Patent Publication Number: US-2021164965-A1

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

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to U.S. Provisional Application Ser. No. 62/685,344 filed on Jun. 15, 2018, the contents of which is incorporated herein by reference in its entirety. 
    
    
     FIELD 
     This disclosure relates to methods and apparatus for characterizing a specimen container (and specimen therein) in an automated diagnostic analysis system. 
     BACKGROUND 
     Automated diagnostic analysis systems may conduct assays or clinical analyses using one or more reagents to identify an analyte or other constituent in a specimen such as urine, blood serum, blood plasma, interstitial liquid, cerebrospinal liquid, and the like. Such specimens are usually contained within specimen containers (e.g., specimen collection tubes). The testing reactions generate various changes that may be read and/or manipulated to determine a concentration of an analyte or other constituent in the specimen. 
     Improvements in automated testing technology have been accompanied by corresponding advances in pre-analytical specimen preparation and handling operations such as sorting, batch preparation, centrifuging of specimen containers to separate specimen components, cap removal to facilitate fluid access, pre-screening for HILN (Hemolysis, Icterus, and/or Lipemia, or Normal), and the like by automated specimen preparation systems called Laboratory Automation Systems (LASs). LASs may also automatically transport a specimen in a specimen container to a number of specimen processing stations so various operations (e.g., pre-analytical or analytical testing) can be performed thereon. 
     LASs may handle a number of different specimens contained in standard, barcode-labeled specimen containers, which may be of different sizes (e.g., diameters and heights). The barcode label may contain an accession number that may contain or be correlated to patient information and other information that may have been entered into a hospital&#39;s Laboratory Information System (LIS) along with test orders. An operator may place the labeled specimen containers onto the LAS system, which may automatically route the specimen containers for pre-analytical operations such as centrifugation, de-capping, and/or aliquot preparation before the specimen is subjected to clinical analysis or assaying by one or more analyzers (e.g., clinical chemistry or assaying instruments) that may also be part of the LAS. 
     For certain tests, a biological liquid such as a serum or plasma portion (obtained from whole blood by centrifugation) may be analyzed. Where the specimen is whole blood, a gel separator may be added to the specimen container to aid in the separation of a settled blood portion from the serum or plasma portion. After pre-processing, the specimen container may be transported to an appropriate analyzer that may extract a portion of the biological fluid (e.g., serum or plasma portion) from the specimen container and combine the fluid with one or more reagents and possibly other materials in a reaction vessel (e.g., a cuvette). Analytical measurements may then be performed via photometric or fluorometric absorption readings by using a beam of interrogating radiation or the like. The measurements allow determination of end-point rate or other values, from which an amount of an analyte or other constituent in the biological fluid is determined using well-known techniques. 
     However, the presence of any interferent (e.g., Hemolysis, Icterus, and/or Lipemia) in the specimen, which may result from a patient condition or sample processing, may adversely affect test results of the analyte or constituent measurement obtained from the one or more analyzers. For example, the presence of hemolysis (H) in the specimen, which may be unrelated to a patient&#39;s disease state, may cause a different interpretation of the disease condition of the patient. Moreover, the presence of icterus (I) and/or lipemia (L) in the specimen may also cause a different interpretation of the disease condition of the patient. 
     In some systems, a skilled laboratory technician may visually inspect and rate the integrity of the serum or plasma portion of the specimen as either normal (N) or as having a degree of H, I, and/or L (e.g., by assigning an index). This may involve a review of the color of the serum or plasma portion against known standards. However, such manual visual inspection is very subjective, labor intensive, and fraught with possible human error. 
     Because manual inspection may be problematic, efforts have been made to evaluate specimen integrity without the use of visual inspection by a laboratory technician, but rather by using an automated machine-vision inspection apparatus, wherein such evaluation takes place during pre-analytical testing (hereinafter “pre-screening”). The pre-screening involves automated detection of an interferent, such as H, I, and/or L, in a serum or plasma portion obtained from whole blood by fractionation (e.g., by centrifugation). 
     However, in some instances, one or more of the above-described barcode-labels may be affixed directly on the specimen container. Such labels may partially occlude and obscure certain lateral viewpoints of the specimen, so that there may be some orientations that do not provide a clear opportunity to visually observe the serum or plasma portion. Thus, automation of such pre-screening has included, for example, rotationally orienting the specimen in such a way that allows for automated pre-screening for H, I, and/or L or N (see e.g., U.S. Pat. No. 9,322,761). In other systems, the specimen container and specimen are imaged from multiple viewpoints and processed with model-based systems so that rotation of the specimen container is not needed (see, e.g., WO 2016/133,900). 
     In some instances, only a small portion of the serum or plasma portion may be visible, so that any H, I, and/or L, or N reading taken on the serum or plasma portion may not involve a high level of confidence. Moreover, such systems may be complicated and processing of the image data may be computationally burdensome. 
     Accordingly, there is an unmet need for a robust and efficient method and apparatus for characterizing a serum or plasma portion of a specimen in order to determine a presence of hemolysis (H), icterus (I), and/or lipemia (L), or whether the serum or plasma portion of the specimen is normal (N). More particularly, there is an unmet need for improved methods and apparatus for determining if a specimen includes H, I, and/or L or is normal (N). 
     SUMMARY 
     According to a first aspect, a method of characterizing a specimen container is provided. The method includes capturing multiple images of the specimen container from multiple viewpoints wherein the specimen container includes a serum or plasma portion of a specimen therein; inputting image data from the multiple images to a segmentation convolutional neural network and processing the image data with the segmentation convolutional neural network to simultaneously output multiple label maps; inputting the multiple label maps to a classification convolutional neural network and processing the multiple label maps with the classification convolutional neural network; and outputting from the classification convolutional neural network a classification of the serum or plasma portion as being one or more of hemolytic, icteric, lipemic, or normal. 
     According to another aspect, a quality check module is provided. The quality check module includes a plurality of image capture devices configured to capture multiple images from multiple viewpoints of a specimen container containing a serum or plasma portion of a specimen therein, and a computer coupled to the plurality of image capture devices. The computer is configured and operative to: input image data from the multiple images to a segmentation convolutional neural network and process the image data with the segmentation convolutional neural network to simultaneously output multiple label maps, input the multiple label maps to a classification convolutional neural network and process the multiple label maps with the classification convolutional neural network, and output from the classification convolutional neural network a classification of the serum or plasma portion as being one or more of hemolytic, icteric, lipemic, or normal. 
     In a further aspect, a specimen testing apparatus is provided. The specimen testing apparatus includes a track, a carrier moveable on the track and configured to contain a specimen container containing a serum or plasma portion of a specimen therein, a plurality of image capture devices arranged around the track and configured to capture multiple images from multiple viewpoints of the specimen container and the serum or plasma portion of the specimen, and a computer coupled to the plurality of image capture devices. The computer is configured and operative to: input image data from the multiple images to a segmentation convolutional neural network and process the image data with the segmentation convolutional neural network to simultaneously output multiple label maps, input the multiple label maps to a classification convolutional neural network and process the multiple label maps with the classification convolutional neural network; and output from the classification convolutional neural network a classification of the serum or plasma portion as being one or more of hemolytic, icteric, lipemic, or normal. 
