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公开(公告)号：US20210333298A1
公开(公告)日：2021-10-28
申请号：US17278289
申请日：2019-09-19
Applicant: Siemens Healthcare Diagnostics Inc.
Inventor： Venkatesh NarasimhaMurthy , Vivek Singh , Yao-Jen Chang , Benjamin S. Pollack , Ankur Kapoor
Abstract: A method of characterizing a specimen as containing hemolysis, icterus, or lipemia is provided. The method includes capturing one or more images of the specimen, wherein the one or more images include a serum or plasma portion of the specimen. Pixel data is generated by capturing the image. The pixel data of the one or more images of the specimen is processed using a first network executing on a computer to predict a classification of the serum or plasma portion, wherein the classification comprises hemolysis, icterus, and lipemia. The predicted classification is verified using one or more verification networks. Quality check modules and specimen testing apparatus adapted to carry out the method are described, as are other aspects.
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公开(公告)号：US20210133971A1
公开(公告)日：2021-05-06
申请号：US17251744
申请日：2019-06-10
Applicant: Siemens Healthcare Diagnostics Inc.
Inventor： Kai Ma , Yao-Jen Chang , Terrence Chen , Benjamin S. Pollack
Abstract: 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.
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43.发明申请公开(公告)号：US20210064927A1
公开(公告)日：2021-03-04
申请号：US16961222
申请日：2019-01-08
Applicant: Siemens Healthcare Diagnostics Inc.
Inventor： Stefan Kluckner , Yao-Jen Chang , Kai Ma , Vivek Singh , Terrence Chen , Benjamin S. Pollack
Abstract: A method of training a neural network (Convolutional Neural Network-CNN) including reduced graphical annotation input is provided. The training method can be used to train a Testing CNN that can be used for determining Hemolysis (H), Icterus (I), and/or Lipemia (L), or Normal (N) of a serum or plasma portion of a test specimen. The training method includes capturing training images of multiple specimen containers including training specimens, generating region proposals of the serum or plasma portions of the training specimens; and selecting the best matches for the location, size and shape of the region proposals for the multiple training specimens. The obtained features (network and weights) from the training CNN can be used in a testing CNN. Quality check modules and testing apparatus adapted to carry out the training method, and characterization methods using abounding box regressor are described, as are other aspects.
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公开(公告)号：US10824832B2
公开(公告)日：2020-11-03
申请号：US15551565
申请日：2016-02-16
Applicant: Siemens Healthcare Diagnostics Inc.
Inventor： Stefan Kluckner , Yao-Jen Chang , Wen Wu , Benjamin Pollack , Terrence Chen
Abstract: Barcode tag conditions on sample tubes are detected utilizing side view images of sample tubes for streamlining handling in clinical laboratory automation systems. The condition of the tags may be classified into classes, each divided into a list of additional subcategories that cover individual characteristics of the tag quality. According to an embodiment, a tube characterization station (TCS) is utilized to obtain the side view images. The TCS enables the simultaneous or near-simultaneous collection of three images for each tube, resulting in a 360 degree side view for each tube. The method is based on a supervised scene understanding concept, resulting in an explanation of each pixel into its semantic meaning. Two parallel low-level cues for condition recognition, in combination with a tube model extraction cue, may be utilized. The semantic scene information is then integrated into a mid-level representation for final decision making into one of the condition classes.
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45.发明申请公开(公告)号：US20200167591A1
公开(公告)日：2020-05-28
申请号：US16630274
申请日：2018-06-25
Applicant: Siemens Healthcare Diagnostics Inc.
Inventor： Yao-Jen Chang , Stefan Kluckner , Benjamin S. Pollack , Terrence Chen
Abstract: Methods for image-based detection of the tops of sample tubes used in an automated diagnostic analysis system may be based on a convolutional neural network to pre-process images of the sample tube tops to intensify the tube top circle edges while suppressing the edge response from other objects that may appear in the image. Edge maps generated by the methods may be used for various image-based sample tube analyses, categorizations, and/or characterizations of the sample tubes to control a robot in relationship to the sample tubes. Image processing and control apparatus configured to carry out the methods are also described, as are other aspects.
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Patent Agency Ranking
公开(公告)号：US20190271714A1
公开(公告)日：2019-09-05
申请号：US16320198
申请日：2017-07-07
Applicant: Siemens Healthcare Diagnostics Inc.
Inventor： Stefan Kluckner , Yao-Jen Chang , Terrence Chen , Benjamin S. Pollack
Abstract: A model-based method of determining characteristics of a specimen container cap to identify the container cap. The method includes providing a specimen container including a container cap; capturing backlit images of the container cap taken at different exposures lengths and using a plurality of different nominal wavelengths; selecting optimally-exposed pixels from the images at different exposure lengths at each nominal wavelength to generate optimally-exposed image data for each nominal wavelength; classifying the optimally-exposed pixels as at least being one of a tube, a label or a cap; and identifying a shape of the container cap based upon the optimally-exposed pixels classified as being the cap and the image data for each nominal wavelength. Quality check modules and specimen testing apparatus adapted to carry out the method are described, as are numerous other aspects.
