution flow. It is therefore essential to identify potential anomalies in the data before any use. The objective of this work is to deploy artificial intelligence tools to automate the data validation and to assess the added value of this approach in assisting the validation performed by an operator. To do so, we compare two state-of-the-art anomaly detection algorithms on turbidity data in a sewer network. On the one hand, we conclude that the One-class SVM model is not adapted to the nature of the studied data which is heterogeneous and noisy. The Matrix Profile model, on the other hand, provides promising results with a majority of anomalies detected and a relatively limited number of false positives. By comparing these results to the expert validation, it turns out that the use of the Matrix Profile model objectifies and accelerates the validation task while maintaining the same level of performance compared to the annotator agreement rate between two experts.

CycleGAN for virtual stain transfer: Is seeing really believing?

Digital Pathology is an area prone to high variation due to multiple factors which can strongly affect diagnostic quality and visual appearance of the Whole-Slide-Images (WSIs). The state-of-the art methods to deal with such variation tend to address this through style-transfer inspired approaches. Usually, these solutions directly apply successful approaches from the literature, potentially with some task-related modifications. The majority of the obtained results are visually convincing, however, this paper shows that this is not a guarantee that such images can be directly used for either medical diagnosis or reducing domain shift.This article shows that slight modification in a stain transfer architecture, such as a choice of normalisation layer, while resulting in a variety of visually appealing results, surprisingly greatly effects the ability of a stain transfer model to reduce domain shift. By extensive qualitative and quantitative evaluations, we confirm that translations resulting from different stain transfer architectures are distinct from each other and from the real samples. Therefore conclusions made by visual inspection or pretrained model evaluation might be misleading.

Data augmentation based on spatial deformations for histopathology: An evaluation in the context of glomeruli segmentation.

BACKGROUND AND OBJECTIVE: The effective application of deep learning to digital histopathology is hampered by the shortage of high-quality annotated images. In this paper we focus on the supervised segmentation of glomerular structures in patches of whole slide images of renal histopathological slides. Considering a U-Net model employed for segmentation, our goal is to evaluate the impact of augmenting training data with random spatial deformations. METHODS: The effective application of deep learning to digital histopathology is hampered by the shortage of high-quality annotated images. In this paper we focus on the supervised segmentation of glomerular structures in patches of whole slide images of renal histopathological slides. Considering a U-Net model employed for segmentation, our goal is to evaluate the impact of augmenting training data with random spatial deformations. RESULTS: We show that augmenting training data with spatially deformed images yields an improvement of up to 0.23 in average Dice score, with respect to training with no augmentation. We demonstrate that deformations with relatively strong distortions yield the best performance increase, while previous work only report the use of deformations with low distortions. The selected deformation models yield similar performance increase, provided that their parameters are properly adjusted. We provide bounds on the optimal parameter values, obtained through parameter sampling, which is achieved in a lower computational complexity with our single-parameter method. The paper is accompanied by a framework for evaluating the impact of random spatial deformations on the performance of any U-Net segmentation model. CONCLUSION: To our knowledge, this study is the first to evaluate the impact of random spatial deformations on the segmentation of histopathological images. Our study and framework provide tools to help practitioners and researchers to make a better usage of random spatial deformations when training deep models for segmentation.

Generation of synthetic training data for SEEG electrodes segmentation.

PURPOSE: Stereoelectroencephalography (SEEG) is a minimally invasive surgical procedure, used to locate epileptogenic zones. An accurate identification of the metallic contacts recording the SEEG signal is crucial to ensure effectiveness of the upcoming treatment. However, due to the presence of metal, post-operative CT scans contain strong streak artefacts that interfere with deep learning segmentation algorithms and require a lot of training data to distinguish from actual contacts. We propose a method to generate synthetic data and use them to train a neural network to precisely locate SEEG electrode contacts. METHODS: Random electrodes were generated following manufacturer's specifications and dimensions and placed in acceptable regions inside metal-free CT images. Metal artefacts were simulated in the generated data set using radon transform, beam hardening, and filtered back projection. A UNet neural network was trained for the contacts segmentation task using various training set-ups combining real data, basic augmented data, and synthetic data. The results were compared. RESULTS: We reported a higher accuracy when including synthetic data during the network training, while training only on real and basic augmented data more often led to misclassified artefacts or missed contacts. The network segments post-operative CT slices in less than 2 s using 4 GeForce RTX2080 Ti GPUs and in under a minute using a standard PC with GeForce GTX1060. CONCLUSION: Using synthetic data to train the network significantly improves contact detection and segmentation accuracy.

An automatic framework for fusing information from differently stained consecutive digital whole slide images: A case study in renal histology.

OBJECTIVE: This article presents an automatic image processing framework to extract quantitative high-level information describing the micro-environment of glomeruli in consecutive whole slide images (WSIs) processed with different staining modalities of patients with chronic kidney rejection after kidney transplantation. METHODS: This four-step framework consists of: 1) approximate rigid registration, 2) cell and anatomical structure segmentation 3) fusion of information from different stainings using a newly developed registration algorithm 4) feature extraction. RESULTS: Each step of the framework is validated independently both quantitatively and qualitatively by pathologists. An illustration of the different types of features that can be extracted is presented. CONCLUSION: The proposed generic framework allows for the analysis of the micro-environment surrounding large structures that can be segmented (either manually or automatically). It is independent of the segmentation approach and is therefore applicable to a variety of biomedical research questions. SIGNIFICANCE: Chronic tissue remodelling processes after kidney transplantation can result in interstitial fibrosis and tubular atrophy (IFTA) and glomerulosclerosis. This pipeline provides tools to quantitatively analyse, in the same spatial context, information from different consecutive WSIs and help researchers understand the complex underlying mechanisms leading to IFTA and glomerulosclerosis.

