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公开(公告)号：US11538159B2
公开(公告)日：2022-12-27
申请号：US16604128
申请日：2018-04-10
Applicant: Siemens Healthcare Diagnostics Inc.
Inventor： Stefan Kluckner , Patrick Wissmann , Yao-Jen Chang , Terrence Chen , Benjamin S. Pollack
Abstract: A method of characterizing a serum and plasma portion of a specimen in regions occluded by one or more labels. The characterization method may be used to provide input to an HILN (H, I, and/or L, or N) detection method. The characterization method includes capturing one or more images of a labeled specimen container including a serum or plasma portion from multiple viewpoints, processing the one or more images to provide segmentation data including identification of a label-containing region, determining a closest label match of the label-containing region to a reference label configuration selected from a reference label configuration database, and generating a combined representation based on the segmentation information and the closest label match. Using the combined representation allows for compensation of the light blocking effects of the label-containing region. Quality check modules and testing apparatus and adapted to carry out the method are described, as are other aspects.
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22.发明授权公开(公告)号：US11042788B2
公开(公告)日：2021-06-22
申请号：US16072406
申请日：2017-01-24
Applicant: Siemens Healthcare Diagnostics Inc.
Inventor： Stefan Kluckner , Yao-Jen Chang , Terrence Chen , Benjamin S. Pollack
IPC: G06K9/62 , G01N35/00 , G06T5/00 , G06T5/50 , G06T7/11 , G01N35/10 , G06T7/60 , G01N35/04 , H04N5/247 , G01N35/02
Abstract: A model-based method of determining characteristics of a specimen container. The method includes providing a specimen container, capturing images of the specimen container at different exposures times and at different spectra having different nominal wavelengths, selecting optimally-exposed pixels from the images at different exposure times at each spectra to generate optimally-exposed image data for each spectra, and classifying the optimally-exposed pixels as at least being one of tube, label or cap, and identifying a width, height, or width and height of the specimen container based upon the optimally-exposed image data for each spectra. Quality check modules and specimen testing apparatus adapted to carry out the method are described, as are other aspects.
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23.发明申请公开(公告)号：US20210164965A1
公开(公告)日：2021-06-03
申请号：US17251756
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 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.
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公开(公告)号：US20200265263A1
公开(公告)日：2020-08-20
申请号：US16634541
申请日：2018-07-25
Applicant: Siemens Healthcare Diagnostics Inc.
Inventor： Stefan Kluckner , Yao-Jen Chang , Kai Ma , Vivek Singh , Terrence Chen , Benjamin S. Pollack
Abstract: 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.
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公开(公告)号：US10746753B2
公开(公告)日：2020-08-18
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: G06K9/00 , G01N35/00 , G01B11/245 , G06T7/62 , G06T5/00 , G06T7/11 , G01N35/10 , G01N15/04 , G06T7/143 , G06T5/50 , G01F23/00 , G06T7/00 , G01N35/04 , G01N15/05
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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Patent Agency Ranking
公开(公告)号：US20200151878A1
公开(公告)日：2020-05-14
申请号：US16604128
申请日：2018-04-10
Applicant: Siemens Healthcare Diagnostics Inc.
Inventor： Stefan Kluckner , Patrick Wissmann , Yao-Jen Chang , Terrence Chen , Benjamin S. Pollack
Abstract: A method of characterizing a serum and plasma portion of a specimen in regions occluded by one or more labels. The characterization method may be used to provide input to an HILN (H, I, and/or L, or N) detection method. The characterization method includes capturing one or more images of a labeled specimen container including a serum or plasma portion from multiple viewpoints, processing the one or more images to provide segmentation data including identification of a label-containing region, determining a closest label match of the label-containing region to a reference label configuration selected from a reference label configuration database, and generating a combined representation based on the segmentation information and the closest label match. Using the combined representation allows for compensation of the light blocking effects of the label-containing region. Quality check modules and testing apparatus and adapted to carry out the method are described, as are other aspects.
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27.发明授权公开(公告)号：US10325182B2
公开(公告)日：2019-06-18
申请号：US15551566
申请日：2016-02-16
Applicant: Siemens Healthcare Diagnostics Inc.
