Siemens Healthcare Diagnostics Gmbh

Benjamin S Pollack

Yao-Jen Chang

Terrence Chen

Stefan Kluckner

Baris Yagci

Colin Mellars

Patrick Wissmann

Vivek Kumar Singh

Ankur Kapoor

Wen Wu

Venkatesh NarasimhaMurthy

Rayal Raj Prasad Nalam Venkat

Ryan German

Kai Ma

Shanhui Sun

Alexander Gelbman

Steven H Pollack

Ilya Malyarov

Mark Edwards

Ludwig Listl

Guillaume Dumont

Beri Cohen

David Stein

Charles V Cammarata

Roy Barr

Thomas J Bao

Boris Mailhe

David D Liu

Jiang Tian

Angela Huenerfauth

Matthew Anthony Rosmarin

JinHyeong Park

Khurram Soomro

Elizabeth Bononno

Ramkrishna Jangale

Jinhyeong Park

David Liu

Matthew Rosmarin

A method of training a model of a diagnostic apparatus includes providing one or more first tube assemblies of a first type and one or more second tube assemblies of a second type in a diagnostic apparatus; capturing one or more first images of at least a portion of each of the one or more first tube assemblies and the second tube assemblies using the imaging device. Training the model includes identifying tube assemblies of the first type and tube assemblies of the second type based on the one or more first images and the one or more second images. Tubes assemblies of the first type are grouped into a first group and tube assemblies of the second type are grouped into a second group. Other methods and apparatus are disclosed.

Apparatus and methods of training models of diagnostic analyzers

A method of synthesizing an image of a tube assembly includes capturing an image of the tube assembly, wherein the capturing generates a captured image. The captured image is decomposed into a plurality of features in latent space using a trained image decomposition model. One or more of the features in the latent space is manipulated into one or more manipulated features. A synthesized tube assembly image is generated with at least one of the manipulated features using a trained image composition model. Other methods and systems are disclosed.

Diagnostic laboratory systems and methods of imaging tube assemblies

A method of identifying a tube type. The method includes capturing one or more pixelated images of a cap affixed to a tube; identifying a color of one or more pixels of the pixilated image of the cap; identifying one or more gradients of a dimension of the cap; and identifying the tube type based at least on: the color of the one or more pixels, and the one or more gradients of a dimension of the cap. Apparatus adapted to carry out the method are disclosed as are other aspects.

Apparatus and methods of identifying tube assembly type

A method of characterizing a specimen and specimen container to be analyzed in an automated diagnostic analysis system. The method can provide a segmentation determination and/or an HILN determination (hemolysis, icterus, lipemia, or normal) of the specimen while protecting patient information. The method includes capturing an image of a specimen container via an image capture device, identifying a label affixed to the specimen container in the captured image via an anonymization network, and editing the captured image via the anonymization network to mask some or all information present in the label so that it is removed from the captured image. Quality check modules and systems configured to carry out the method are also described, as are other aspects.

Methods and apparatus for protecting patient information during characterization of a specimen in an automated diagnostic analysis system

A method of characterizing a specimen to be analyzed in an automated diagnostic analysis system provides an HILN classification (hemolysis, icterus, lipemia, normal) of the specimen along with a basis for that determination. The method includes assigning a hash code to each training image of a sample specimen used in the characterization training process. In response to an HILN determination for a test specimen, the method can retrieve via the hash code one or more of the closest matching training images upon which the HILN classification is based. The one or more of the closest matching training images can be displayed alongside of the one or more images of the test specimen. Quality check modules and systems configured to carry out the method are also described, as are other aspects.

Methods and apparatus for hashing and retrieval of training images used in HILN determinations of specimens in automated diagnostic analysis systems

Embodiments provide a method of using image-based tube top circle detection based on multiple candidate selection to localize the tube top circle region in input images. According to embodiments provided herein, the multi-candidate selection enhances the robustness of tube circle detection by making use of multiple views of the same tube to improve the robustness of tube top circle detection. With multiple candidates extracted from images under different viewpoints of the same tube, the multi-candidate selection algorithm selects an optimal combination among the candidates and provides more precise measurement of tube characteristics. This information is invaluable in an IVD environment in which a sample handler is processing the tubes and moving the tubes to analyzers for testing and analysis.

Image-based tube top circle detection with multiple candidates

A calibration method is provided including identifying the imaging area on each light panel with respect to each imaging device. A center position of the imaging area of each light panel for each imaging device is determined. An optimal optical center of the imaging apparatus using the center position of the imaging area of each imaging device is determined. A tube calibration tool is installed in a carrier on a track, and the carrier is moved on the track so that a center of the tube calibration tool is located at a closest location to the optimal optical center of the imaging apparatus. The center of the tube calibration tool is used to determine a center of a region of interest (ROI) for backlight calibration. Methods for health checking the calibration and apparatus used to carry out the calibration are provided as well as other aspects.