Crowdsourcing of Histological Image Labeling and Object Delineation by Medical Students.

Crowdsourcing in pathology has been performed on tasks that are assumed to be manageable by nonexperts. Demand remains high for annotations of more complex elements in digital microscopic images, such as anatomical structures. Therefore, this paper investigates conditions to enable crowdsourced annotations of high-level image objects, a complex task considered to require expert knowledge. Seventy six medical students without specific domain knowledge who voluntarily participated in three experiments solved two relevant annotation tasks on histopathological images: 1) labeling of images showing tissue regions and 2) delineation of morphologically defined image objects. We focus on methods to ensure sufficient annotation quality including several tests on the required number of participants and on the correlation of participants' performance between tasks. In a set up simulating annotation of images with limited ground truth, we validated the feasibility of a confidence score using full ground truth. For this, we computed a majority vote using weighting factors based on individual assessment of contributors against scattered gold standard annotated by pathologists. In conclusion, we provide guidance for task design and quality control to enable a crowdsourced approach to obtain accurate annotations required in the era of digital pathology.

Image analysis of immune cell patterns in the human mammary gland during the menstrual cycle refines lymphocytic lobulitis.

PURPOSE: To improve microscopic evaluation of immune cells relevant in breast cancer oncoimmunology, we aim at distinguishing normal infiltration patterns from lymphocytic lobulitis by advanced image analysis. We consider potential immune cell variations due to the menstrual cycle and oral contraceptives in non-neoplastic mammary gland tissue. METHODS: Lymphocyte and macrophage distributions were analyzed in the anatomical context of the resting mammary gland in immunohistochemically stained digital whole slide images obtained from 53 reduction mammoplasty specimens. Our image analysis workflow included automated regions of interest detection, immune cell recognition, and co-registration of regions of interest. RESULTS: In normal lobular epithelium, seven CD8[Formula: see text] lymphocytes per 100 epithelial cells were present on average and about 70% of this T-lymphocyte population was lined up along the basal cell layer in close proximity to the epithelium. The density of CD8[Formula: see text] T-cell was 1.6 fold higher in the luteal than in the follicular phase in spontaneous menstrual cycles and 1.4 fold increased under the influence of oral contraceptives, and not co-localized with epithelial proliferation. CD4[Formula: see text] T-cells were infrequent. Abundant CD163[Formula: see text] macrophages were widely spread, including the interstitial compartment, with minor variation during the menstrual cycle. CONCLUSIONS: Spatial patterns of different immune cell subtypes determine the range of normal, as opposed to inflammatory conditions of the breast tissue microenvironment. Advanced image analysis enables quantification of hormonal effects, refines lymphocytic lobulitis, and shows potential for comprehensive biopsy evaluation in oncoimmunology.

Detection of lobular structures in normal breast tissue.

BACKGROUND: Ongoing research into inflammatory conditions raises an increasing need to evaluate immune cells in histological sections in biologically relevant regions of interest (ROIs). Herein, we compare different approaches to automatically detect lobular structures in human normal breast tissue in digitized whole slide images (WSIs). This automation is required to perform objective and consistent quantitative studies on large data sets. METHODS: In normal breast tissue from nine healthy patients immunohistochemically stained for different markers, we evaluated and compared three different image analysis methods to automatically detect lobular structures in WSIs: (1) a bottom-up approach using the cell-based data for subsequent tissue level classification, (2) a top-down method starting with texture classification at tissue level analysis of cell densities in specific ROIs, and (3) a direct texture classification using deep learning technology. RESULTS: All three methods result in comparable overall quality allowing automated detection of lobular structures with minor advantage in sensitivity (approach 3), specificity (approach 2), or processing time (approach 1). Combining the outputs of the approaches further improved the precision. CONCLUSIONS: Different approaches of automated ROI detection are feasible and should be selected according to the individual needs of biomarker research. Additionally, detected ROIs could be used as a basis for quantification of immune infiltration in lobular structures.

Combat or surveillance? Evaluation of the heterogeneous inflammatory breast cancer microenvironment.

Evaluation of specific lymphocyte subsets is important in understanding the microenvironment in cancer and holds promise as a prognostic parameter in invasive breast cancer. To address this, we used digital image analysis to integrate cell abundance, distance metrics, neighbourhood relationships and sample heterogeneity into comprehensive assessment of immune infiltrates. Lymphocyte and macrophage subpopulations were detected by chromogenic duplex immunohistochemistry for CD3/perforin and CD68/CD163 in samples of invasive breast cancer. The analysis workflow combined commercial and open-source software modules. We confirmed the accuracy of automated detection of cells with lymphoid morphology [concordance correlation coefficient (CCC), 0.92 for CD3(+) -T lymphocytes], whereas variable morphology limited automated classification of macrophages as distinct cellular objects (CCC, 0.43 for object-based detection; 0.79 for pixel-based area analysis). Using a supervised learning algorithm that clustered image areas according to lymphocyte abundance, grouping behaviour and distance to tumour cells, we identified recurrent infiltration patterns reflecting different grades of direct interaction between tumour and immune effector cells. The approach provided comprehensive visual and statistical assessment of the inflammatory tumour microenvironment and allowed quantitative estimation of heterogeneous immune cell distribution. Cases with dense lymphocytic infiltrates (8/33) contained up to 65% of areas in which observed distances between tumour and immune cells suggested a low chance of direct contact, indicating the presence of regions where tumour cells might be protected from immune attack. In contrast, cases with moderate (11/33) or low (14/33) lymphocyte density occasionally comprised areas of focally intense interaction, likely not to be captured by conventional scores. Our approach improves the conventional evaluation of immune cell density scores by translating objective distance metrics into reproducible, largely observer-independent interaction patterns.