     Still other aspects, features, and advantages of this disclosure may be readily apparent from the following description by illustrating a number of example embodiments and implementations, including the best mode contemplated for carrying out the invention. This disclosure may also be capable of other and different embodiments, and its several details may be modified in various respects, all without departing from the scope of the invention. This disclosure is intended to cover all modifications, equivalents, and alternatives falling within the scope of the appended claims (see further below). 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The drawings, described below, are for illustrative purposes and are not necessarily drawn to scale. Accordingly, the drawings and descriptions are to be regarded as illustrative in nature, and not as restrictive. The drawings are not intended to limit the scope of the invention in any way. 
         FIG. 1  illustrates a top schematic view of a specimen testing apparatus including one or more quality check modules configured to carry out HILN detection methods according to one or more embodiments. 
         FIG. 2  illustrates a side view of a specimen container including a separated specimen with a serum or plasma portion containing an interferent, and wherein the specimen container includes a label thereon. 
         FIG. 3A  illustrates a side view of a specimen container including a label, a separated specimen including a serum or plasma portion containing an interferent, and a gel separator therein. 
         FIG. 3B  illustrates a side view of the specimen container of  FIG. 3A  held in an upright orientation in a holder. 
         FIG. 4A  illustrates a schematic top view of a quality check module (with top removed) including multiple viewpoints and configured to capture and analyze multiple backlit images to enable a determination of a presence of an interferent according to one or more embodiments. 
         FIG. 4B  illustrates a schematic side view of the quality check module (with front enclosure wall removed) of  FIG. 4A  taken along section line  4 B- 4 B of  FIG. 4A  according to one or more embodiments. 
         FIG. 5  illustrates a block diagram of functional components of a quality check module including a single deep convolutional neural network (SDNN) configured to determine a presence of H, I, and/or L or N in a specimen according to one or more embodiments. 
         FIG. 6  illustrates a block diagram of an architecture of the segmentation convolutional neural network (SCNN) of  FIG. 5  according to one or more embodiments. 
         FIG. 7  illustrates a block diagram of an architecture of the classification convolutional neural network (CONN) of  FIG. 5  according to one or more embodiments. 
         FIG. 8  is flowchart of a method of determining H, I, and/or L, or N in a specimen according to one or more embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     During pre-screening of a specimen contained in a specimen container, such as at a quality check module described further below, a method is provided in accordance with embodiments that determines the presence of an interferent in the serum or plasma portion of the specimen. The serum or plasma portion may be the liquid component of blood and may be found above the settled blood portion after fractionation (e.g., by centrifugation). The settled blood portion may be a packed semi-solid made up of blood cells such as white blood cells (leukocytes), red blood cells (erythrocytes), and platelets (thrombocytes). Plasma and serum may differ from each other in the content of coagulating components, primarily fibrinogen. Plasma may be the un-clotted liquid, whereas serum may refer to blood plasma that has been allowed to clot either under the influence of endogenous enzymes or exogenous components. 
     An interferent, such as H, I, and/or L, or a determination of normal (N) (hereinafter “HILN”), as used herein refers to the presence of at least one of hemolysis (H), icterus (I), or lipemia (L) in the serum or plasma portion of the specimen. “N” refers to “normal,” which may be defined as a serum or plasma portion that includes acceptably low amounts of H, I, and L. Hemolysis may be defined as a condition in the serum or plasma portion wherein red blood cells are destroyed during processing, which leads to the release of hemoglobin from the red blood cells into the serum or plasma portion such that the serum or plasma portion takes on a reddish hue. The degree of hemolysis may be quantified by assigning a Hemolytic Index. Icterus may be defined as a condition of the blood where the serum or plasma portion is discolored dark yellow, caused by an accumulation of bile pigment (bilirubin). The degree of icterus may be quantified by assigning an Icteric Index. Lipemia may be defined as a presence in the blood of an abnormally high concentration of emulsified fat, such that the serum or plasma portion has a whitish or milky appearance. The degree of lipemia may be quantified by assigning a Lipemic Index. 
     The method in accordance with embodiments may determine just HILN or N-Class H (e.g., H1, H2, H3, or more), N-Class I (e.g., I1, I2, I3, or more), and/or N-Class L (e.g., L1, L2, L3, or more), or N. In addition, the method may classify (or “segment”) various regions of the specimen container and specimen, such as serum or plasma portion, settled blood portion, gel separator (if used), air, label, type of specimen container (indicating, e.g., height and width/diameter), and/or type and/or color of a specimen container cap. A specimen container holder or background may also be classified. Differentiation of the serum and plasma portion from the region comprising one or more labels on the specimen container is a particularly vexing problem, because the one or more labels may wrap around the specimen container to various degrees. Thus, the one or more labels may obscure one or more views, such that a clear view of the serum or plasma portion may be difficult to obtain. 
     Thus, classification of the serum or plasma portion may be challenging due to interference from the one or more labels, whose placement may vary substantially from one specimen container to the next. In particular, the obstruction caused by the one or more labels may heavily influence the spectral responses, such as from various viewpoints, given that the one or more labels may appear on a back side and thus may affect light transmission received at a front side. 
     Moreover, the quality check module of an automated diagnostic analysis system and associated characterization method performed therein should be computationally efficient. Accordingly, given the challenges described above, in a first broad aspect, embodiments of this disclosure provide methods and apparatus configured to determine the presence of HILN using a single semantic segmentation convolutional neural network (SCNN) whose output is coupled as input to a classification convolutional neural network (CONN), which are collectively referred to herein as a single deep neural network (SDNN). The SDNN may include a large number of operational layers (e.g., 50-100; other numbers of operational layers are possible), described further below. 
     In some embodiments, the input to the SCNN may be multi-spectral, multi-exposure image data, which may be consolidated and normalized, and obtained from a plurality of image capture devices. An image capture device may be any device capable of capturing a pixelated image (e.g., digital image) for analysis, such as a digital camera, a CCD (charge-coupled device), one or more CMOS (complementary metal-oxide semiconductor) sensors, an array of sensors, or the like. The plurality of image capture devices may be arranged and configured to capture images from multiple viewpoints (e.g., three viewpoints; other numbers of viewpoints are possible). The methods described herein may use high dynamic range (HDR) image processing of the specimen container and serum or plasma portion as an input to the SCNN. HDR imaging may involve capturing multiple exposures while using multiple spectral illuminations. In some embodiments, the SCNN is trained to recognize regions occluded by one or more labels on the specimen container so that the SCNN can better account for the presence of labels on the back side of the specimen container from any viewpoint in characterizing HILN. 