Methods and apparatus providing calibration of background illumination for sample and/or sample container characterization

An analyzer system for in vitro diagnostics includes a sample handler module having a robot arm that delivers samples from drawers into carriers on a linear synchronous motor automation track. Samples are delivered via the automation track to individual track sections associated with individual analyzer modules. Analyzer modules aspirate sample portions directly from the sample carriers and perform analysis thereon.

Automated clinical analyzer system and method

A method of characterizing a serum or plasma portion of a specimen in a specimen container provides a fine-grained HILN index (hemolysis, icterus, lipemia, normal) of the serum or plasma portion of the specimen, wherein the H, I, and L classes may each have five to seven sub-classes. The HILN index may also have one un-centrifuged class. Pixel data of an input image of the specimen container may be processed by a deep semantic segmentation network having, in some embodiments, more than 100 layers. A small front-end container segmentation network may be used to determine a container type and boundary, which may additionally be input to the deep semantic segmentation network. A discriminative network may be used to train the deep semantic segmentation network to generate a homogeneously structured output. Quality check modules and testing apparatus configured to carry out the method are also described, as are other aspects.

Methods and apparatus for fine-grained HIL index determination with advanced semantic segmentation and adversarial training

A method of characterizing a serum or plasma portion of a specimen in a specimen container provides an HILN (hemolysis, icterus, lipemia, normal) determination. Pixel data of an input image of the specimen container is processed by a classification network to identify whether the specimen contains plasma or serum. Pixel data representing a plasma sample are forwarded to a segmentation/classification/regression network trained with plasma samples for HILN determination. Pixel data representing a serum sample are forwarded to a transformation network, wherein the serum sample pixel data is transformed into pixel data that matches pixel data of a corresponding previously-collected plasma sample by changing sample color, contrast, intensity, and/or brightness. The transformed serum sample pixel data are forwarded to the segmentation/classification/regression network for HILN determination. Quality check modules and testing apparatus configured to carry out the method are also described, as are other aspects.

Methods and apparatus for HILN determination with a deep adaptation network for both serum and plasma samples

A quality check module for characterizing a specimen and/or a specimen container. The quality check module includes an imaging location within the quality check module configured to receive a specimen container containing a specimen, one or more cameras located at one or more viewpoints adjacent to the imaging location, and one or more spectrally-switchable light source including a light panel assembly located adjacent the imaging location and configured to provide lighting for the one or more cameras, the spectrally-switchable light source configured to be operatively switchable between multiple different spectra. Methods of imaging a specimen and/or specimen container and specimen, and specimen testing apparatus including a quality check module adapted to carry out the method are described herein, as are other aspects.

Methods and apparatus for characterizing a specimen container and specimen

A quality check module for characterizing a specimen and/or a specimen container including stray light compensation. The quality check module includes an imaging location within the quality check module configured to receive a specimen container containing a specimen, one or more image capture devices configured to capture images of the imaging location from one or more viewpoints, and one or more light sources configured to provide back lighting for the one or more image capture devices, and one or more stray light patches located in an area receiving stray light from the one or more light sources enabling stray light affecting the images to be compensated for and to provide a stray light compensated image. Calibration methods, methods of characterizing a specimen, specimen testing apparatus including a quality check module, and specimen container carriers including one or more stray light patches are provided, as are other aspects.

Stray light compensating methods and apparatus for characterizing a specimen

A method of characterizing a serum or plasma portion of a specimen in a specimen container includes capturing a plurality of images of the specimen container from multiple viewpoints, stacking the multiple viewpoint images along a channel dimension into a single stacked input, and processing the stacked input with a single deep convolutional neural network (SDNN). The SDNN includes a segmentation convolutional neural network that receives the stacked input and outputs multiple label maps simultaneously. The SDNN also includes a classification convolutional neural network that processes the multiple label maps and outputs an HILN determination (Hemolysis, Icterus, and/or Lipemia, or Normal) of the serum or plasma portion of the specimen. Quality check modules and testing apparatus configured to carry out the method are also described, as are other aspects.

Specimen container characterization using a single deep neural network in an end-to-end training fashion

A neural network-based method for quantifying a volume of a specimen. The method includes providing a specimen, capturing images of the specimen, and directly classifying to one of a plurality of volume classes or volumes using a trained neural network. Quality check modules and specimen testing apparatus adapted to carry out the volume quantification method are described, as are other aspects.

Deep learning volume quantifying methods and apparatus

A model-based method for quantifying a specimen. The method includes providing a specimen, capturing images of the specimen while illuminated by multiple spectra at different nominal wavelengths, and exposures, and classifying the specimen into various class types comprising one or more of serum or plasma portion, settled blood portion, gel separator (if used), air, tube, label, or cap; and quantifying of the specimen. Quantifying includes determining one or more of: a location of a liquid-air interface, a location of a serum-blood interface, a location of a serum-gel interface, a location of a blood-gel interface, a volume and/or a depth of the serum or plasma portion, or a volume and/or a depth of the settled blood portion. Quality check modules and specimen testing apparatus adapted to carry out the method are described, as are other aspects.

Growing community of inventors

Jersey City, NJ, United States of America

Benjamin S Pollack