A user-friendly tool for cloud-based whole slide image segmentation with examples from renal histopathology

Background: Image-based machine learning tools hold great promise for clinical applications in pathology research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often lack the programming experience required for the setup and use of these tools which often rely on the use of command line interfaces. Methods: We have developed Results: By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in three murine models. Conclusions

Iterative annotation to ease neural network training: Specialized machine learning in medical image analysis

Neural networks promise to bring robust, quantitative analysis to medical fields, but adoption is limited by the technicalities of training these networks. To address this translation gap between medical researchers and neural networks in the field of pathology, we have created an intuitive interface which utilizes the commonly used whole slide image (WSI) viewer, Aperio ImageScope (Leica Biosystems Imaging, Inc.), for the annotation and display of neural network predictions on WSIs. Leveraging this, we propose the use of a human-in-the-loop strategy to reduce the burden of WSI annotation. We track network performance improvements as a function of iteration and quantify the use of this pipeline for the segmentation of renal histologic findings on WSIs. More specifically, we present network performance when applied to segmentation of renal micro compartments, and demonstrate multi-class segmentation in human and mouse renal tissue slides. Finally, to show the adaptability of this technique to other medical imaging fields, we demonstrate its ability to iteratively segment human prostate glands from radiology imaging data.Comment: 15 pages, 7 figures, 2 supplemental figures (on the last page

MO077AUTOMATIC SEGMENTATION OF ARTERIES, ARTERIOLES AND GLOMERULI IN NATIVE BIOPSIES WITH THROMBOTIC MICROANGIOPATHY AND OTHER VASCULAR DISEASES

Abstract Background and Aims Thrombotic microangiopathies (TMAs) manifest themselves in arteries, arterioles and glomeruli. Nephropathologists need to differentiate TMAs from mimickers like hypertensive nephropathy and vasculitis which can be problematic due to interobserver disagreement and poorly defined diagnostic criteria over a wide spectrum of morphological changes with partial overlap. As a first step towards a machine learning analysis of TMAs, we developed a computer vision model for segmenting arteries, arterioles and glomeruli in TMA and mimickers. Method We manually segmented n=939 arteries, n=6,023 arterioles, n=4,507 glomeruli on whole slide images (WSIs) of 34 renal biopsies and their HE, PAS, trichrome and Jones sections (19 TMA, 11 hypertensive nephropathy, 4 vasculitis with preglomerular involvement). As a segmentation model we used DeepLab V3, pretrained on 61,734 segmented glomeruli from 768 WSIs. 58 randomly chosen WSIs served as the intrainstitutional holdout testing set after training of the model on the remaining slides. Automatic segmentation accuracies were reported as Cohen's kappa, intersection over union (IoU) and Matthews correlation coefficient (MCC) against the nephropathologist's segmentation as ground truth. Results Over all classes (artery, arteriole, glomerulus) Cohen's kappa was 0.86. IoU was 0.716 for artery, 0.491 for arteriole and 0.829 for glomerulus. MCC was 0.837 for artery, 0.664 for arteriole and 0.907 for glomerulus. Conclusion We achieved good automatic segmentation of arteries, arterioles and glomeruli, even with severe pathological distortion on routine histopathological slides. We will further improve this segmentation technology in order to enable the bulk analysis of these descisive tissue compartments in large clinicopathological repositories of native kidney biopsies with TMA using supervised and unsupervised machine learning algorithms

Iterative annotation to ease neural network training: Specialized machine learning in medical image analysis

Neural networks promise to bring robust, quantitative analysis to medical fields, but adoption is limited by the technicalities of training these networks. To address this translation gap between medical researchers and neural networks in the field of pathology, we have created an intuitive interface which utilizes the commonly used whole slide image (WSI) viewer, Aperio ImageScope (Leica Biosystems Imaging, Inc.), for the annotation and display of neural network predictions on WSIs. Leveraging this, we propose the use of a human-in-the-loop strategy to reduce the burden of WSI annotation. We track network performance improvements as a function of iteration and quantify the use of this pipeline for the segmentation of renal histologic findings on WSIs. More specifically, we present network performance when applied to segmentation of renal micro compartments, and demonstrate multi-class segmentation in human and mouse renal tissue slides. Finally, to show the adaptability of this technique to other medical imaging fields, we demonstrate its ability to iteratively segment human prostate glands from radiology imaging data.Comment: 15 pages, 7 figures, 2 supplemental figures (on the last page

Beyond narratives of victim and villain: Characteristics and service needs of domestic minor victims of sex trafficking, and the challenges of service delivery

Young people involved in the sex trade have existed throughout history. Following passage of the 2000 Trafficking Victims Protection Act (TVPA) by the U.S. Congress, 22 U.S.C. 7102(8), U.S. citizens and lawful permanent residents who are under the age of 18 and trading sex in any capacity are now considered domestic minor victims of human trafficking. With the TVPA's passage, public awareness and services for victims of human trafficking increased. However, strategies to meet the needs of these young people are in the early stages of development. Knowledge gaps exist about the characteristics of young people who trade sex, what services these young people would like to receive, and the challenges of service provision. This study focused on three agencies that received funding to work with domestic minor victims of sex trafficking: the SAGE Project, Inc. in San Francisco, the Streetwork Project at Safe Horizon in New York, and the STOP-IT Program at Salvation Army in Chicago. This study addressed the following questions through a secondary analysis of quantitative and qualitative data: (1) What are the characteristics of young people who trade sex?; (2) What services do the young people request and what do they receive?; and (3) What are the challenges case managers experience in their work with this population