     As a result, more effective classification of the serum or plasma region may be available in cases where label obstruction is present, and the confidence in the intensity readings for those regions of the serum or plasma portion that are occluded by a label can be improved. Thus, an improved determination of HILN and/or the extent of HIL can be output from the SDNN. 
     The specimen may be collected in a specimen container, such as a blood collection tube and may include a settled blood portion and a serum and plasma portion after fractionation (e.g., separation by centrifugation). In some specimen containers, a gel separator may be used, which positions itself between the settled blood portion and the serum or plasma portion during centrifugation. The gel separator serves as a physical barrier between the two portions (liquid and semi-solid, settled blood cells), and may minimize remixing thereof. The specimen containers may be of different sizes and thus may be supplied for pre-screening and to the analyzers in a number of different configurations. For example, the specimen containers may have sizes such as 13 mm×75 mm, 13 mm×100 mm, 16 mm×100 mm, and 16 mm×125 mm. Other suitable sizes may be used. 
     In accordance with one aspect, the characterization method may be carried out by a quality check module, and in specimen testing systems, each including the SDNN. The SDNN may include operational layers including, e.g., BatchNorm, ReLU activation, convolution (e.g., 2D), dropout, and deconvolution (e.g., 2D) layers to extract features, such as simple edges, texture, and parts of the serum or plasma portion and label-containing regions. Top layers, such as fully convolutional layers, may be used to provide correlation between parts. The output of the layer may be fed to a SoftMax layer, which produces an output on a per pixel (or per patch—including n×n pixels) basis concerning whether each pixel or patch includes HILN. In some embodiments, only an output of HILN is provided from the CCNN. In other embodiments, the output of the CCNN may be fine-grained HILN, such as H1, H2, H3, I1, I2, I3, L1, L2, L3, or N, so that for each interferent present an estimate of the level (index) of the interferent is also obtained. 
     Should the specimen be found to contain one or more of H, I, and L, a suitable notice may be provided to the operator, and/or the specimen container may be taken off line (1) to perform a remediation to rectify the one or more of the H, I, or L, (2) to more accurately measure an extent of the interferent present, (3) to redraw the specimen, or (4) to perform other processing. Thus, the ability to pre-screen for HILN, such as at the first possible instance after centrifugation, and before analysis by one or more analyzers, may advantageously (a) minimize time wasted analyzing specimens that are not of the proper quality for analysis, (b) may avoid or minimize erroneous test results, (c) may minimize patient test result delay, and/or (d) may avoid wasting of patient specimen. 
     In some embodiments, combinations of segmentation output and HILN output may be provided. The outputs may result from multiple branches of the SDNN. The branches may include separate convolutional layers and deconvolution and SoftMax layers, wherein one branch may be dedicated to segmentation and the other to HILN detection. Multi-branch embodiments including HILN, segmentation, specimen container type detection, and/or cap type detection may also be provided. 
     Further details of inventive characterization methods, quality check modules configured to carry out the characterization methods, and specimen testing apparatus including one or more quality check modules will be further described with reference to  FIGS. 1-8  herein. 
       FIG. 1  illustrates a specimen testing apparatus  100  capable of automatically processing multiple specimen containers  102  containing specimens  212  (see, e.g.,  FIGS. 2-3B ). The specimen containers  102  may be provided in one or more racks  104  at a loading area  105  prior to transportation to, and analysis by, one or more analyzers (e.g., first, second, and third analyzer  106 ,  108 , and/or  110 , respectively) arranged about the specimen testing apparatus  100 . More or less numbers of analyzers can be used. The analyzers may be any combination of clinical chemistry analyzers and/or assaying instruments, or the like. The specimen containers  102  may be any suitably transparent or translucent container, such as a blood collection tube, test tube, sample cup, cuvette, or other clear or opaque glass or plastic container capable of containing and allowing imaging of the specimen  212  contained therein. The specimen containers  102  may be varied in size. 
     Specimens  212  ( FIGS. 2-3B ) may be provided to the specimen testing apparatus  100  in the specimen containers  102 , which may be capped with a cap  214 . The caps  214  may be of different types and/or colors (e.g., red, royal blue, light blue, green, grey, tan, yellow, or color combinations), which may have meaning in terms of what test the specimen container  102  is used for, the type of additive included therein, whether the container includes a gel separator, or the like. Other colors may be used. In one embodiment, the cap type may be determined by the characterization method described herein. 
     Each of the specimen containers  102  may be provided with a label  218 , which may include identification information  218   i  (i.e., indicia) thereon, such as a barcode, alphabetic, numeric, or combination thereof. The identification information  218   i  may be machine readable at various locations about the specimen testing apparatus  100 . The machine readable information may be darker (e.g., black) than the label material (e.g., white paper) so that it can be readily imaged. The identification information  218   i  may indicate, or may otherwise be correlated, via a Laboratory Information System (LIS)  147 , to a patient&#39;s identification as well as tests to be performed on the specimen  212 . The identification information  218   i  may indicate other or additional information. Such identification information  218   i  may be provided on the label  218 , which may be adhered to or otherwise provided on an outside surface of the tube  215 . As shown in  FIG. 2 , the label  218  may not extend all the way around the specimen container  102  or all along a length of the specimen container  102  such that from the particular front viewpoint shown, a large part of the serum or plasma portion  212 SP is viewable (the part shown dotted) and unobstructed by the label  218 . 
     However, in some embodiments, multiple labels  218  may have been provided (such as from multiple facilities that have handled the specimen container  102 ), and they may overlap each other to some extent. For example, two labels (e.g., a manufacturer&#39;s label and a barcode label) may be provided and may be overlapping and may occlude (obstruct) some or all of one or more viewpoints. 
     Thus, it should be understood that in some embodiments, although the label(s)  218  may occlude some portion of the specimen  212  (an occluded portion), some portion of the specimen  212  and serum and plasma portion  212 SP may still be viewable from at least one viewpoint (an un-occluded portion). Thus, in accordance with another aspect of the disclosure, embodiments of the SDNN configured to carry out the characterization method can be trained to recognize the occluded and un-occluded portions, such that improved HILN detection may be provided. 
     Again referring to  FIG. 2 , the specimen  212  may include the serum or plasma portion  212 SP and a settled blood portion  212 SB contained within the tube  215 . Air  216  may be provided above the serum and plasma portion  212 SP and a line of demarcation between them is defined as the liquid-air interface (LA). The line of demarcation between the serum or plasma portion  212 SP and the settled blood portion  212 SB is defined as a serum-blood interface (SB). An interface between the air  216  and cap  214  is defined as a tube-cap interface (TC). The height of the tube (HT) is defined as a height from a bottom-most part of the tube  215  to a bottom of the cap  214 , and may be used for determining tube size. A height of the serum or plasma portion  212 SP is (HSP) and is defined as a height from a top of the serum or plasma portion  212 SP to a top of the settled blood portion  212 SB. A height of the settled blood portion  212 SB is (HSB) and is defined as a height from the bottom of the settled blood portion  212 SB to a top of the settled blood portion  212 SB at SB. HTOT is a total height of the specimen  212  and equals HSP plus HSB. 
