Our aim was to assess the utility of a novel machine learning software (Orbit Image Analysis) in the histological quantification of acute ischemic stroke (AIS) clots. We analyzed 50 AIS blood clots retrieved using mechanical thrombectomy procedures. Following H&E staining, quantification of clot components was performed by two different methods: a pathologist using a reference standard method (Adobe Photoshop CC) and an experienced researcher using Orbit Image Analysis. Following quantification, the clots were categorized into 3 types: RBC dominant (≥60% RBCs), Mixed and Fibrin dominant (≥60% Fibrin). Correlations between clot composition and Hounsfield Units density on Computed Tomography (CT) were assessed. There was a significant correlation between the components of clots as quantified by the Orbit Image Analysis algorithm and the reference standard approach (ρ = 0.944**, p < 0.001, n = 150). A significant relationship was found between clot composition (RBC-Rich, Mixed, Fibrin-Rich) and the presence of a Hyperdense artery sign using the algorithmic method (X2(2) = 6.712, p = 0.035*) but not using the reference standard method (X2(2) = 3.924, p = 0.141). Orbit Image Analysis machine learning software can be used for the histological quantification of AIS clots, reproducibly generating composition analyses similar to current reference standard methods.

We describe Orbit Image Analysis, an open-source whole slide image analysis tool. The tool consists of a generic tile-processing engine which allows the execution of various image analysis algorithms provided by either Orbit itself or from other open-source platforms using a tile-based map-reduce execution framework. Orbit Image Analysis is capable of sophisticated whole slide imaging analyses due to several key features. First, Orbit has machinelearning capabilities. This deep learning segmentation can be integrated with complex object detection for analysis of intricate tissues. In addition, Orbit can run locally as standalone or connect to the open-source image server OMERO. Another important characteristic is its scale-out functionality, using the Apache Spark framework for distributed computing. In this paper, we describe the use of Orbit in three different real-world applications: quantification of idiopathic lung fibrosis, nerve fibre density quantification, and glomeruli detection in the kidney.

We describe the open-source whole slide image analysis tool Orbit Image Analysis. It is a generic tile-processing engine which allows the execution of various image analysis algorithms provided by either Orbit itself or other open-source solutions using a tile-based map-reduce execution framework. We show its sophisticated machine-learning approach for WSI quantification, and its flexibility by integrating a deep learning segmentation method for complex object detection. It can run locally standalone or connect to the open-source image server OMERO, and provides scale-out functionality to use the Spark framework for distributed computing. We demonstrate the application of Orbit in three real-world use-cases: Idiopathic lung fibrosis, nerve fibre density quantification, and glomeruli detection in kidney. Author summaryWhole slide images (WSI) are digital scans of samples, e.g. tissue sections. It is very convenient to view samples in this digital form, and with the increasing computation power it can also be used for quantification. These images are often too large to be analysed with standard tools. To overcome this issue, we created on open-source tool called Orbit Image Analysis which divides the images into smaller parts and allows the analysis of it with either embedded algorithms or the integration of existing tools. It also provides mechanisms to process huge amounts of images in distributed computing environments such as clusters or cloud infrastructures. In this paper we describe the Orbit system and demonstrate its application based on three real-word use-cases.

Intratracheal administration of bleomycin induces fibrosis in the lung, which is mainly assessed by histopathological grading that is subjective. Current literature highlights the need of reproducible and quantitative pulmonary fibrosis analysis. If some quantitative studies looked at fibrosis parameters separately, none of them quantitatively assessed both aspects: lung tissue remodeling and collagenization. To ensure reliable quantification, support vector machine learning was used on digitalized images to design a fully automated method that analyzes two important aspects of lung fibrosis: (i) areas having substantial tissue remodeling with appearance of dense fibrotic masses and (ii) collagen deposition. Fibrotic masses were identified on low magnification images and collagen detection was performed at high magnification. To insure a fully automated application the tissue classifier was trained on several independent studies that were performed over a period of four years. The detection method generates two different values that can be used to quantify lung fibrosis development: (i) percent area of fibrotic masses and (ii) percent of alveolar collagen. These two parameters were validated using independent studies from bleomycin- and saline-treated animals. A significant change of these lung fibrosis quantification parameters- increased amount of fibrotic masses and increased collagen deposition- were observed upon intratracheal administration of bleomycin and subsequent significant beneficial treatments effects were observed with BIBF-1120 and pirfenidone.

Pathological features of pulmonary fibrosis include accumulation of myofibroblasts and increased extracellular matrix (ECM) deposition in lung tissue. Contractile α–smooth muscle actin (α–SMA)–expressing myofibroblasts that produce and secrete ECM are key effector cells of the disease and therefore represent a viable target for potential novel anti–fibrotic treatments. We used primary normal human lung fibroblasts (NHLF) in two novel high–throughput screening assays to discover molecules that inhibit or revert fibroblast–to–myofibroblast differentiation. A phenotypic high–content assay (HCA) quantified the degree of myofibroblast differentiation, whereas an impedance–based assay, multiplexed with MS / MS quantification of α–SMA and collagen 1 alpha 1 (COL1) protein, provided a measure of contractility and ECM formation. The synthetic prostaglandin E1 (PGE1) alprostadil, which very effectively and potently attenuated and even reversed TGF–β1–induced myofibroblast differentiation, was identified by screening a library of approved drugs. In TGF–β1–induced myofibroblasts the effect of alprostadil was attributed to activation of prostanoid receptor 2 and 4 (EP2 and EP4, respectively). However, selective activation of the EP2 or the EP4 receptor was already sufficient to prevent or reverse TGF–β1–induced NHLF myofibroblast transition. Our high–throughput assays identified chemical structures with potent anti–fibrotic properties acting through potentially novel mechanisms.