Abstract: Automated systems and methods are presented for retrospectively analyzing clinical trial data. A plurality of image derived from biological samples of patients in a cohort population are accessed. Image features are computed based on the plurality of images. A diagnostic feature metric is derived based on the computed image features. A cut point value is determined by applying a statistical minimization method using the derived diagnostic feature metric and patient outcome data from the cohort population, in which the cut point value identifies a patient in the cohort population as positive or negative for a diagnostic test.

Abstract: Disclosed herein are systems and methods for of assessing stain titer levels. An exemplary method includes generating a set of field of views for the image or the region of the image, selecting field of views from the set of field of views that meet predefined criteria, creating a series of patches within each of the selected field of views, retaining patches from the series of patches that meet predefined criteria indicative of a presence of the stain for which the titer is to be estimated, deriving stain color features and stain intensity features pertaining to the stain from the retained patches, estimating a titer score for each of the retained patches based on the stain color features and the stain intensity features, and calculating a weighted average score for the titer of the stain based on the estimated titer score for each of the retained patches.

Abstract: Techniques described herein pertain to automated systems and methods for analyzing, storing, and/or retrieving information associated with biological objects including lymphocytes. In some embodiments, a shape metric is derived for each detected and segmented lymphocyte and the shape metric is stored along with other relevant data.

Abstract: Embodiments disclosed herein generally relate to representative datasets for biomedical machine learning models. Particularly, aspects of the present disclosure are directed to identifying a representative distribution of characteristics for a disease, generating a dataset comprising a set of biomedical images, wherein the dataset has a distribution of the characteristics that corresponds to the representative distribution of the characteristics for the disease, processing the dataset using a trained machine learning model, and outputting a result of the processing, wherein the result corresponds to a prediction that a biomedical image of the dataset includes a depiction of a set of tumor cells or other structural and/or functional biological entities associated with the disease, the biomedical image is associated with a diagnosis of the disease, the biomedical image is associated with a classification of the disease, and/or the biomedical image is associated with a prognosis for the disease.

Abstract: Disclosed herein are novel chromogenic conjugates, the conjugates comprising at least two detectable moieties.

Abstract: The present disclosure relates to techniques for efficient development of initial models and efficient model update and/or adaptation to a different image domain using an adaptive learning framework. For efficient development of initial models, a two-step development strategy may be performed as follows: Phase 1: Model preconditioning, where an artificial intelligence system leverages existing annotated datasets and improves learning skills through training of these datasets; and Phase 2: Target-model training, where an artificial intelligence system utilizes the learning skills learned from Phase 1 to extend itself to a different image domain (target domain) with less number of annotations required in the target domain than conventional learning methods.

Abstract: Devices and methods for the deposition of reagents onto cells or tissue samples are disclosed. Also disclosed are reagent compositions suitable for dispensing via a droplet-on-demand system.

Abstract: Scoring functions for predicting response of a dMMR and/or MSI-H colorectal tumor to a PD-1 axis-directed therapy are disclosed, as well as methods and systems for evaluating tissue samples for the presence of feature metrics useful in computing such scoring functions. The scoring functions integrate one or more spatial relationships between cell types into a numerical indication of the likelihood that the tumor will respond to the PD-1 axis-directed therapy. Based on the output of the scoring function, a subject may then be selected to receive a PD-1 axis-directed therapy (if the scoring function indicates a sufficient likelihood of positive response) or an alternative therapy (if the scoring function indicates an insufficient likelihood of positive response).

Abstract: Automated systems to make target compounds from slide stainer waste streams inactive utilizing advanced oxidation processes are described herein. Advanced oxidation processes are promoted by UV irradiation and further accelerated by use of radical initiators, such as hydrogen peroxide. The automated systems further include mechanisms for segregating components of the waste streams.

Abstract: Systems and methods described herein relate, among other things, to unmixing more than three stains, while preserving the biological constraints of the biomarkers. Unlimited numbers of markers may be unmixed from a limited-channel image, such as an RGB image, without adding any mathematical complicity to the model. Known co-localization information of different biomarkers within the same tissue section enables defining fixed upper bounds for the number of stains at one pixel. A group sparsity model may be leveraged to explicitly model the fractions of stain contributions from the co-localized biomarkers into one group to yield a least squares solution within the group. A sparse solution may be obtained among the groups to ensure that only a small number of groups with a total number of stains being less than the upper bound are activated.