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公开(公告)号：US20190033230A1
公开(公告)日：2019-01-31
申请号：US16072386
申请日：2017-01-24
Applicant: Siemens Healthcare Diagnostics Inc.
Inventor： Stefan Kluckner , Yao-Jen Chang , Terrence Chen , Benjamin S. Pollack
CPC classification number: G01N21/90 , G01N21/9027 , G01N21/9036 , G01N35/00 , G01N35/00732 , G01N2021/8861 , G01N2021/8887 , G01N2035/00801 , G01N2035/1018 , G06T7/0012 , G06T7/0014 , G06T7/143 , G06T7/174 , G06T7/77 , G06T2207/10056 , G06T2207/10144 , G06T2207/10152 , G06T2207/20081 , G06T2207/20221 , G06T2207/30004 , G06T2207/30024 , G06T2207/30168
Abstract: A model-based method of inspecting a specimen for presence of one or more artifacts (e.g., a clot, bubble, and/or foam). The method includes capturing multiple images of the specimen at multiple different exposures and at multiple spectra having different nominal wavelengths, selection of optimally-exposed pixels from the captured images to generate optimally-exposed image data for each spectra, computing statistics of the optimally-exposed pixels to generate statistical data, identifying a serum or plasma portion of the specimen, and classifying, based on the statistical data, whether an artifact is present or absent within the serum or plasma portion. Testing apparatus and quality check modules adapted to carry out the method are described, as are other aspects.
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公开(公告)号：US20180364268A1
公开(公告)日：2018-12-20
申请号：US16072412
申请日：2017-01-24
Applicant: Siemens Healthcare Diagnostics Inc.
Inventor： Stefan Kluckner , Yao-Jen Chang , Terrence Chen , Benjamin S. Pollack
IPC: G01N35/00 , G06T7/00 , G01B11/245
CPC classification number: G01N35/00732 , G01B11/245 , G01F23/0076 , G01N15/042 , G01N35/1016 , G01N2015/047 , G01N2015/055 , G01N2035/0401 , G01N2035/0406 , G01N2035/0493 , G01N2035/1018 , G01N2035/1025 , G06T5/009 , G06T5/50 , G06T7/0012 , G06T7/11 , G06T7/143 , G06T7/62 , G06T2207/10024 , G06T2207/10036 , G06T2207/10144 , G06T2207/10152 , G06T2207/20081 , G06T2207/20084 , G06T2207/20208 , G06T2207/20221
Abstract: A model-based method of classifying a specimen in a specimen container. The method includes capturing images of the specimen and container at multiple different exposures times, at multiple different spectra having different nominal wavelengths, and at different viewpoints by using multiple cameras. From the captured images, 2D data sets are generated. The 2D data sets are based upon selection of optimally-exposed pixels from the multiple different exposure images to generate optimally-exposed image data for each spectra. Based upon these 2D data sets, various components are classified using a multi-class classifier, such as serum or plasma portion, settled blood portion, gel separator (if present), tube, air, or label. From the classification data and 2D data sets, a 3D model can be generated. Specimen testing apparatus and quality check modules adapted to carry out the method are described, as are other aspects.
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公开(公告)号：US12253533B2
公开(公告)日：2025-03-18
申请号：US17755461
申请日：2020-10-22
Applicant: Siemens Healthcare Diagnostics Inc.
Inventor： Yao-Jen Chang , Patrick Wissmann , Ludwig Listl , Benjamin S. Pollack , Ramkrishna Jangale , Rayal Raj Prasad Nalam Venkat , Venkatesh NarasimhaMurthy , Ankur Kapoor
IPC: G01N35/00 , G01N35/04 , G06V10/141 , G06V10/56
Abstract: 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.
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公开(公告)号：US20240385204A1
公开(公告)日：2024-11-21
申请号：US18570544
申请日：2022-06-15
Applicant: Siemens Healthcare Diagnostics Inc.
Inventor： Yao-Jen Chang , Rayal Raj Prasad Nalam Venkat , Benjamin S. Pollack , Ankur Kapoor
Abstract: A method of monitoring a specimen container or specimen in a diagnostic laboratory system includes moving the specimen container on a track within the diagnostic laboratory system; moving a sensor module on the track; and monitoring at least one characteristic of the specimen container or a specimen located in the specimen container using the sensor module. Other methods, sensor modules, and diagnostic laboratory systems are disclosed.
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