     In cases where a gel separator  313  is used ( FIG. 3A ), the height of the serum or plasma portion  212 SP is (HSP) and is defined as a height from the top of the serum or plasma portion  212 SP at LA to the top of the gel separator  313  at SG, wherein SG is an interface between the serum or plasma portion  212 SP and the gel separator  313 . A height of the settled blood portion  212 SB is (HSB) and is defined as a height from the bottom of the settled blood portion  212 SB to the bottom of the gel separator  313  at BG, wherein BG is an interface between the settled blood portion  212 SB and the gel separator  313 . HTOT is the total height of the specimen  212  and equals HSP plus HSB plus height of the gel separator  313 . In each case, Tw is a wall thickness, W is an outer width, which may also be used for determining the size of the specimen container  102 , and Wi is an inner width of the specimen container  102 . 
     In more detail, specimen testing apparatus  100  may include a base  120  ( FIG. 1 ) (e.g., a frame, floor, or other structure) upon which a track  121  may be mounted. The track  121  may be a railed track (e.g., a mono rail or a multiple rail), a collection of conveyor belts, conveyor chains, moveable platforms, or any other suitable type of conveyance mechanism. Track  121  may be circular or any other suitable shape and may be a closed track (e.g., endless track) in some embodiments. Track  121  may, in operation, transport individual ones of the specimen containers  102  to various locations spaced about the track  121  in carriers  122 . 
     Carriers  122  may be passive, non-motored pucks that may be configured to carry a single specimen container  102  on the track  121 , or optionally, an automated carrier including an onboard drive motor, such as a linear motor that is programmed to move about the track  121  and stop at pre-programmed locations. Other configurations of carrier  122  may be used. Carriers  122  may each include a holder  122 H ( FIG. 3B ) configured to hold the specimen container  102  in a defined upright position and orientation. The holder  122 H may include a plurality of fingers or leaf springs that secure the specimen container  102  on the carrier  122 , but some may be moveable or flexible to accommodate different sizes of the specimen containers  102 . In some embodiments, carriers  122  may leave from the loading area  105  after being offloaded from the one or more racks  104 . The loading area  105  may serve a dual function of also allowing reloading of the specimen containers  102  from the carriers  122  to the loading area  105  after pre-screening and/or analysis is completed. 
     A robot  124  may be provided at the loading area  105  and may be configured to grasp the specimen containers  102  from the one or more racks  104  and load the specimen containers  102  onto the carriers  122 , such as onto an input lane of the track  121 . Robot  124  may also be configured to reload specimen containers  102  from the carriers  122  to the one or more racks  104 . The robot  124  may include one or more (e.g., least two) robot arms or components capable of X (lateral) and Z (vertical—out of the paper, as shown), Y and Z, X, Y, and Z, or r (radial) and theta (rotational) motion. Robot  124  may be a gantry robot, an articulated robot, an R-theta robot, or other suitable robot wherein the robot  124  may be equipped with robotic gripper fingers oriented, sized, and configured to pick up and place the specimen containers  102 . 
     Upon being loaded onto track  121 , the specimen containers  102  carried by carriers  122  may progress to a first pre-processing station  125 . For example, the first pre-processing station  125  may be an automated centrifuge configured to carry out fractionation of the specimen  212 . Carriers  122  carrying specimen containers  102  may be diverted to the first pre-processing station  125  by inflow lane or other suitable robot. After being centrifuged, the specimen containers  102  may exit on outflow lane, or otherwise be removed by a robot, and continue along the track  121 . In the depicted embodiment, the specimen container  102  in carrier  122  may next be transported to a quality check module  130  to carry out pre-screening, as will be further described herein with reference to  FIGS. 4A-8  herein. 
     The quality check module  130  is configured to pre-screen and carry out the characterization methods described herein, and is configured to automatically determine a presence of, and possibly an extent of H, I, and/or L contained in a specimen  212  or whether the specimen is normal (N). If found to contain effectively-low amounts of H, I and/or L, so as to be considered normal (N), the specimen  212  may continue on the track  121  and then may be analyzed by the one or more analyzers (e.g., first, second, and/or third analyzers  106 ,  108 , and/or  110 ). Thereafter, the specimen container  102  may be returned to the loading area  105  for reloading to the one or more racks  104 . 
     In some embodiments, in addition to detection of HILN, segmentation of the specimen container  102  and specimen  212  may take place. From the segmentation data, post processing may be used for quantification of the specimen  212  (i.e., determination of HSP, HSB, HTOT, and determination of location of SB or SG, and LA). In some embodiments, characterization of the physical attributes (e.g., size—height and width/diameter) of the specimen container  102  may take place at the quality check module  130 . Such characterization may include determining HT and W, and possibly TC, and/or Wi. From this characterization, the size of the specimen container  102  may be extracted. Moreover, in some embodiments, the quality check module  130  may also determine cap type, which may be used as a safety check and may catch whether a wrong tube type has been used for the test ordered. 
     In some embodiments, a remote station  132  may be provided on the specimen testing apparatus  100  that is not directly linked to the track  121 . For instance, an independent robot  133  (shown dotted) may carry specimen containers  102  containing specimens  212  to the remote station  132  and return them after testing/pre-processing. Optionally, the specimen containers  102  may be manually removed and returned. Remote station  132  may be used to test for certain constituents, such as a hemolysis level, or may be used for further processing, such as to lower a lipemia level through one or more additions and/or through additional processing, or to remove a clot, bubble or foam, for example. Other pre-screening using the HILN detection methods described herein may be accomplished at remote station  132 . 
     Additional station(s) may be provided at one or more locations on or along the track  121 . The additional station(s) may include a de-capping station, aliquoting station, one or more additional quality check modules  130 , and the like. 
     The specimen testing apparatus  100  may include a number of sensors  116  at one or more locations around the track  121 . Sensors  116  may be used to detect a location of specimen containers  102  on the track  121  by means of reading the identification information  218   i , or like information (not shown) provided on each carrier  122 . Any suitable means for tracking the location may be used, such as proximity sensors. All of the sensors  116  may interface with the computer  143 , so that the location of each specimen container  102  may be known at all times. 
     The pre-processing stations and the analyzers  106 ,  108 ,  110  may be equipped with robotic mechanisms and/or inflow lanes configured to remove carriers  122  from the track  121 , and with robotic mechanisms and/or outflow lanes configured to reenter carriers  122  to the track  121 . 