Abstract: A method is provided for operating a microscope scanner 1 comprising a line scanner 3. Pre-scan data is generated for a target 6 comprising a sample, wherein generating the pre-scan data comprises: obtaining a pre-scan image of the target 6 from a pre-scan; and identifying one or more sample-containing regions 24 and one or more sample-free regions 26 of the target 6 from the pre-scan image. An imaging scan is then performed of a scanning region of the target 6 including the one or more sample-containing regions 24 and the one or more sample-free regions 26. The scanning speed of the line scanner 3 is adjusted along the image scan path based on the pre-scan data so that the target 6 is imaged at a higher scanning speed within the one or more sample-free regions 26 than in the one or more sample-containing regions 24.

Abstract: A method for fixing a biological sample includes delivering energy through a biological sample that has been removed from a subject, while fixing the biological sample. A change in speed of the energy traveling through the biological sample is evaluated to monitor the progress of the fixation. A system for performing the method can include a transmitter that outputs the energy and a receiver configured to detect the transmitted energy. A computing device can evaluate the speed of the energy based on signals from the receiver.

Abstract: Disclosed herein are novel quinone methide analog precursors and embodiments of a method and a kit of using the same for detecting one or more targets in a biological sample. The method of detection comprises contacting the sample with a detection probe, then contacting the sample with a labeling conjugate that comprises an enzyme. The enzyme interacts with a quinone methide analog precursor comprising a detectable label, forming a reactive quinone methide analog, which binds to the biological sample proximally to or directly on the target. The detectable label is then detected. In some embodiments, multiple targets can be detected by multiple quinone methide analog precursors interacting with different enzymes without the need for an enzyme deactivation step.

Abstract: A microscope scanner is provided comprising a detector array for obtaining an image from a sample and a sample holder configured to move relative to the detector array. The sample holder can be configured to move to a plurality of target positions relative to the detector array in accordance with position control signals issued by a controller and the detector array is configured to capture images during an imaging scan based on the position control signals.

Abstract: The disclosure relates to devices, systems and methods for image registration and annotation. The devices include computer software products for aligning whole slide digital images on a common grid and transferring annotations from one aligned image to another aligned image on the basis of matching tissue structure. The systems include computer-implemented systems such as work stations and networked computers for accomplishing the tissue-structure based image registration and cross-image annotation. The methods include processes for aligning digital images corresponding to adjacent tissue sections on a common grid based on tissue structure, and transferring annotations from one of the adjacent tissue images to another of the adjacent tissue images.

Abstract: Embodiments described herein pertain to systems and methods for imaging. An imaging system may include a castellated optical element, a CMOS image sensor, and color filtering elements. The CMOS image sensor may include focus areas, a line-scan area, a 2D imaging area, and look-ahead gaps. The imaging system may be configured scan and capture bi-directionally, forward-focused brightfield and fluorescence images of one or more slides comprising at least one biological material.

Abstract: Methods, computer-program products and systems are provided to perform actions including: receiving an image and displaying the image using a graphical user interface; receiving at least one first image annotation provided by a user via the graphical user interface; producing a first segmented image using a deep learning model, wherein the deep learning model uses the digital pathology image and the at least one first image annotation; and displaying the first segmented image using the graphical user interface; receiving at least one second image annotation provided by the user via the graphical user interface; producing a second segmented image using the deep learning model, wherein the deep learning model uses the digital pathology image, the at least one first image annotation, and the at least one second image annotation; and displaying the second segmented image using the graphical user interface.

Abstract: A microscope scanner is provided comprising a detector array for obtaining an image from a sample and a sample holder configured to move relative to the detector array. The sample holder can be configured to move to a plurality of target positions relative to the detector array in accordance with position control signals issued by a controller and the detector array is configured to capture images during an imaging scan based on the position control signals.

Abstract: A method of preparing a stage for use in a slide imaging apparatus including positioning a stage in relation to a flat surface so that the flat surface is positioned in front of the top surfaces of slide support pin bases on the top surface of the stage. The method also includes injecting a fluid pin surfacing material configured to solidify into the hole of each slide support pin base so that at least some of the fluid pin surfacing material exits the hole at the top surface of the slide support pin bases and pushes up against the flat surface. The method also includes removing the flat surface so that a tip of solid pin surfacing material is formed on the top surface of each slide support pin base, thereby providing the stage with a plurality of slide support pins.

Abstract: The present disclosure relates to techniques for segmenting and detecting cells within image data using transfer learning and a multi-task scheduler. Particularly, aspects of the present disclosure are directed to accessing a plurality of images of one or more cells, extracting three labels from the plurality of images, where the three labels are extracted using a Voronoi transformation, a local clustering, and application of repel code, training, by a multi-task scheduler, a convolutional neural network model based on three loss functions corresponding to the three labels, generating, by the convolutional neural network model, a nuclei probability map and a background probability map for each of the plurality of images based on the training with the three loss functions, and providing the nuclei probability map and the background probability map.