Inventor： Khurram Soomro , Yao-Jen Chang , Stefan Kluckner , Wen Wu , Benjamin Pollack , Terrence Chen
IPC: G06K9/32 , G06K9/46 , G06K9/62 , B01L3/00 , B01L9/06 , G01N35/00 , G06T7/11 , G06K7/14 , G06K9/20 , G06K9/78 , G06T7/00
Abstract: Embodiments are directed to classifying barcode tag conditions on sample tubes from top view images to streamline sample tube handling in advanced clinical laboratory automation systems. The classification of barcode tag conditions leads to the automatic detection of problematic barcode tags, allowing for a user to take necessary steps to fix the problematic barcode tags. A vision system is utilized to perform the automatic classification of barcode tag conditions on sample tubes from top view images. The classification of barcode tag conditions on sample tubes from top view images is based on the following factors: (1) a region-of-interest (ROI) extraction and rectification method based on sample tube detection; (2) a barcode tag condition classification method based on holistic features uniformly sampled from the rectified ROI; and (3) a problematic barcode tag area localization method based on pixel-based feature extraction.
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28.发明申请公开(公告)号：US20180365530A1
公开(公告)日：2018-12-20
申请号：US16072406
申请日：2017-01-24
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. The method includes providing a specimen container, capturing images of the specimen container at different exposures times and at different spectra having different nominal wavelengths, selecting optimally-exposed pixels from the images at different exposure times at each spectra to generate optimally-exposed image data for each spectra, and classifying the optimally-exposed pixels as at least being one of tube, label or cap, and identifying a width, height, or width and height of the specimen container based upon the optimally-exposed image data for each spectra. 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：US20180047150A1
公开(公告)日：2018-02-15
申请号：US15551570
申请日：2016-02-16
Applicant: Siemens Healthcare Diagnostics Inc.
Inventor： Yao-Jen Chang , Wen Wu , Guillaume Dumont , Benjamin Pollack , Terrence Chen
CPC classification number: G06T7/0004 , G06K9/00208 , G06K9/3233 , G06K9/4604 , G06K9/4661 , G06K9/6205 , G06T7/0008 , G06T7/11 , G06T7/12 , G06T7/13 , G06T7/60 , G06T2207/30108 , G06T2207/30164
Abstract: Embodiments provide a method of using image-based tube top circle detection that includes extracting, from one of a series of images of a tube tray, a region of interest (ROI) patch having a target tube top circle and boundaries constrained by two dimensional (2D) projections of different types of tube top circle centers. The method also includes calculating an edge gradient magnitude map of the ROI patch and constructing a three dimensional (3D) map of a circle parameter space, each location in the 3D map corresponding to a circle parameter having a center location and a diameter. The method further includes accumulating weighted votes in the 3D map from edge points in the edge gradient magnitude map along edge point gradient directions, determining locations in the 3D map as circle candidates based on the accumulated votes and selecting a target tube top circle based on the greatest accumulated votes.
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公开(公告)号：US20180033140A1
公开(公告)日：2018-02-01
申请号：US15551571
申请日：2016-02-16
Applicant: Siemens Healthcare Diagnostics Inc.
Inventor： Wen Wu , Benjamin Pollack , Yao-Jen Chang , Guillaume Dumont , Terrence Chen
CPC classification number: G06T7/0012 , G01N35/00732 , G01N35/00871 , G01N2035/00881 , G01N2035/0091 , G01N2035/0498 , G06K9/4604 , G06K9/6227 , G06K9/6261 , G06K9/6267 , G06T7/0008 , G06T7/12 , G06T7/74 , G06T2207/30108 , H04N5/2256 , H04N5/247
Abstract: A method for detecting properties of sample tubes is provided that includes extracting image patches substantially centered on a tube slot of a tray or a tube top in a slot. For each image patch, the method may include assigning a first location group defining whether the image patch is an image center, a corner of an image or a middle edge of an image, selecting a trained classifier based on the first location group and determining whether each tube slot contains a tube. The method may also include assigning a second location group defining whether the image patch is from an image center, a left corner of the image, a right corner of the image, a left middle of the image; a center middle of the image or a right middle of the image, selecting a trained classifier based on the second location group and determining a tube property.
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