Instrument parameter determination based on sample tube identification

1 . A method for determining characteristics of a sample container in an automated testing system, the method comprising the steps of:

identifying the presence of a rack within the automated testing system;

capturing at least one image of the rack using a sensor of the automated testing system;

processing the at least one image to determine that the rack comprises at least one container;

processing the at least one image to determine one or more characteristics of the container selected from the group consisting of width, height, shape, and presence of a cap;

processing the at least one image to determine presence or absence of a false bottom of the container;

determining a type or class of the container based at least on the one or more characteristics of the container; and

capturing at least a second image of the rack, measuring height, width, or both of the container at different heights in each image, and calculating mean values with standard deviation for determining the container's dimensions.

2 . The method according to claim 1 , wherein determining the type or class of the container based on the one or more characteristics of the container comprises identifying the type or class of the container using container data stored in a database, and wherein the database further comprises a set of instrument parameters assigned to a container type or container class.

3 . The method according to claim 1 , wherein presence of the container is determined by determining intersection points of a top of the rack and a background illumination by identifying a 2D pixel intensity matrix in different sections in the at least one image where the background illumination is present, followed by a first-order differentializing for identifying a slope in the intensity profile.

4 . The method according to claim 3 , wherein the 2D pixel intensity matrix is convolved to reduce noise in the image.

5 . The method of claim 4 , wherein the 2D pixel intensity matrix is converted to a 1D matrix by taking an average along each row and an intensity plot and variance of the 1D matrix is used for determining the presence of the container.

6 . The method of claim 1 , wherein the image is classified into one of two classes by a convolutional neural network (CNN).

7 . The method of claim 1 , wherein determining the type or class of the container comprises determining presence or absence of a false bottom of the container by comparing the position of the container's inner lower end with the position of a bottom end of the rack or the bottom end of a rack insert.

8 . The method of claim 1 , further comprising illuminating the container using a light source positioned to illuminate a first side of the rack opposite a second side of the rack, wherein the sensor is arranged to capture an image of the second side of the rack.

9 . The method according to claim 8 , wherein a width for illumination of the container is in a range between 15 to 35 mm.

10 . The method according to claim 8 , wherein the light source comprises LEDs arranged in two opposite arranged LED stripes.

11 . The method of claim 1 , further comprising:

determining a region of interest (ROI) in the at least one image by identifying reference points;

determining an upper end of the rack in the ROI; and

determining edges of an upper end of the container within the ROI.

12 . The method of claim 1 , wherein determining presence or absence of the false bottom of the container is based on comparing a position of the container's inner lower end with the position of a bottom end of the rack or a bottom end of an insert of the rack.

13 . The method of claim 1 , wherein determining presence or absence of the false bottom of the container further comprises capturing a plurality of images of the container while rotating the container, and processing the plurality of images to determine presence or absence of the false bottom.

14 . A method for determining characteristics of a sample container in an automated testing system, the method comprising the steps of:

identifying the presence of a rack within the automated testing system;

capturing at least one image of the rack using a sensor of the automated testing system;

determining that the rack comprises at least one container;

determining, based on the at least one image, one or more characteristics of the container selected from the group consisting of width, height, shape, and presence of a cap;

determining, based on the at least one image, presence or absence of a false bottom of the container;

determining a type or class of the container based at least on the one or more characteristics of the container;

determining boundaries of separated layers of a material located in the container;

determining hematocrit based on one or more of layers, liquid levels, or liquid volumes percentage of a first layer and a second layer in the one container; and

capturing multiple images of the container during rotation of the container in front of the sensor and forming a segmented picture from the multiple images.

15 . The method of claim 14 , wherein the first layer comprises red blood cells and the second layer comprises plasma.

16 . The method of claim 14 , further comprising the step of applying the segmented picture to a convolutional neural network (CNN) for determining an upper boundary and a lower boundary of a plasma layer in the segmented picture for generating a bounding box enclosing the plasma layer in all segments of the segmented picture.

17 . The method of claim 16 , comprising the step of rearranging the segments of the segmented picture prior to determining again the upper boundary and the lower boundary of the plasma layer in the newly arranged segmented picture for generating a bounding box enclosing the plasma layer in all segments of the segmented picture.

18 . A system for determining characteristics of a container in a rack for an automated testing system, the system comprising:

a sensor configured to capture one or more images of a first side of the rack;

a processor configured to receive the one or more images; to process the one or more images to determine one or more characteristics of the container selected from the group consisting of width, height, shape, and presence of a cap; and

to process the one or more images to determine presence or absence of a false bottom of the container;

a light source configured to provide back illumination by illuminating a second side of the rack opposite the first side of the rack; and

comprising a second light source configured to provide front illumination by illuminating the first side of the rack, and wherein the first light source and the second light source each comprise an LED stripe.

19 . The system of claim 18 , further comprising a database comprising characteristics of a plurality of containers, and wherein the processor is further configured to determine a type or class of the container based on the one or more characteristics of the container and information of the characteristics of the plurality of containers from the database.

20 . The system of claim 18 , wherein the sensor is a camera selected from the group consisting of a monochrome CMOS sensor and a color sensor.