Tucson, AZ, United States
Tucson, AZ, United States

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Patent
Ventana Medical Systems | Date: 2017-03-22

The present invention relates to antibody-signal generating moiety conjugates, wherein the antibody is covalently linked to the signal generating moiety through a heterobifunctional polyethylene glycol (PEG) linker.


Patent
Ventana Medical Systems | Date: 2017-05-03

The disclosure presents systems and methods for processing a specimen slide using a specimen processing system. Specimen slides may sequentially be ejected from a slide carrier towards a label reader to determine an appropriate processing protocol for the slides. A cracking element may be engaged with the slide carrier prior to ejecting the slide, in order to break or crack any residual adhesive bond between edges of a slide and walls of the carrier. The specimen slides may be horizontally aligned and resting on a corresponding plurality of flat shelves, enabling even spacing via gravitational forces. The slides are therefore evenly spaced, enabling damage-free ejection of slides, and proper positioning for additional operations including scanning, viewing, heating, washing, and other processing. The label reader may process scanned information from the label of the slide to determine one or more attributes of the slide, and to generate an order or sequence of operations to be thereafter performed on the slide.


Patent
Ventana Medical Systems | Date: 2017-01-06

An apparatus and method for selecting and dispensing coverglasses over specimens on slides for the purpose of viewing specimens through a microscope. The selecting device contains suctioning mechanisms for picking up a coverglass from a stack of coverglasses. It also contains the ability to shape the coverglass to assist in separating and laying down of the coverglasses with a reduction in the creation of bubbles in the fluid.


Methods, systems, and apparatuses for automatically identifying glandular regions and tubule regions in a breast tissue sample are provided. An image of breast tissue is analyzed to detect nuclei and lumen candidates, identify tumor nuclei and true lumen from the candidates, and group tumor nuclei with neighboring tumor nuclei and lumina to define tubule glandular regions and non-tubule glandular regions of the image. Learnt supervised classifiers, such as random forest classifiers, can be applied to identify and classify the tumor nuclei and true lumina. Graph-cut methods can be applied to group the tumor nuclei and lumina and to define the tubule glandular regions and non-tubule glandular regions. The analysis can be applied to whole slide images and can resolve tubule areas with multiple layers of nuclei.


The subject disclosure presents systems and computer-implemented methods for automatic immune cell detection that is of assistance in clinical immune profile studies. The automatic immune cell detection method involves retrieving a plurality of image channels from a multi-channel image such as an RGB image or biologically meaningful unmixed image. A cell detector is trained to identify the immune cells by a convolutional neural network in one or multiple image channels. Further, the automatic immune cell detection algorithm involves utilizing a non-maximum suppression algorithm to obtain the immune cell coordinates from a probability map of immune cell presence possibility generated from the convolutional neural network classifier.


Patent
Genentech, Spring Bioscience Corporation and Ventana Medical Systems | Date: 2017-05-17

The invention provides programmed death-ligand 1 (PD-L1 ) antibodies and methods of using the same.


The subject disclosure presents systems and computer-implemented methods for assessing a risk of cancer recurrence in a patient based on a holistic integration of large amounts of prognostic information for said patient into a single comparative prognostic dataset. A risk classification system may be trained using the large amounts of information from a cohort of training slides from several patients, along with survival data for said patients. For example, a machine-learning-based binary classifier in the risk classification system may be trained using a set of granular image features computed from a plurality of slides corresponding to several cancer patients whose survival information is known and input into the system. The trained classifier may be used to classify image features from one or more test patients into a low-risk or high-risk group.


Patent
Ventana Medical Systems | Date: 2017-03-08

Process and reagent system for removing hematoxylin precipitate, and in particular automated processes for the removal of hematoxylin precipitate from automated staining equipment utilizing said reagent system.


Patent
Ventana Medical Systems | Date: 2017-05-17

An apparatus and method for selecting and dispensing coverglasses over specimens on slides for the purpose of viewing specimens through a microscope. The selecting device contains suctioning mechanisms for picking up a coverglass from a stack of coverglasses. It also contains the ability to shape the coverglass to assist in separating and laying down of the coverglasses with a reduction in the creation of bubbles in the fluid.


Patent
Providence Health & Services Oregon D B A and Ventana Medical Systems | Date: 2017-04-05

Systems and methods for automatic FOV selection in immunoscore computation that involve reading images for individual markers from an unmixed multiplex slide or single stain slides, and computing the tissue region mask from the individual marker image. The heat map of each marker is determined by applying the low pass filter on the individual marker image channel and selecting the top K highest intensity regions from the heat map as the candidate FOVs for each marker. The candidate FOVs from the individual marker images are merged together in the same coordinate system by either adding all of the FOVs together or by only adding the FOVs from the selected marker images depending on the users choice, and registering all the individual marker images to a common coordinate system and transferring the FOVs back to the original images.

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