     Specimen testing apparatus  100  may be controlled by the computer  143 , which may be a microprocessor-based central processing unit CPU, having a suitable memory and suitable conditioning electronics and drivers for operating the various system components. Computer  143  may be housed as part of, or separate from, the base  120  of the specimen testing apparatus  100 . The computer  143  may operate to control movement of the carriers  122  to and from the loading area  105 , motion about the track  121 , motion to and from the first pre-processing station  125  as well as operation of the first pre-processing station  125  (e.g., centrifuge), motion to and from the quality check module  130  as well as operation of the quality check module  130 , and motion to and from each analyzer  106 ,  108 ,  110  as well as operation of each analyzer  106 ,  108 ,  110  for carrying out the various types of testing (e.g., assay or clinical chemistry). 
     For all but the quality check module  130 , the computer  143  may control the specimen testing apparatus  100  according to software, firmware, and/or hardware commands or circuits such as those used on the Dimension® clinical chemistry analyzer sold by Siemens Healthcare Diagnostics Inc. of Tarrytown, N.Y. and such control is typical to those skilled in the art of computer-based electromechanical control programming and will not be further described herein. However, other suitable systems for controlling the specimen testing apparatus  100  may be used. The control of the quality check module  130  may also be provided by the computer  143 , but in accordance with the inventive characterization methods described in detail herein. 
     The computer  143  used for image processing and to carry out the characterization methods described herein may include a CPU or GPU, sufficient processing capability and RAM, and suitable storage. In one example, the computer  143  may be a multi-processor-equipped PC with one or more GPUs, 8 GB Ram or more, and a Terabyte or more of storage. In another example, the computer  143  may be a GPU-equipped PC, or optionally a CPU-equipped PC operated in a parallelized mode. MKL could be used as well, 8 GB RAM or more, and suitable storage. 
     Embodiments of the disclosure may be implemented using a computer interface module (CIM)  145  that allows a user to easily and quickly access a variety of control and status display screens. These control and status display screens may display and enable control of some or all aspects of a plurality of interrelated automated devices used for preparation and analysis of specimens  212 . The CIM  145  may be employed to provide information about the operational status of a plurality of interrelated automated devices as well as information describing the location of any specimen  212 , as well as a status of tests to be performed on, or being performed on, the specimen  212 . The CIM  145  is thus adapted to facilitate interactions between an operator and the specimen testing apparatus  100 . The CIM  145  may include a display screen operative to display a menu including icons, scroll bars, boxes, and buttons through which the operator may interface with the specimen testing apparatus  100 . The menu may comprise a number of function elements programmed to display and/or operate functional aspects of the specimen testing apparatus  100 . 
       FIGS. 4A and 4B  show a first embodiment of a quality check module  130  configured to carry out the characterization methods as shown and described herein. Quality check module  130  may be configured to pre-screen for presence of an interferent (e.g., H, I, and/or L) in a specimen  212  (e.g., in a serum or plasma portion  212 SP thereof) prior to analysis by the one or more analyzers  106 ,  108 ,  110 . Pre-screening in this manner allows for additional processing, additional quantification or characterization, and/or discarding and/or redrawing of a specimen  212  without wasting valuable analyzer resources or possibly having the presence of an interferent affect the veracity of the test results. 
     In addition to the interferent detection methods described herein, other detection methods may take place on the specimen  212  contained in the specimen container  102  at the quality check module  130 . For example, a method may be carried out at the quality check module  130  to provide segmentation as an output from the SDNN. The segmentation data may be used in a post processing step to quantify the specimen  212 , i.e., determine certain physical dimensional characteristics of the specimen  212  (e.g., LA and SB, and/or determination of HSP, HSB, and/or HTOT). Quantification may also involve estimating, e.g., a volume of the serum or plasma portion (VSP) and/or a volume of the settled blood portion (VSB). Other quantifiable geometrical features may also be determined. 
     Furthermore, the quality check module  130  may be used to quantify geometry of the specimen container  102 , i.e., quantify certain physical dimensional characteristics of the specimen container  102 , such as the location of TC, HT, and/or W or Wi of the specimen container  102 . 
     Referring to  FIGS. 1, 4A, and 4B , the quality check module  130  may include multiple image capture devices  440 A- 4400 . Three image capture devices  440 A- 440 C are shown and are preferred, but optionally two or four or more can be used. Image capture devices  440 A- 4400  may be any suitable device for capturing well-defined digital images, such as conventional digital cameras capable of capturing a pixelated image, charged coupled devices (CCD), an array of photodetectors, one or more CMOS sensors, or the like. For example, the three image capture devices  440 A,  440 B,  440 C are illustrated in  FIG. 4A  and are configured to capture images from three different lateral viewpoints (viewpoints labeled  1 ,  2 , and  3 ). The captured image size may be, e.g., about 2560×694 pixels. In another embodiment, the image capture devices  440 A,  440 B,  440 C may capture an image size that may be about 1280×387 pixels, for example. Other image sizes and pixel densities may be used. 
     Each of the image capture devices  440 A,  440 B,  440 C may be configured and operable to capture lateral images of at least a portion of the specimen container  102 , and at least a portion of the specimen  212 . For example, the image capture devices  440 A- 4400  may capture a part of the label  218  and part or all of the serum or plasma portion  212 SP. In some instances, e.g., part of a viewpoint 1-3 may be partially occluded by label  218 . In some embodiments, one or more of the viewpoints 1-3 may be fully occluded, i.e., no clear view of the serum or plasma portion  212 SP may be possible. However, even in cases where a side (front side or back side) of a viewpoint 1-3 is fully occluded by one or more labels  218 , the characterization method may still be able to distinguish the boundaries of the serum or plasma portion  212 SP through the one or more occluding labels  218 . 
     In the embodiment shown, the plurality of image capture devices  440 A,  440 B,  440 C are configured to capture lateral images of the specimen container  102  and specimen  212  at an imaging location  432  from the multiple viewpoints 1-3. The viewpoints 1-3 may be arranged so that they are approximately equally spaced from one another, such as about 120° from one another, as shown. As depicted, the image capture devices  440 A,  440 B,  440 C may be arranged around the track  121 . Other arrangements of the plurality of image capture devices  440 A,  440 B,  440 C may be used. In this way, the images of the specimen  212  in the specimen container  102  may be taken while the specimen container  102  is residing in the carrier  122  at the imaging location  432 . The field of view of the multiple images obtained by the image capture devices  440 A,  440 B,  440 C may overlap slightly in a circumferential extent. 
     In one or more embodiments, the carriers  122  may be stopped at a pre-determined location in the quality check module  130 , such as at the imaging location  432 , i.e., such as at a point where normal vectors from each of the image capture devices  440 A,  440 B,  440 C intersect each other. A gate or the linear motor of the carrier  122  may be provided to stop the carriers  122  at the imaging location  432 , so that multiple quality images may be captured thereat. In an embodiment where there is a gate at the quality check module  130 , one or more sensors (like sensors  116 ) may be used to determine the presence of a carrier  122  at the quality check module  130 . 
     The image capture devices  440 A,  440 B,  440 C may be provided in close proximity to and trained or focused to capture an image window at the imaging location  432 , wherein the image window is an area including an expected location of the specimen container  102 . Thus, the specimen container  102  may be stopped so that it is approximately located in a center of the view window in some embodiments. Within the images captured, one or more reference datum may be present. 
