A laboratory automation system for processing sample containers containing laboratory samples and/or for processing the samples is presented. The laboratory automation system comprises a digital camera configured to take an image of the sample container together with a calibration element. The image comprises image data related to the sample container and image data related to the calibration element. The laboratory automation system also comprises an image processing device configured to determine geometrical properties of the sample container depending on the image data related to the sample container and the image data related to the calibration element.

A method of establishing a sample tube set which is adapted to be processed by a laboratory automation system. The method includes selecting a sample tube set comprising several sample tube types by selecting a plurality of different sample tube types from an assortment of available sample tube types; obtaining a parameter of distribution for at least one detection parameter of each sample tube type comprised in said sample tube set, wherein the parameter of distribution comprises information regarding a distribution of previously detected reference data values of the at least one detection parameter; determining whether the laboratory automation system is capable of correctly identifying each sample tube type comprised in said sample tube set by comparing the parameter of distribution for the at least one detection parameter of each sample tube type comprised in said sample tube set with the parameter of distribution for the at least one detection parameter of all the other sample tube types comprised in said sample tube set; and indicating that the selected sample tube set is approved for being processed by the laboratory automation system if it is determined that the laboratory automation system is capable of correctly identifying each sample tube type comprised in said sample tube set, or proposing at least one conflict remediation if it is determined that the laboratory automation system is not capable of correctly identifying each sample tube type comprised in said sample tube set.

Systems and methods are provided for sample processing. A device may be provided, capable of receiving the sample, and performing one or more of a sample preparation, sample assay, and detection step. The device may be capable of performing multiple assays. The device may comprise one or more modules that may be capable of performing one or more of a sample preparation, sample assay, and detection step. The device may be capable of performing the steps using a small volume of sample.

Method and associated system for reading machine-readable labels on a plurality of sample receptacles held by a sample rack. In the method, a machine-readable label associated each of the plurality of sample receptacles is read with a first label reader when the rack is at a first location. The sample rack is then moved from the first location to a second location, where a rack identifier on the sample rack is sensed with a sensor separate from the first label reader. Finally, the rack identifier is associated with the machine-readable labels of the plurality of sample receptacles.

The present invention provides a device and system for monitoring the accuracy of procedures in the course of the performance of a task, the task comprising at least one procedure to be performed, the device comprising: an input interface for receiving input data relating to the procedures; a data store for storing data relating to the procedures; a processor for: comparing the input data with the stored data; and generating a comparison result indicating the result of that comparison; and an output interface for outputting the comparison result.

An apparatus for characterizing a specimen and/or specimen container. The characterization apparatus includes an imaging location configured to receive a specimen container containing a specimen, a light source configured to provide lighting of the imaging location, and a hyperspectral image capture device. The hyperspectral image capture device is configured to generate and capture a spectrally-resolved image of a small portion of the specimen container and specimen at a spectral image capture device. The spectrally-resolved image data received at the spectral image capture device is processed by a computer to determine at least one of: segmentation of at least one of the specimen and/or specimen container, and determination of a presence or absence of an interferent, such as hemolysis, icterus, or lipemia. Methods of imaging a specimen and/or specimen container, and specimen testing apparatus including a characterization apparatus are described, as are other aspects.

A method for introducing a sample rack holding a plurality of sample receptacles into a receiving bay in a housing of an automated analytical system is disclosed. The method is carried out by manually moving the sample rack from a loading position towards a processing position along a lane in the receiving bay, pausing the movement when the rack reaches a focusing position, moving a reader to focuse onto the lane, resuming the movement of the sample rack and detecting features of the sample rack during the movement, positioning, detecting, and locking the sample rack in the processing position, and processing the content of the sample receptacles.

An automated sample specimen storage system including a tube holding microplate including a plate frame, a predetermined array of tube holding receptacles formed in the plate frame, the receptacles having a SBS standard pitch corresponding to the predetermined array, and being configured for holding therein sample store and transport tubes, each disposed so as to contain sample specimen in a sample storage of the storage system and to effect, with the sample tube, delivery from the sample storage to a workstation, the predetermined array of receptacles defining a volume capacity of the tube holding microplate, and each of the receptacles being shaped to conformally engage walls of the sample tubes and hold a respective one of the sample store and transport tubes, wherein the receptacles are arranged so that the tube holding microplate volume capacity defined by the predetermined array is an under optimum volume capacity.

An automatic sample injection system (1) includes at least an injector (2). The injector (2) includes a turret (10) comprising a plurality of vial receiving holes (30) that are corresponding to a plurality of types of vials having different sizes, the plurality of vial receiving holes (30) being provided on the same circumference on an upper surface of the turret, the turret being configured to rotate so that the plurality of the vial receiving holes (30) are each moved along a circumferential track, and a controller (22) configured, in a case where a sampler (4) for supplying a vial to the injector (2) is provided, to recognize a size of a target vial to be supplied at the time when the target vial is supplied from the sampler (4) and to arrange the vial receiving hole (30) corresponding to the target vial at a delivery position (P) set on the circumferential track.

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.