     In operation of the quality check module  130 , each image may be triggered and captured responsive to a triggering signal provided in communication lines  443 A,  443 B,  443 C that may be sent by the computer  143 . Each of the captured images may be processed by the computer  143  according to one or more embodiments. In one particularly effective method, high dynamic range (HDR) processing may be used to capture and process the image data from the captured images. In more detail, multiple images are captured of the specimen  212  at the quality check module  130  at multiple different exposures (e.g., at different exposure times), while being sequentially illuminated at one or more different spectra. For example, each image capture device  440 A,  440 B,  440 C may take 4-8 images of the specimen container  102  including the serum or plasma portion  212 SP at different exposure times at each of multiple spectra. For example, 4-8 images may be taken by image capture device  440 A at viewpoint 1 while the specimen  212  is backlit illuminated with light source  444 A that has a red spectrum. Additional like images may be taken sequentially at viewpoints 2 and 3. 
     In some embodiments, the multiple spectral images may be accomplished using different light sources  444 A- 444 C emitting different spectral illumination. The light sources  444 A- 444 C may back light the specimen container  102  (as shown). A light diffuser may be used in conjunction with the light sources  444 A- 444 C in some embodiments. The multiple different spectra light sources  444 A- 444 C may be RGB light sources, such as LEDs emitting nominal wavelengths of 634 nm+/−35 nm (Red), 537 nm+/−35 nm (Green), and 455 nm+/−35 nm (Blue). In other embodiments, the light sources  444 A- 444 C may be white light sources. In cases where the label  218  obscures multiple viewpoints, IR backlighting or NIR backlighting may be used. Furthermore, RGB light sources may be used in some instances even when label occlusion is present. In other embodiments, the light sources  444 A- 444 C may emit one or more spectra having a nominal wavelength between about 700 nm and about 1200 nm. 
     By way of a non-limiting example, to capture images at a first wavelength, three red light sources  444 A- 444 C (wavelength of about 634 nm+/−35 nm) may be used to sequentially illuminate the specimen  212  from three lateral locations. The red illumination by the light sources  444 A- 444 C may occur as the multiple images (e.g., 4-8 images or more) at different exposure times are captured by each image capture device  440 A- 4400  from each viewpoint 1-3. In some embodiments, the exposure times may be between about 0.1 ms and 256 ms. Other exposure times may be used. In some embodiments, each of the respective images for each image capture device  440 A- 4400  may be taken sequentially, for example. Thus, for each viewpoint 1-3, a group of images are sequentially obtained that have red spectral backlit illumination and multiple (e.g., 4-8 exposures, such as different exposure times). The images may be taken in a round robin fashion, for example, where all images from viewpoint 1 are taken followed sequentially by viewpoints 2 and 3. 
     In each embodiment, the quality check module  130  may include a housing  446  that may at least partially surround or cover the track  121  to minimize outside lighting influences. The specimen container  102  may be located inside the housing  446  during the image-taking sequences. Housing  446  may include one or more doors  446 D to allow the carriers  122  to enter into and/or exit from the housing  446 . In some embodiments, the ceiling may include an opening  4460  to allow a specimen container  102  to be loaded into the carrier  122  by a robot including moveable robot fingers from above. 
     Once the red illuminated images are captured in the embodiment of  FIGS. 4A-4B , another spectra of light, for example, green spectral light sources  444 A- 444 C may be turned on (nominal wavelength of about 537 nm with a bandwidth of about +/−35 nm), and multiple images (e.g., 4-8 or more images) at different exposure times may be sequentially captured by each image capture device  440 A,  440 B,  440 C. This may be repeated with blue spectral light sources  444 A- 4440  (nominal wavelength of about 455 nm with a bandwidth of about +/−35 nm) for each image capture devices  440 A,  440 B,  440 C. The different nominal wavelength spectral light sources  444 A- 444 C may be accomplished by light panels including banks of different desired spectral light sources (e.g., R, G, B, W, IR, and/or NIR) that can be selectively turned on and off, for example. Other means for backlighting may be used. 
     The multiple images taken at multiple exposures (e.g., exposure times) for each respective wavelength spectra may be obtained in rapid succession, such that the entire collection of backlit images for the specimen container  102  and specimen  212  from multiple viewpoints 1-3 may be obtained in less than a few seconds, for example. In one example, four different exposure images for each wavelength at three viewpoints 1-3 using the image capture devices  440 A,  440 B,  440 C and back lighting with RGB light sources  444 A- 444 C will result in 4 images×3 spectra×3 image capture devices=36 images. In another example, 4 different exposure images for each wavelength at three viewpoints using the image capture devices  440 A,  440 B,  440 C and back lighting with R, G, B, W, IR, and NIR light sources  444 A- 444 C will result in 4 images×6 spectra×3 cameras=72 images. 
     According to embodiments of the characterization methods, the processing of the image data may involve a preprocessing step including, for example, selection of optimally-exposed pixels from the multiple captured images at the different exposure times at each wavelength spectrum and for each image capture device  440 A- 440 C, so as to generate optimally-exposed image data for each spectrum and for each viewpoint 1-3. This is referred to as “image consolidation” herein. 
     For each corresponding pixel (or patch), for each of the images from each image capture device  440 A- 440 C, pixels (or patches) exhibiting optimal image intensity may be selected from each of the different exposure images for each viewpoint 1-3. In one embodiment, optimal image intensity may be pixels (or patches) that fall within a predetermined range of intensities (e.g., between 180-254 on a scale of 0-255), for example. In another embodiment, optimal image intensity may be between 16-254 on a scale of 0-255), for example. If more than one pixel (or patch) in the corresponding pixel (or patch) locations of two exposure images is determined to be optimally exposed, the higher of the two is selected. 
     The selected pixels (or patches) exhibiting optimal image intensity may be normalized by their respective exposure times. The result is a plurality of normalized and consolidated spectral image data sets for the illumination spectra (e.g., R, G, B, white light, IR, and/or IR—depending on the combination used) and for each image capture device  440 A- 440 C where all of the pixels (or patches) are optimally exposed (e.g., one image data set per spectrum) and normalized. In other words, for each viewpoint 1-3, the data pre-processing carried out by the computer  143  results in a plurality of optimally-exposed and normalized image data sets, one for each illumination spectra employed. 
       FIG. 5  shows apparatus  500  that includes functional components configured carry out the HILN characterization method described herein. Apparatus  500  may be embodied as a quality check module  130  controlled by the computer  143 . As discussed above, the specimen container  102  may be provided at the imaging location  432  ( FIGS. 4A and 4B ) of the quality check module  130  in functional block  502 . The multi-view images are captured in functional block  504  by the plurality of image capture devices  440 A- 440 C. The image data for each of the multi-view, multi-spectral, multi-exposure images may be pre-processed in functional block  506  as discussed above to provide a plurality of optimally-exposed and normalized image data sets (hereinafter “image data sets”). Moreover, in some embodiments, respective image data from each of the plurality of image capture devices  440 A- 4400  may be stacked, or correlated, as a single input with additional channels corresponding to the number of image capture devices (e.g., three times more channels corresponding to the three image capture devices  440 A- 4400 ). For example, images from the three image capture devices  440 A- 440 C may be stacked along the channel dimension, wherein each image capture device may generate a hyper-spectrum image having a dimension of 1272×360×6. The resulting stacked image input may then have a dimension of 1272×360×18, wherein the first 6 channels belong to the image capture device  440 A, the second 6 channels belong to the image capture devices  440 B, and the third 6 channels belong to the image capture device  440 C. This stacked image input may be provided to a single deep convolutional neural network (SDNN)  535 . 
     The SDNN  535  is advantageous over known techniques that separately process images from each image capture device via a respective convolutional neural network. That is, if three image capture devices were used, known techniques included three separate convolutional neural networks and three separate statistical analyses to determine HILN. Such techniques are not memory efficient or computationally efficient. In contrast, by stacking the images from the plurality of image capture devices  440 A- 4400  into a single stacked image input and by processing the stacked image inputs with the SDNN  535  as described herein, higher memory and computational efficiency is achieved. 
     The SDNN  535  may include a segmentation convolutional neural network (SCNN)  536  that receives the stacked image data and simultaneously outputs multiple pixel label maps  537 , wherein the number of pixel label maps  537  corresponds to the number of image capture devices (e.g., three, corresponding to the three image capture devices  440 A- 4400 ). The SDNN  535  may also include a classification convolutional neural network (CONN)  538  that receives the multiple pixel label maps  537  as input and outputs a determination of HILN  540 H,  540 I,  540 L,  540 N. Optionally, the SCNN  536  may output serum segmentation information  542  and/or specimen container/cap type information  544 . 
     Prior to receiving image data from image capture devices  440 A- 4400  for determining HILN (and/or optionally segmentation and/or cap type information), the SDNN  535  may have been previously trained to recognize HILN and optionally serum segmentation and/or specimen container/cap type. In some embodiments, the SCNN  536  may first be trained without the CONN  538 . Multiple sets of training examples may be used to train the SCNN  536 . The SCNN  536  may be trained by imaging with the quality check module  130  a multitude of samples of specimen containers  102  containing specimen  212  by graphically outlining various regions of a multitude of examples of specimens  212  having various specimen HILN conditions, outlining the various regions of occlusion by label  218 , levels of serum or plasma portion  212 SP, and the like. Along with the graphical outlines, class characterization information for each area may be provided. As many as 500 or more, 1000 or more, 2,000 or more, or even 5,000 or more images may be used for training the SCNN  536 . Each training image may have at least the serum or plasma portion  212 SP, its H, I. L, or N identified, various index levels (if output), and the label  218  outlined manually to identify and teach the SCNN  536  the areas that belong to each class that will be a possible output. The SCNN  536  may be tested intermittently with a sample specimen container to see if the SCNN  536  is operating at a sufficiently high level of confidence. If not operating at 100% (e.g., 98% confidence level or more) in determining the correct HILN configuration as an output, then more training samples may be imaged and input along with associated characterization information. In embodiments where segmentation and/or cap type is also provided, the training may involve outlining the segmented classes and/or cap types outputted and including as input class identification information. After training of SCNN  536  alone, CCNN  538  may be added to SCNN  536  and both networks may be additionally trained end-to-end, wherein any segmentation and classification losses can be combined at the end. That is, the loss from the CCNN  538  can be back-propagated to the SCNN  536 . 
     In some embodiments of apparatus  500 , the output of the SDNN  535  may be N-class hemolytic  540 H, N-class icteric  540 I, N-class lipemic  540 L, or normal  540 N, wherein N-class is the number (N) of class options in that interferent class. As before, stacked multi-view, multi-spectral, multi-exposure consolidated and normalized image data sets may be input into the SDNN  535  and the image data sets may be operated upon and processed by the SCNN  536  and CCNN  538 . The output of the processing by the SDNN  535  may be multiple output possibilities (N-classes) for each of HIL, and/or for each viewpoint. For example, N may equal three, wherein the outputs may include H1, H2, and H3 at  540 H; I1, I2, and I3 at  540 I; and L1, L2, L3 at  540 L. 
       FIG. 6  illustrates an architecture  636  of SCNN  536  in accordance with one or more embodiments. SCNN  536  may be coded using any suitable scientific computing framework, program, or toolbox, such as, for example, Caffe available from Berkley Vision and Learning Center (BVLC), Theano, a Python framework for fast computation of mathematical expressions, TensorFlow, Torch, and the like. Architecture  636  may include the following operational layers: two convolutional layers (CONV1 and CONV2)  602  and  630 ; five dense block layers (DB1-DB5)  604 ,  610 ,  616 ,  622 , and  628 ; four concatenation layers (C1-C4)  606 ,  612 ,  620 , and  626 ; two transition down layers (TD1 and TD2)  608  and  614 ; and two transition up layers (TU1 and TU2), arranged as shown in  FIG. 6  wherein multiple pixel label maps  637  are output. Note that the input to each dense block layer  604  and  610  is concatenated (at C1 and C2, respectively) with its output, which may result in a linear growth of the number of pixel label maps. Each dense block layer  604 ,  610 ,  616 ,  622 , and  628  may include multiple layers (e.g., 3 or 4), each including a batch normalization operation, a ReLu layer, and a 3×3 convolutional layer with dropout p=0.2. A first layer receives an input and outputs a number of pixel label maps, which are concatenated to the input. A second layer then receives the concatenated output as its input and outputs a number of pixel label maps, which are again concatenated to the previous pixel label maps. This is repeated for each layer in the dense label block. Each transition down layer  608  and  614  may include a batch normalization operation, followed by a ReLu layer, followed by a 1×1 convolutional layer with dropout p=0.2, followed by a 2×2 max pooling layer. Each transition up layer  618  and  624  may include a 3×3 transposed convolutional layer with stride 2. 
       FIG. 7  illustrates an architecture  738  of CCNN  538  in accordance with one or more embodiments. CCNN  536  may be coded using any suitable scientific computing framework, program, or toolbox, such as, for example, Caffe available from Berkley Vision and Learning Center (BVLC), Theano, a Python framework for fast computation of mathematical expressions, TensorFlow, Torch, and the like. Architecture  738  may include the following operational layers: five sets of convolutional layers (CONV1-CONV5)  702 ,  706 ,  710 ,  714 , and  718  and max pooling layers (POOL1-POOL5)  704 ,  708 ,  712 ,  716 , and  720 , followed by two fully-connected layers (FC1 and FC2)  722  and  724 , followed by a softmax layer  726 . Convolutional layer  702  receives as input multiple pixel label maps  737 , which may be multiple pixel label maps  537  or  637  ( FIGS. 5 and 6 , respectively). Convolutional layer  702  may be one or more 3×3 convolutional layers of depth of 64 (i.e., 64 filters). Convolutional layer  706  may be one or more 3×3 convolutional layers of depth of 128 (i.e., 128 filters). Convolutional layer  710  may be one or more 3×3 convolutional layers of depth of 256 (i.e., 256 filters). Convolutional layer  714  may be one or more 3×3 convolutional layers of depth of 512 (i.e., 512 filters). And convolutional layer  718  may be one or more 3×3 convolutional layers also of depth of 512 (i.e., 512 filters). Fully-connected layers  722  and  724  may each be of size 4096, while softmax layer may be of size 1000. 
     As used herein, a convolution layer is a processing step that may apply a filter (also referred to as a kernel) to input image data (e.g., pixel intensity values) to output an activation map that may indicate detection of some specific type of feature (e.g., from a simple curve after application of a first convolution layer to somewhat more complex features after application of several convolution layers) at some spatial position in the input image data. 
     A max pooling layer is a processing step that may apply a filter to generate output activation maps having maximum pixel values appearing in the one or more activation maps received from a convolutional layer. 
     A ReLU (rectified linear unit) layer is a processing step that may apply a nonlinear function to all values in a received activation map resulting in, e.g., all negative activation values being assigned a value of zero. 
     A fully connected layer is a processing step that aggregates previous activation maps (each of which may indicate detection of lower level features) to indicate detection of higher-level features. 
     A softmax layer is typically a final processing step that outputs a probability distribution highlighting or identifying the most likely feature of one or more images from a class of image features. 
     By using a single deep convolutional neural network such as SDNN  535  for receiving image data from all the image capture devices  440 A- 440 C, instead of using a respective convolutional neural network for each image capture device  440 A- 440 C as in some known apparatus, and by attaching a classification convolutional neural network (e.g., CCNN  538  and architecture  738 ) to the segmentation convolutional neural network (e.g., SCNN  536  and architecture  636 ), the single deep convolutional neural network design has higher memory and computational efficiency as well as increased system performance as compared to other known convolutional neural networks. 
       FIG. 8  illustrates a flowchart of a characterization method  800  according to embodiments of the disclosure. The characterization method  800  may be carried out by quality check module  130  as described herein. In particular, the characterization method  800  may determine a presence of an interferent in a specimen  212  according to one or more embodiments. The characterization method  800  includes, in process block  802 , capturing multiple images of a specimen container (e.g., specimen container  102 ) including a serum or plasma portion (e.g., serum or plasma portion  212 SP) of a specimen (e.g., specimen  212 ) from multiple viewpoints (e.g., viewpoints 1, 2, and 3). Moreover, the specimen container  102  may include one or more labels (e.g., label  218 ) thereon. The one or more images may be digital, pixelated images captured using one or more image capture devices (e.g., image capture devices  440 A- 4400 ). 
     The characterization method  800  further includes, in process block  804 , inputting image data (e.g., stacked, consolidated, and normalized image data sets) from the multiple images to a segmentation convolutional neural network (e.g., SCNN  536 ) and processing the image data with the SCNN to simultaneously output multiple label maps. The processing may be accomplished by the computer  143  described herein after suitable training of the SCNN  536 . 
     In process block  806 , the characterization method  800  includes inputting the multiple label maps to a classification convolutional neural network (e.g., CCNN  538 ) and processing the multiple label maps with the CCNN. The processing may be accomplished by the computer  143  described herein after suitable training of the SCNN  536  and the CCNN  538 . 
     The characterization method  800  further includes, in process block  808 , outputting from the classification convolutional neural network (e.g., CCNN  538 ) a classification of the serum or plasma portion as being one or more of hemolytic, icteric, lipemic, and normal (i.e., H, I, L, H and I, H and L, I and L, H, I, and L, or N). 
     The multiple images from the multiple viewpoints may be captured at different exposure times and/or at a different spectral illumination (e.g., R, G, B, white light, IR, and/or near IR). For example, there may be 4-8 different exposures or more taken at different exposure times for each viewpoint under the different spectral illumination conditions. 
     In an optional aspect, in addition to the HILN determination, a segmentation of the image data sets may be obtained. The method  800  may, in process block  810 , output from the SCNN (e.g. SCNN  536 ) a segmentation of the specimen container  102  and specimen  212 . The image data may be segmented into N′-classes (e.g., 7 classes), such as (1) Tube, (2) Gel Separator, (3) Cap, (4) Air, (5) Label, (6) Settled Blood Portion, and/or (7) Serum or Plasma Portion. Other numbers of classes may be used. 
     The characterization method  800  may also optionally include, in process block  812 , outputting from the SCNN (e.g., SCNN  536 ) a cap type ( 544 ), which may be a specific cap shape or cap color that was pre-trained into the SCNN  536  and the CONN  538 . 
     Accordingly, based on the foregoing it should be apparent that an improved characterization method  800  is provided that better characterizes the serum or plasma portion  212 SP by accounting for labels that may occlude the one or more viewpoints. The improved characterization may be used to provide a rapid and robust characterization of a presence of HILN in the specimen  212 , and in some embodiments, an interferent level (H1, H2, H3, I1, I2, I3, L1, L2, L3) may be assessed and output from the CONN  538 . 
     As should be apparent, the above characterization methods may be carried out using a quality check module (e.g., quality check module  130 ), comprising a plurality of image capture devices (e.g., image capture devices)  440 A- 440 C arranged around an imaging location (e.g., imaging location  432 ), and configured to capture multiple images from multiple viewpoints (e.g., multiple viewpoints 1-3) of a specimen container  102  including one or more labels  218  and containing a serum or plasma portion  212 SP of a specimen  212 , and a computer (e.g., computer  143 ) coupled to the plurality of image capture devices and configured to process image data of the multiple images. The computer (e.g., computer  143 ) may be configured and capable of being operated to process and stack the multiple images from the multiple viewpoints (e.g., viewpoints 1-3) to provide HILN determination or HILN determination in combination with segmentation for each of the multiple viewpoints. 
     Further, the characterization method  800  may be carried out on a specimen testing apparatus  100  that includes the quality check module  130 . The specimen testing apparatus  100  may include a track  121 , and a carrier  122  moveable on the track  121 . The carrier  122  may be configured to contain and support the specimen container  102  including the one or more labels  218  and containing a serum or plasma portion  212 SP of a specimen  212  and to carry the specimen container  102  to the quality check module  130  to accomplish the characterization and the pre-screening for the presence of an interferent. 
     While the disclosure is susceptible to various modifications and alternative forms, specific method and apparatus embodiments have been shown by way of example in the drawings and are described in detail herein. It should be understood, however, that the particular methods and apparatus disclosed herein are not intended to limit the disclosure but, to the contrary, to cover all modifications, equivalents, and alternatives falling within the scope of the claims.